VOLUME 11, ISSUE 12, DECEMBER 2022

Abstract: Food Recognition is a computer vision application that has gained huge research interest. Food recognition and classification system help to understand foods’ different diversity as different cultures entails different cuisines. There is need to recognize and document Nigerian Foods to avoid going into extinction. It is also necessary to understand the food specific estimated calorie contents. Existing food recognition systems are based on foods found in developed nations. This paper presents a real-time Android system for Nigerian food recognition and documentation system using finetuned MobileNetV2 model. The Convolutional Neural Network (CNN) is effective in image recognition and classification. Therefore, MobileNetV2, a CNN-based deep learning model, is utilized to recognize and classify 10 Nigerian food classes from 500 food images of our dataset developed from scratch known as the NaijaFood101. We evaluated the Nigerian food learning model’s performance to determine the accuracy of the food recognition and classification system. The model achieved an average of 97% recognition accuracy on the evaluation or test data.

Keywords: Food recognition, calorie estimation, NaijaFood101 dataset, Nigerian foods, deep learning, convolutional Neural Network

Abstract: The aggressive technology scaling in VLSI leads to decrease the size of chip. Such continual miniaturization of VLSI devices has strong impact on the interconnects in several ways. Interconnects in high-speed applications suffer from crosstalk, signal delay and ground noise, causing degradation of system performance. Thus, interconnects are becoming a limiting factor in determining circuit performance. This paper presents a comparative study on different interconnect circuit techniques for on chip interconnects. We have compared different circuit structure by placing on RC and RLC interconnects. In this delay benefit for current sensing increases with an increase in wire width. Unlike repeaters, current sensing does not require placement of buffers along the wire and it eliminates any placement constraints. Out of all these techniques a differential RLC current mode signaling circuit insertion has offered the less amount of energy. All the circuits are simulated and compared different parameters such as power, delay and energy by using microwind in 45nm technology.

Keywords: Interconnect, repeaters, wire, Current mode, differential signaling, clamped bit line Sense Amplifier, energy dissipation.

Abstract: In Nanometer regime, process variation and circuit aging cause remarkable and unnecessary and ambiguous circuit system characteristics and the resultant effects on the system design remains a great challenge to the designers. Even though the Guard band design can provide a little protection against these effects yet creates an increased design issues. Hence there is a strong need to equip circuits with the capability of tuning themselves and thereby compensating the variations with a proposed adaptive nature. This work is an effort towards supply voltage adaptation for variation resilience in VLSI interconnects. The main idea is a boostable repeater design that can transiently and autonomously raise its internal voltage rail to boost switching speed. The boosting can be turned on/off to compensate variations. The boostable repeater design achieves fine-grained voltage adaptation without stand-alone voltage regulators or an additional power grid. Since interconnect is a widely recognized cause of bottleneck in chip performance, and tremendous repeaters are employed on chip designs, boostable repeater has plenty of chances to improve system robustness.

Keywords: Interconnects, Process variations, switching time.

Abstract: In the current days, huge number of data are processed by the user in different emerging technologies with respect to data aggregation, it aggregates the bulk of confidential data to be more accurate and efficient as it is handled by business organization. In this process, the problem of data interruption and modification occurs and leads to data inaccuracy and unreliability as there are various confidential information persist in the various business organization. In the data aggregation, the data are divided into several group and with a role provisioning. To overcome the problem of data interruption and modification, propose a MG based network aggregation algorithm with the integration of emerging technology to gather and aggregate the data, energy efficiency can be improved and enhanced based on the input data.  Then the data are gathered and routed to the destination where resource utilization is diminished and network lifetime gets expanded using a grouping-based central entity. In the performance analysis, the ranking the data and similarity are determined along with the evaluation of energy consumption and scheduling length compared with existing DICA and DICA extension.

Keywords: Organization, Data Aggregation, Fuzzy, Neural, data interruption, Resource utilization.

Abstract: The prevalence of online fraud cases, including e-commerce fraud, is rising as a result of technological advancements and the speed with which cybercriminals can change their methods of operation. Scams are nothing new, but as the frequency of transactions without currency rises, so does the trend of online fraud. People are purchasing more goods online as a result of the COVID-19 quarantine because they want to be safe or because the items, they require are hard to get in the shuttered local stores. The best course of action in this circumstance is to implement a fraud prevention service that automatically identifies fraudulent behaviour patterns, associated with the time, place, and device name associated with the login or transaction. This will prevent fraudsters from using the data they stole. You can halt fraudsters before they start a transaction by spotting suspicious activity on an account. Through relevant historical data from databases and machine learning techniques, this study aims to identify fraud patterns in e-commerce transactions. Based on email, payment methods, payment method providers, and transaction volume, this research will train a computer or system that can predict fraud patterns. Machine learning must be used to improve fraud protection in e-commerce since it allows machines to be analysed using learning algorithms. Support vector machine and naive Bayes will be the algorithms employ.

Keywords: Machine Learning, Support Vector Machine, Naïve Bayes, Classification, Fraud E-Commerce.

Abstract: The life cycle of BSF to produce a good productive period, in conditions of warmer temperatures or above 30°C in these conditions adult flies become more active and productive. Likewise, the optimal temperature for larvae to grow and develop is at a temperature of 30°C, but at a temperature of 36°C, it has the effect that the pupae cannot maintain their life so that they are unable to hatch into adult flies. By monitoring the temperature and humidity in the area in the IoT-based BSF (Black Soldier Fly) fly cultivation cage which aims to monitor in real time with cellphones through the Telegram application. It is an attempt to condition the temperature and humidity conditions, which work automatically. Likewise, if there is a change in the light intensity variable, the process of maintaining the intensity value can be done automatically. By using NodeMCU 8266 as a microcontroller that has been programmed to monitor the temperature, humidity and lighting of the area in the drum, by utilizing the LDR sensor (Light Dependent Resistor) to determine the intensity value, and the DHT-22 sensor reading the temperature and humidity values of the area in the cage via placed in the cage, then the value obtained from the DHT-22 sensor and the LDR (Light Dependent Resistor) obtained will be sent to the Telegram application via NodeMCU 8266, then the humidity temperature value and the value obtained will be processed by the Microcontroller to carry out the watering process to reduce the temperature if there is an increase temperature above the specified threshold value and also lighting in the area inside the drum if the intensity value is below the threshold value. With this tool, it is hoped that it will make it easier for business actors to cultivate BSF (Black Soldier Fly) flies in terms of maintaining the temperature, humidity in the area in the cage to keep it stable and automation of lighting, using the Prototype method.

Keywords: Microcontroller, Black Soldier Fly, DHT-22 Sensor, LDR sensor, Internet of Things, Prototype.

Abstract: Liver turquoise is one of the silent and dangerous diseases. If it can be detected in the early stages, the lives of many affected people can be saved. Providing smart methods to identify and diagnose this disease can save patients' lives in addition to reducing medical costs and overheads. In this research, an innovative method using a three-layer radial basis neural network is proposed as a multi-class method for diagnosing liver fibrosis. To increase the accuracy and efficiency of the pre-processed data, the data are balanced using the SMOT method. Also, feature selection is done with the bee algorithm. In this way, the desired features are first reduced using the bee algorithm. For this purpose, a mapping of features is done using the bee algorithm. Then the data with reduced features are applied to the proposed RBF network. The simulation results show that the proposed method is 5% more accurate than similar methods.

Keywords: Liver Fibrosis Diagnosis - Feature Selection – Artificial Bee Colony - Radial Basis Function (RBF) neural network.

Abstract: The mosque is a place for Muslims to carry out worship activities, social activities and other religious activities, one of the activities carried out by mosque administrators is financial management provided by the community. Financial management activities must be carried out carefully because financial information is very important for the community, regardless of the amount it must be accurately informed so as not to cause misunderstandings between managers and the community. The problem at this time is that there is no software for mosque financial managers and donors who are not on time in giving donations to mosques, so that it becomes a consideration for the eligibility of donors. With these problems, the researchers designed a financial management information system that can facilitate DKM administrators in managing finances and neat documentation. The Naive Bayes algorithm aims to classify data into certain classes, then the pattern can be used to estimate the eligibility of donors, so that the Mosque Prosperity Council (DKM) knows whether or not regular donors are eligible.

Keywords: Information Systems; Financial Information System; Mosque; Naive Bayes; Software

Abstract: News has always been an important source of information to build perception of market investments. As the volumes of news and news sources are increasing rapidly, it is becoming impossible for an investor or even a group of investors to find out relevant news from the big chunk of news available. This forecasts real time news sentiment that reflects stock price movement trends. The aim is to create a headline scraping algorithm for sentiment analysis, choosing the most advanced and accurate algorithm for classification, and integrating the models in a single application.

Keywords: API, GUI, News Sentiment, Prediction Model

Abstract: From this problem the author conducted research to design a point of sales information system that can be used to facilitate business processes plus the market basket analysis feature with the FP-Growth algorithm to provide knowledge that is expected to make it easier for owners to make sales strategies and arrange items that must be restocked so that Goods turnover is always smooth. The results of the design show that the system made is able to carry out transaction processes, goods management, report generation and market basket analysis processes well based on black-box testing. The results of the market basket analysis process from 200 transactions were obtained as many as 29 association rules with a minimum support requirement of 5% and a minimum confidence of 75%. The combination of the three highest variables is 'If Octavo Books are 100 OK, then ABD Notes Books' with support of 8%, confidence of 100% and lift ratio of 11.11.

Keywords: Point of Sales, Market Basket Analysis, Website, Algorithm, FP-Growth.

Abstract: Data mining is the method of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Clustering performs an important role in the reference composition of data analysis. Clustering is a famous data analysis and data mining problem. Symmetry can be studied as a pre-attentive feature, which can enhance shapes and objects, as reconstruction and recognition. Clustering, recognized as a crucial issue of unsupervised learning, deals with the segmentation of the data structure in an unknown region and is the basis for more understanding. This paper explains the different types of clustering and methods in data mining.

Keywords: Data Mining, Clustering, Algorithm, k-means Clustering.

Abstract: These days, smartphones and other mobile gadgets play a huge role in all facets of our lives. due to the fact that they essentially gave the same capabilities as desktop workstations and became powerful in terms of CPU (central processing unit), storage, and installing a wide variety of software. Security is therefore seen as a crucial component of wireless communication technologies, notably in wireless ad hoc networks and mobile operating systems. Additionally, as the number of mobile applications grows across a range of platforms, security is seen as one of the most important and significant topics of discussion in terms of problems, trustees, dependability, and accuracy.Security this article intends to give a comprehensive report on thriving security on mobile application platforms and to inform users and businesses about critical dangers. Additionally, several methodologies and methods for security measurements, analysis, and prioritizing at the pinnacle of mobile platforms will be described in this article. Increased knowledge and awareness of security on mobile application platforms are also beneficial for avoiding discovery, forensics, and countermeasures employed by the operating systems. Last but not least, this study also covers security add-ons for well-known mobile platforms and analysis for a poll inside a recent platform security investigation, awareness, sensitive data, and vulnerability.

Keywords: Threats, cyber strategy, mobile platforms, application security, mobile malware.

Abstract: These days, smart phones and other mobile gadgets play a huge role in all facets of our lives. due to the fact that they essentially gave the same capabilities as desktop workstations and became powerful in terms of CPU (central processing unit), storage, and installing a wide variety of software. Security is therefore seen as a crucial component of wireless communication technologies, notably in wireless ad hoc networks and mobile operating systems. Additionally, as the number of mobile applications grows across a range of platforms, security is seen as one of the most important and significant topics of discussion in terms of problems, trustees, dependability’s, and accuracy. Security this article intends to give a comprehensive report of thriving security on mobile application platforms and to inform users and businesses about critical dangers. Additionally, several methodologies and methods for security measurements, analysis, and prioritizing at the pinnacle of mobile platforms will be described in this article. Increased knowledge and awareness of security on mobile application platforms is also beneficial for avoiding discovery, forensics, and countermeasures employed by the operating systems. Last but not least, this study also covers security add-ons for well-known mobile platforms and analysis for a poll inside a recent platform security investigation, awareness, sensitive data and vulnerability.

Keywords: Threats, cyber strategy, mobile platforms, application security, mobile malware.

Abstract: The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.

Keywords: Classification, Fake user detection, Online social network, Spammer’s identification.

Abstract: From past year wireless technology makes tremendous growth. Evolution and revolution of wireless technology is reached at seven.5G. Wireless technology FG (Future Generation) mobile communications can have higher knowledge transmission rates than 6G and 7G. Wireless technology is unceasingly one among the most well liked areas that square measure developing at a high speed, with advanced techniques rising in all the fields of mobile and wireless communications. Current times square measure simply the beginning for deploying 5G mobile communication systems. at the moment we've got many technologies every capable of playacting functions like supporting voice traffic mistreatment vox information processing (VoIP), broadband knowledge access in mobile atmosphere etc., however there's an excellent want of deploying such technologies which will integrate all these systems into one unified system. 8G presents an answer of this downside as it is all regarding seamlessly desegregation the terminals, networks and applications. In this paper an effort has been created to supply a study of various cellular technologies particularly 4G, 5G, 6G, 7G, and FG severally and detail comparison among them.

Keywords: Network; communication, Mobile Technology, Cellular Generation

Abstract: Virtualization is used to make simulated environment through a physical hardware system. The software that controls cloud technology is virtualization, while cloud computing is a digital facility. Virtualization and cloud computing knowledges share a exclusive relationship and often work together. The virtualization process in cloud computing is where a name is allotted to the physical storage and is available on demand. A single dedicated hardware can do a great job in virtualization. There is a host machine and a visitor machine.

Keywords: Virtualization cloud computing host.

Abstract: Security of cloud computing is a firm hand of cyber security devoted to securing cloud computing systems. Security of cloud computing or directly cloud security touch on to a board set of policies, technologies, applications, and controls utilized to protect virtualized IP, data, application, services, and the associated infrastructure of security of cloud computing.

Keywords: Security, Cloud Computing

Abstract: Computer graphics is the field of computer science that deals with generating images using computers and manipulate them. It is an important area of research with applications in many fields, including medicine, engineering, and entertainment. In recent years, there have been significant advances in the realism and sophistication of graphics, due to the development of new algorithms and the increasing power of computers. Computer graphics and simulation are two important fields that have seen tremendous advances in recent years. Graphics have become increasingly realistic and sophisticated, while simulations have become more accurate and detailed. In this talk, we will review some of the recent advances in these two areas, and discuss how they can be used together to create even more realistic and immersive experiences.

Keywords: Manipulate, Simulation, Graphics, Realism.

Abstract: The use of smart phones ready with GPS navigation unit have accelerated swiftly from 3% to extra than 20% in the past five years. therefore, a clever cellphone may be used efficaciously for non-public safety or various different protection functions especially for women. This app can be activated with the aid of a single click on while the person feels she is in hazard. This application communiqués the person’s location to the registered contacts for each few seconds in the form of message. consequently, it acts like a sentinel following at the back of the man or woman till the person feels she is safe. This paper affords evaluation a completely unique characteristic of this utility to ship the message to the registered contacts continuously until they are pressing ‘assist’ button. non-stop place monitoring facts through SMS facilitates to locate the vicinity of the victim quickly and can be rescued properly. This software objectives to make sure women safety. that is accomplished by means of addressing the situations that compromise the protection of girls in these days’s day and age. This app ensures ladies aren't put into such situations thru diverse functions supplied by our gadget.

Keywords: Android, GPS Location, Database, URL, Smart Phone

Abstract: chat bots are the knowledgeable systems that understands and responds to the question asked by users in their own language. chat bot responds in voice communication a touch like however a person's move with each other. throughout institute’s tutorial admission procedure there is a massive queue at the enquiry window. students simply have to be compelled to add question range to the chatbot and it can answer to student question. a login and register system webpages can also be additional inside the system to forestall unknown users from gathering details. the user might offer their suggestions through the suggestion box. the system replies exploitation an economical graphical worm that suggests that as if a real person is interacting with the user. this chatbot is usually a sort of service that is in a position to be your agent providing you enquiry data regarding faculty.

Keywords: Chat Bot, Virtual Assistant, College Enquiry, Web Application, Queries.

Abstract: Fraud is increasing with the expansion of modern technology and the globe of communication, results in the loss of billions of dollars every year. Though preventions are the best way to reduce fraud, fraudsters are adaptive, they will usually find there ways to avoid such measures. Methods for the detection of fraud are vital. If we are to catch the fraudsters once fraud prevention has failed. Effective technologies for fraud detection has been provided by statistics and machine learning and they have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, computer intrusion and telecommunications frauds. The areas in which fraud detection technologies are most used are describes the tools available for statistical fraud detection

Keywords: Fraud detection, credit card fraud, money laundering, computer intrusion, telecommunication fraud.

Abstract: Technology used like IoT increasing day by day in today’s world. This article put a light on what is meant by E- campus Environment. How it is useful for student and teachers to enables campus from anywhere. By operating during this manner access and communication with the various type of gadgets and devices like camera, audio recorder, sensible watches, google glass, digital broad displays, sensors …. etc. The IoT will nurture the development of learning circumstance that make use of the large subject Information generated by those objects to provide dynamic services to lecturers, learners and even to contain developers in trendy field, sensible field permits us to North American country IoT methodologies to create it accessible for room notes everyplace within network space our objectives. Square measure to create simply shareable notes share victimisation web-based environment s/w which permit North American country to share via IoT enabled to accesses among network limit. We and to don’t have any IoT enabled resources in school and university campuses for this purpose thus far in learning setting.

Keywords: IoT, E-Campus, Sensors, Camera, Audio recorder, Sensible watches etc.

Abstract: This paper proposes a general design of cloud storage system, analyses the functions of the parts, and discusses the key technologies, etc. Cloud storage could be a novel storage service mode which the service suppliers provide storage capacities and information storage services through the net to the clients; meantime, the clients needn’t apprehend the main points and lowered structures and mechanisms. The planned design of cloud storage is layered and cooperative, and therefore the mentioned key technologies involve preparation, storage virtualization, information organization, migration, security, etc. The operation mechanism as well as ecology chain, scientific theory, hymenopter on colony optimisation, data life cycle management, maintenance and update, convergence and evolution mechanisms area unit analysed too. therefore, AN overall and new viewpoint to cloud storage system is illustrated.

Keywords: Cloud Storage Architecture, Key Technologies, Operation Mechanism, Ecology Chain, Game Theory, Ant Colony Optimization.

Abstract: A steganography is the science of sending secrete messages between the sender and receiver. It is such a technique that makes the exchange of covert messages possible. Each time a carrier plays a major role in establishing covert communication channel. This survey paper has more about network protocols to be used as a carrier. There are a number of protocols available to do so in the networks. TCP/IP protocol suite has been a potential target for network steganography from the very beginning. It has a lot of possibilities for creation of hidden channel that can be used to communicate covertly. This paper depicts the technique that creates a covert channel using the Overflow filed of Timestamp option of Internet protocol, version 4, over a Local Area Network. This technique implements a storage area network steganography that uses the timestamp option which is used to hide information by modifying protocol header fields, such as unused bits of a header, or the data field of a packet.

Keywords: Cryptography, Steganography, Protocol, Steganography, Covert, IP4

Abstract: Cryptography is the science of information security. The word stands resultant from the Greek kryptos, meaning concealed. Cryptography contains techniques such as microdots, merging words through images, and other ways to hide in order in storage or transfer. Modern cryptography interconnects the disciplines of mathematics, computer science, and electrical engineering. Applications of cryptography contain ATM cards and laptop passwords. Cryptology before the modern age was almost the same as Encryption, the translation of information from an understandable state to apparent nonsense. The sender retained the ability to decrypt the data and avoid redundant persons being able to read it.

Keywords: Security, Cryptography, Decryption, Encryption.

Abstract: Latar belakang penelitian, ini adalah penggunaan internet merupakan kebutuhan yang tidak dapat dihindari lagi. Dengan internet, segala sesuatu menjadi lebih mudah dan cepat. Namun dibalik kemudahan dan keuntungan dengan hadirnya internet, terdapat permasalahan yang menyertainya. Masalah keamanan telah menjadi fokus utama dalam dunia jaringan komputer, yang disebabkan tingginya ancaman yang mencurigakan (suspicious threat) dan serangan dari Internet. Keamanan Informasi merupakan salah satu kunci yang dapat mempengaruhi tingkat kehandalan (Reliability) suatu jaringan. Untuk mengatasi permasalahan keamanan jaringan dan komputer ada beberpa pendekatan yang dapat dilakukan. Salah satunya menggunakan sistem IDS (Intrution Detection System) dan IPS (Intrusion Prevention System). Tujuan penelitian ini adalah merancang sistem IDPS dengan password terenkripsi dan kunci kripto pada Secure Shell (SSH).

Keywords: Intrution Detection System; Intrusion Prevention System; Secure Shell.

Abstract: Drowsy driving is one of the most road accidents. Totally different techniques are reportable in the literature to discover drivers’ sleepiness. However, the majority of the prevailing systems solely alert the motive force if sleepiness is detected. Consequently, the drowsy driver continues driving, with a high risk of a devastating accident. During this paper, we have a tendency to project associate degreed verified an EEG primarily based system that not solely alerts the motive force by alarm, but conjointly puts the vehicle in semiautomatic parking mode by dominant fuel provide if sleepiness is detected. At an equivalent time, it reports closed police offices by SMS that contains necessary info to require essential steps locating the vehicle. hold on EEG signals, obtained with wireless wearable headsets in different subjects {in totally different |in several |in numerous} conditions by different analysis teams, were utilized in this work. Power spectrum analyses were dispensed in MATLAB to see the dominant frequency elements within the brain signals. The slow wave to quick wave ratios of EEG activities was assessed for a variety of epochs to see the driver's sleepiness. GPS and GSM modules were used with Arduino MEGA for the following, remote notification and servomotor management. The performance of the projected system was evaluated by holding on to information that confirmed its feasibility and responsibility.

Keywords: Smart system, driver fatigue detection, remote notifications, drowsiness detection, Arduino MEGA, GPS module.

Abstract: Educational institutions are now paying more attention to what students think about their participation in tutoring and literacy activities through reviews or feedback. The Feedback Management System is a web-based platform that collects student feedback online. The goal of the project was to create a system of evaluation that would benefit both students and educators. The system includes the creation and analysis of feedback runners for teachers, as well as summaries and feedback delivery. All council students and staff members are included in the system's development. Additionally, students can express their opinions regarding their professors. The student's options are as follows: Strongly agree, Agree, Partially agree , Disagree. further, after attempting each question, the user must offer his input to the system. This online feedback system is the best place to find feedback that is estimated according to the conditions, and it is the most useful for getting students and staff feedback analysis. Evaluation type Strongly agree Agree Partially agree Disagree

Abstract: This paper presents a low-cost flexible and reliable home monitoring and control system with additional security using ESP32, with IP connectivity through local Wi-Fi for accessing and controlling devices by formal user remotely using android smart phone application. This system is server selfgoverning and uses internet of things to control human desired appliances starting from industrialized machine to user goods. Home monitoring and device control system not only refers to decrease human efforts but also save the energy and time competence. To demonstrate the effectiveness and feasibility of this system, in this we presents a home monitoring system by using ESP32 module. It helps the user to monitor various conditions in the home like room temperature, gas leakage, water levels in the tank and person detection and control various appliances such as light, fan, motor, gas knob and take decision based on the feedback of sensors remotely.

Keywords: ESP32, Internet of Things (IoT), Wi-Fi network, PIR (passive infrared) sensor

Abstract: Network security has become major crucial to personal computer users, bureaucratic, and the services. With the arrival of the internet, security became a crucial anxiety as well as the past of security approve a better understanding of the disclosure of security technology. The internet complex itself allows for many securities risks to happen. If the architecture of the internet is adapted, it can bring to the possible attacks that can be sent over the network. Aware the attack methods allow us to emerge with proper security.

Keywords: Firewall, Threats, Network Security, Network Security Architecture.

Abstract: The IoT (Internet of Things) is the network in which physical devices, equipment, sensors and extra objects can communicate between themselves without human involvement. The WSN (Wireless Sensor Network) is a dominant component of the IoT, which has multiplied into numerous different applications in real-time. The IoT and WSNs now have many critical and non-critical applications impacting almost every area of our everyday life. Wireless sensor networks with the mobile sink can support to prevent the hot-spot problem and improve the network lifetime. Nevertheless, in practice, the courses of the sink cannot ideally change due to the obstacle of the environment or the necessity of the application.

Keywords: Internet of Things (IoT), Wireless Sensor Networks (WSN), Energy-efficiency, Data Aggregation.

Abstract: This research paper is made for how to control Robotic car by the voice command through remote operation and Android application along with an arm series type micro-controller is with least for doing the desirable operation there in a HC-05 Bluetooth module connected in Robotic car through Android application. There is a provision for sending the commands through the Android application with in form of power Button or voice command for the moment of the robotic vehicle to de Caesar Servo Motor are any interests by the microcontroller switch price or voice command are taken by the RF transmitter and Bluetooth which then connected to Digital encoded data for getting enough range about hundred metres from the Robotic car this Robotic car is used in an emergency for today directed command by voice when physical commands are hard to Voice command make this Robotic car more convincing and easy to the for old any age group nowadays. The branch Robotics is playing major role of enhancing in in Lifestyle to minimise the direct physical labour. Also, it has much scope in the field of artificial intelligence. So we chose this project which which is the future of department and nutrient in technology.

Keywords: Arduino, Android, Bluetooth, Servo Motor, Transmitter, Robotic.

Abstract: In this work we present a approach for shortlisting candidates for a particular job position. Every company invest a lot of money and time for recruiting people to fill some specific positions. They invest a lot of resources finding a appropriate candidate but the total invest become loss if the candidate do not join the company after completing the entire process. The main aim of this research is to predict joining efficient candidate with minimum cost and time by using the attributes like number of hackathons given, skills, internship, salary expectation and preferred job location using the machine learning algorithm to build a model Key Words: Machine Learning, decision tree, K-nearest neighbors, Random forest

Abstract: The internet is a huge virtual space wherein to explicit and percentage man or woman evaluations, influencing any factor of life, with implications for marketing and verbal exchange alike. Social Media are influencing purchasers' options through shaping their attitudes and behaviors. monitoring the Social Media activities is a great manner to degree customers' loyalty, keeping a track on their sentiment towards manufacturers or merchandise. Social Media are the following logical advertising and marketing arena. currently, fb dominates the digital marketing area, followed closely by means of Twitter. This paper describes a Sentiment evaluation take a look at performed on over than 1000 fb posts about newscasts, evaluating the sentiment for Rai -the Italian public broadcasting provider -in the direction of the emerging and more dynamic private company La7. This observe maps examine effects with observations made by way of the Osservatorio di Pavia, that is an Italian institute of studies specialized in media evaluation at theoretical and empirical stage, engaged in the analysis of political conversation inside the mass media. This take a look at takes additionally in account the statistics provided with the aid of Auditel regarding newscast target audience, correlating the evaluation of social media, of fb particularly, with measurable facts, to be had to public area.

Keywords: Sentiment Analysis, social media, Digital Marketing Area, Public Area.

Abstract: In recent years, cloud computing has changed the way that resources are used, allowing users to request resources whenever they need them. The scheduler of cloud computing uses task scheduling and resource allocation algorithms for efficient and effective load balancing of a workload among cloud resources to improve the overall performance of the cloud system when the highly incoming user requests are coming for the resources. But cloud providers are limited by the amount of resources they have, and are thus compelled to strive to maximum utilization. When the credit based task length & priority scheduling algorithm is used to schedule the task without knowing the deadline of the task, it will cause the dead of the task that having least deadline. In this paper a new hybrid approach (Hybrid TLPD) is designed which is a combination of credit based task length & priority algorithm (TLP) and credit based deadline algorithm. In the new hybrid algorithm, the assigning number of resources to the tasks in such a way that there will be minimum execution time and minimum response time is achieved. With the help of Cloudsim and Net beans IDE8.0 the designed algorithm is simulated and analyzed the results.

Keywords: Task length & Priority, Hybrid TLPD, FCFS, SJF, Cloudsim

Abstract: C language, one of the widely used languages among the programmers. It is one simple and powerful language that is being used on many platforms.It was developed at Bell laboratories by Dennis Ritchie. If we learn C, it becomes easier to learn other programming concepts. In this paper we will discuss about C Language.

Keywords: C programming, Dynamic memory allocation, other programming languages, drivers, kernels

Abstract: Python: We are aware that websites like YouTube and Dropbox make use of Python Programming in their source code? Python is a commonly used language that anyone can easily understand and apply. We can make nearly anything using Python. Most systems today (Mac, Linux, UNIX, etc.) have Python installed as a default setting since it is an open-source language and free language. Upon reading this book, we are going to become fluent in this awesome code language and see it applied to a variety of examples. No type of declaration of methodology, parameters, functions, or variables (like in other languages) is found in Python making its code concise and easy. As I said earlier, we can use language in everything if we want to build a website, make a game, or even create a search engine. The large plus of using Python is, an obvious compiler is not necessary since it’s an entirely interpreted language .Net: When we hear the name .NET, it gives a feeling that it is something to do only with internet or networked applications. Level although it is correct that .NET provides a hard basis for increasing such requests it is likely to make a lot of new forms of demands. The following list determination give you an idea of various types of applications that we can develop. NET. • ASP.NET Web applications: These comprise data-driven and dynamic browser-based programmers. Apps that use Windows Forms are the standard rich client applications. • Console applications: These are the standard DOS types of programmers, such as batch scripts. • Component Libraries: These are collections of parts that frequently include some business logic. • Windows Custom Controls: You can create your own Windows controls, just like with conventional ActiveX controls. • Web Custom Controls: By extending the concept of custom controls to web applications, code reuse and modularization are made possible. • Web services: These are features that can be accessed via "web callable" protocols like HTTP, XML, and SOAP.

Keywords: Methods, Python Net installation

Abstract: A web browser is employed to show sites, navigate from one website to a different with the utilization of hyperlinks, and transfer any sort of information right from PDF files, displays, Word files to music, videos and pictures.Browsing is exploitation the mouse, keyboard and bit (in case of smartphone applications. however what concerning the incapacitated and the visually impaired users? however would they use the search engine? we've tried to come back up with an answer by creating a Voice primarily based Browser that's a totally hands-free search.

Keywords: Speech Recognition, Text to Speech, Web Browsing, Speech Synthesis, Web Crawler.  

Abstract: Virtual reality (VR), is a representation of artificial reality created entirely in 3D graphics using information technology. VR makes it possible to experience the real world by sending sensory data to the brain via a special system. The purpose of virtual medicine is to minimize direct contact and effects on the human body during treatment. Given the increased availability of high-quality electronic devices, their enormous computing power, and the ever-evolving  Internet infrastructure, progress in this area is only a matter of time. Because of this, the purpose of this article is to identify VR applications in medicine as well as some of the ways that virtual reality can be used to educate and support medical professionals as well as to improve their lives and heal patients.

Keywords: Virtual Reality (VR), Healthcare, Medical training, Devices in VR  

Abstract: Imparting the most comprehensive account of the multidisciplinary discipline of HCI, this e-book illustrates the powerful advantages of a person-orientated method to the layout of contemporary pc systems. It balances the technical and cognitive problems required for information the subtle interaction among humans and computer systems, especially in emerging fields like multimedia, virtual environments and pc supported cooperative work (CSCW). a completely unique characteristic is the inclusion of interviews with many main government in HCI, supplying non-public perception into their paintings and conveying the pleasure of cutting-edge research activity: Human-pc interaction is flexibly dependent to allow a spread of gaining knowledge of paths for students in computer science, engineering, psychology and cognitive technological know-how. Programmers and device designers will admire its emphasis at the layout of interactive systems.

Abstract: Protecting Wireless Sensor Networks from various security attacks is challenging. The effect of destructive security attacks like black-hole and warm-hole are more on resource-constrained Wireless Sensor Networks; these attacks target the nodes and cause packet alteration, routing disruption, and node failure. Adding malicious nodes to an existing wireless sensor network is one of the common threats. Malicious nodes may reduce network reliability by saturating the network with traffic, sending data, or creating new paths.This paper proposes a methodology based on the AODV protocol to detect and recover malicious nodes using trust metrics like node behaviour, acknowledgements, and residual energy, nodes fail to score threshold value declared as malicious, and node recovery mechanism activated, idle node present near to the malicious node recover the affected node and make it eligible for further communication. Experimental results are conducted using NS2 and proved that the proposed methodology enhances the Throughput, Packet Delivery Ration and reduces End to End Delay by identifying and recovering the malicious nodes.

Keywords: Security, Malicious Nodes, AODV, Node Recovery, Wireless Sensor Network.

Abstract: Many constructions that are being constructed at present tend to wind-sensitive because of their shapes, slenderness, flexibility, size and lightness. This leads to demands for economical wind resistant designs. In the designs of high-rise buildings, lateral loads like wind and earthquake forces play a significant role in the stability of the structure for. In the present study only wind effects on tall buildings are studied. Wind loading on the basis of wind tunnel tests is carried out on square shaped building model with uniform cross-section along the height given for different building models. In present study, square shape is considered. The present study is carried out under two major heads namely: (i) Experimental study and (ii) Response study. In the experimental study, the rigid model of the tall buildings with square is tested in boundary layer wind tunnel in order to find mean and fluctuating pressures at various points in different surfaces. Square shape models are tested in open circuit wind tunnel in which small blocks are used as the obstruction to meet the wind tunnel simulation requirement and for the development of turbulent flow for generating the atmospheric surface layer in the 2mX2m cross section at wind tunnel. The effects of shape of the building model on the wind pressures distribution are also discussed. The second part of the present study is to carry out study to obtain response of the building having square shape by using wind loads obtained experimentally in part one. Prototype buildings are assumed to be made of RCC beams and columns with grid size 20mX20m and story height 3.75m (ground floor) and 3.125m (remaining 18 floors). The buildings are analyses by readily available software package STAADPRO. Mean response including moment about axis, shear force, twisting moment and displacement are obtained under wind incidence angle in order to study the effects of square shape under wind loads.

Keywords: RCC, STAADPRO.

Abstract: Cyber security has become a vital part of information technology. The need to secure the information has increased manifolds during the present scenarios. While making one’s data authentic one has to face various challenges. Cyber security has come into limelight due to the high rate of cybercrime in our society. Although government and companies are taking various steps to curb it but all seems to be in vain for a majority of people. This paper mainly focuses on the various threats imposed by cybercrime and the measures taken to prevent it. It basically deals with the different types of hackers in our society.

Keywords: Security,Internet,privacy, Hackers, cybercrimes, Integrity,Phishing,identity theft, Authentication.

Abstract: Credit Card Fraud detection is difficult for researchers as fraudsters as fraudsters square measure innovative, quick-moving people. Credit card fraud detection is difficult because the dataset provided for fraud detection is incredibly unbalanced. In today’s economy, credit card (CC) plays a big role. It's associate inevitable a part of a household, business world business whereas mistreatment. CCs are often an enormous advantage if used cautiously and safely, important credit monetary harm are often incurred by dishonest activity. Many ways to manage rising credit card fraud (CCF). During this paper, associate ensemble learning-based and intelligent approach for detecting fraud in credit card transactions using XGBoost classifier square measure want to observe credit card fraud, and it's a lot of regularized type of Gradient Boosting. XGBoost uses advanced regularization (L1 and L2), that will increase model simplification skills. What is more, XGBoost has an associate inherent ability to handle missing values. Once XGBoost encounters a node at lost weight, it tries to separate left and right hands, learning all ways to the very best loss.

Keywords: Credit Card, credit monetary harm, XGBoost

Abstract: Cognitive robots are required to work in dangerous areas since humans are unable to due to health-related restrictions. Robot interaction with the working environment is hampered by the fact that a robot cannot learn the spatial semantics of the environment or an object. In this work, a computational agent is created to address this issue. This agent learns cognitive maps from input spatial data of an environment or an item by simulating the behaviour of place neurons and grids. It is suggested that a novel quadrant-based modelling strategy be used to simulate the behaviour of the grid neuron, which, like the real grid neuron, can produce periodic hexagonal grid-like output patterns from the input body movement.

Abstract: This research paper's primary goal is to is to demonstrate how to play a computer game using human gestures. The system's secondary goal is to develop a system that allows a player to play a game without using a physical controller. This system seeks to create a gesture recognition application. The system's attached camera or webcam may be used to identify human hand gestures. The operations on the Game will be performed with the game's basic gaming controls based on the analysis performed by the program on identifying human Hand Gestures. The software includes instructions for recognizing human hand motions. Hand motions should be made using the palms of the hands. The system will be divided into three sections: user interface, gesture recognition, and analysis. The user interface module provides the user with all of the necessary graphical user interface to register the user's arm positions for performing gestures. Gesture recognition by the Media pipe is a cross-platform library developed by Google that provides amazing ready-to-use ML solutions for computer vision tasks. Open CV library in python is a computer vision library that is widely used for image analysis, image processing, detection, recognition etc. This research paper will be accomplished with the help of OPEN CV.

Keywords: Camera, hand gestures, motions, recognitions, Media pipe.

Keywords: Machine learning, soil properties are Calcium, Phosphorus, pH, Soil Organic Carbon, and Sand.  

Abstract: The thesis presents a mechanism for research paper classification based on a relational graph that represents the interrelationships among papers, such as citations, authors, common references, etc. The proposed method is a semi-supervised learning that have used graph convolution neural network in learning the relations. The GCNN captures the spatial relation i.e. neighbors of a node in feature vector creation. The Proposed method makes use of message passing system, where node sends their feature values i.e. word embedding to their neighbors node so that every node can create its own feature vector based on the content of its own article and its neighboring articles.  This method has better performances it tries to predict the class based on the neighboring classes and the content of its neighbor which is common between them. Comparison with the previous methods, the proposed method has performed well with a significant margin. Keywords: GCNN, Article Classification

Abstract: The banking industry has applied automated technologies since the late 1970s, focusing on transactional, low-risk, and volumes of business-type tasks. Several banks are now investing in research and programs for intelligent data engineering to help eliminate less predictable and consequential low-impacts tasks in human and decision-making intensive processes. Functions that are needed to be augmented by data engineering’s intelligence capabilities to help improve productivity, performance, and customer service quality in these areas include investment decision-making, wealth advisory, loan issuance, model risk monitoring, model validation, regulatory compliance, market risk assessment, and credit loss estimation. However, it is unclear how the new opportunities of innovation through intelligent technologies would be implemented in existing domains without augmenting quest tools and technologies in terms of managing augmented traditions, organizational, and institutional dilemmas and challenges, and what implications the new opportunities would pose to rethink fraud-catching, risk estimating, trading, corporate colloquialism model-led change processes, treasury management, and risk-compliance management. This concern arises from the fact that financial system technologies’ prior investments and developments are already sufficiently complex and complete. There may be an AI and data engineering-enabled revamping of data lifecycles in the banking and finance industry. The proposed adaptation may be substantially broader than the narrowly defined opportunities within well-defined and low-contest markets and domains typically approached in automating sector-specific applications. Available privacy, regulatory compliance, fairness, transparency, and explainability issues and such are already well-known in practice. The analysis suggests opening up high-consequence areas for banking and finance experts to augment their competitive intelligence to engineer robust and reliable domains or trading engines to monitor and mitigate extreme risk events for systemic risks while rethinking and reconstituting trust in sequential rules and risk choices in addition to inputs. Future work should provide in-depth analyses of the aqualism implications and apply the approach to other industries where data engineering and intelligent technologies may have a similarly profound impact.

Keywords: Financial Innovation,Artificial Intelligence (AI),Data Engineering,Risk Management,Regulatory Compliance,Banking Technology,Predictive Analytics,Machine Learning in Finance,Real-time Risk Assessment,Compliance Automation,Fintech Disruption,Big Data in Banking,AI-Driven Decision Making,RegTech Solutions,Digital Transformation in Finance.

Abstract: Cloud service providers use AI capabilities to provide clients with AI Service in addition to usual cloud services today. In such a scenario, business departments can use these AI Services conveniently by just calling APIs. For example, the price forecasting can be done by inputting time series data into an AI Service without having the Data Scientists or ML Engineers to build model training or hyper-parameter tuning. However, AI models with complex structures are difficult to interpret and explain as black boxes. As a result, it is essential to monitor the distribution of input and prediction results and show alert signals to end-users as well as business departments. As model governance is crucial for AI compliance, on the one hand, there come questions about how to monitor cloud-based AI services to enhance interpretability and transparency. On the other hand, to analyze time series-based data and gain insightful views, auto-ML technology is becoming popular to build, analyze, and optimize time-series forecasting models. In addition to black-box models, such custom-built model deployment applications also need built-in rate sampling, distribution, and prediction monitoring mechanisms after deployment. Furthermore, there exists a gap in the visual exploration of time-series forecasting model analysis and monitoring in contrast to the explainability of the prediction as an algorithm-agnostic solution. Many methods have been proposed to deal with time-series data, and how to provide an effective and efficient data API is essential. In the big data era, modern applications generate massive volumes of time-series data as a result of the high frequency of measurements from IoT devices, sensors, and financial transactions. Data mountains cause problems in solving data storage, computation, and analytics, and there is a timely review of big time-series data, a class of big data. With the increased need for data-in-cloud pattern recognition and intelligence discovery, temporal data mining has attracted growing attention. A lot of pattern mining methods have been proposed, and in contrast, visualization support on temporal data mining is scarce.

Keywords: Wealth Management,Artificial Intelligence (AI),Financial Services,Cloud Computing,Data Engineering,Next-Generation Finance,Robo-Advisory,Predictive Analytics,Portfolio Optimization,Digital Transformation,Fintech,Big Data,Machine Learning,Intelligent Automation,Customer Personalization.

Abstract: Cloud service providers are adopting AI-based systems to efficiently offer data services. A data platform combines data management systems, serving storage, and compute infrastructure. A data service is the result of the careful orchestration of these services, with a set of users on the right-hand side and their data and knowledge requests on the left. The data service is typically composed of microservices with data as an object. Each microservice then offers APIs and SDKs for users to submit tasks and queries to the data platforms. The cloud-scale data platforms and the data service architecting and planning should be automated so that users can focus on describing their workloads without worrying about the underlying architectures. Data service design is extremely challenging. The workloads are broad and horizontally scaling. All the architectural components are stateful, dynamic, and performance-sensitive. The architectural complexity is huge due to the vast design space and requirement sets. The trade-offs on diverse metrics and concerns are crucial. The aforementioned challenges are further magnified in cloud-scale systems. A simulation-based framework is built to facilitate performance- and power-accuracy exploration across heterogeneous hardware implementations. The framework is employed to explore the design space of big data analytics written in a high-level domain-specific language for reconfigurable systems. An architecture transformation framework is presented to transform plain applications into efficient hardware blocks. The framework performs automatically instruction-level optimization on a petascale simulation kernel, achieving speedup over state-of-the-art toolchains and domain-specific compilers.

Keywords: Real-Time Data Processing, Cloud-Native Architecture, Scalable Data Pipelines, AI-Driven Insights, End-to-End Data Integration, Data Lake House Architecture, Streaming Analytics, Machine Learning at Scale, Event-Driven Architecture, Unified Data Platform, Low-Latency AI Inference, Big Data Orchestration, Cloud Data Warehousing, Predictive Analytics in Real Time, Automated Data Engineering.

Abstract: Automatic disease diagnosis using medical imaging has been a hot research topic in the past few years. Over the past decade, significant research efforts have been made in X-ray and CT image analysis and diagnosis of different diseases, including but not limited to laparoscopic surgical actions, kidney stone types, Alzheimer’s disease, and other general diseases like heart problems. Medical imaging is a very helpful and effective tool for the diagnosis of atypical and common symptoms. In recent years, novel and enhanced imaging methods have been developed for the effective extraction of medical images with advanced resolution and other enhanced features. However, although modern imaging modes are advanced and very effective for extraction, the interpretation of these images is still labor-intensive and requires high expertise in the relevant field. There is a growing void between the discovery of images and their interpretation due to the scarcity of expert doctors in this field. The solution is automation, and the best approach to deploy such automation at a grand scale in real life is to utilize AI. Artificial intelligence comprises various fields that assist in tackling tough problems in automation, such as computer vision, natural language processing, and robotics. Among these different fields, computer vision has achieved tremendous success in recent years and is very active in both academia and industry. Numerous intelligent computer vision systems are deployed in different domains, including but not limited to autonomous driving, agriculture, wildlife, security, social media, smart retail, and health care. The healthcare domain is one of the most active computer vision research areas due to automatic medical imaging diagnosis becoming an increasingly attractive research problem. Early automatic diagnosis is essential for providing timely interventions. It is difficult to discover effective workaround solutions for complex processes like human actions, building structure parsing, security event understanding, and so on. But it is comparatively easier to devise solutions. Thus, significant research efforts have been made in medical image analysis and disease diagnosis.

Abstract: The trend toward big data (BD) in the financial technology (FinTech) sector has recently gained momentum [1]. By utilizing cloud-based services and advanced algorithms, insurers, banks, credit unions, and other players in the finance ecosystem can efficiently gather and analyze vast amounts of data. Behavioral data (Big Data) gathered through the web and mobile channels is essential to proactively assess risks and opportunities for financial institutions. Here, it is postulated that Cloud-based architecture, aided with big data analytics and AI, augments precise financial modeling in the insurance and banking ecosystems. The paper constructs a hierarchically-structured smart architecture with a four-tier cloud databank, and data mining integrated with AI. It discusses intelligent big data services and cloud-based data markets for financial modeling. The significance and pressing need for the research are highlighted. Integrated cloud-based architecture as intelligent big data analytic services for financial modeling. New cloud-based financial big data technologies and smart buildings. Intelligent big data services for financial modeling: benefits and challenges. A cloud-based insurance ecosystem was proposed. A comparison of cloud-based insurance systems was also examined. Integrating financial modeling: Services and challenges. Intelligent performance benchmarking-architecture of competitive insurance and banking systems. Financial modeling of insurance, banks, and others. A summated proposal for the integration of AI into the systems was made. The broadest observations were also uncovered.

Keywords: Big Data, Artificial Intelligence, Financial Modeling, Cloud Computing, Predictive Analytics, Risk Assessment, Customer Segmentation, Real-Time Processing, Machine Learning, Data Lakes, Insurtech, Fintech, API Integration, Regulatory Compliance, Data Security, Scalable Infrastructure, Decision Automation, Fraud Detection, Digital Transformation, Personalized Services

Abstract: This paper describes predictive modeling applied to optimization of solar power generation systems. Such modeling, based on machine learning principles, is performed for both solar irradiation and load demand, applied to both redistribution of load demand to specified time slots, and to time-specified prediction of power generation and load demand. Weather prediction is the most important part of solar power generation forecasting, particularly with reference to solar resource inflation, deflation, and backfill. A method of optimization of solar power generation systems combining known methods is proposed. AI-enhanced predictive modeling, neural fuzzy modeling with fuzzy-weighted regionalization, net-load creation and solar power generation forecasting from multi-analysis of previous generation time history, event overlay on prediction of net-load shape, prediction combining, envelope based backfilling, and bottoming by thermal and hydro resources, are elements used. Generalization of predictive modeling principles and methods, in particular for net-load modeling, can be performed for any other renewable power source. Electricity load demand forecasting is one of the most challenging tasks in power distribution system management, in both near and long terms. For forecasting, the main challenge consists in the presence of some characteristic load structures such as daily and even weekly periodicities, promotion for special events, season and long period past generalization by means of supporting production of particular events similarly defined, high relationship of non-shiftable elements on near and mid-term forecasts, and season relationships to long-term ones. There are two different approximation intents, and accuracy somewhat split between them.

Keywords: AI-driven optimization, solar power generation, predictive weather modeling, load forecasting, machine learning, energy management, renewable energy, power output prediction, real-time data, smart grid, energy efficiency, photovoltaic systems, deep learning, demand prediction, energy forecasting, intelligent control systems, data analytics, operational efficiency, weather-based optimization, sustainable energy systems.

Abstract: The evolving landscape of commercial insurance is becoming increasingly intertwined with the advancements in artificial intelligence. This transformation is particularly pertinent for small businesses, which often face unique challenges in risk assessment and management. AI technologies are poised to overhaul traditional risk profiling methods in commercial insurance, offering a more nuanced, data-driven approach. By leveraging machine learning algorithms, natural language processing, and predictive analytics, insurers can obtain a more comprehensive understanding of risk factors pertinent to small businesses. This integration enables insurers to offer more tailored and accurate risk assessments, which are both cost-effective and timely. In this work, we examine the intersection of AI and commercial insurance, focusing on how these technologies can be harnessed to enhance risk profiling for small enterprises. Traditional risk assessment models often rely on historical data and generalized assumptions, which may not adequately capture the complexities of small business operations. AI provides an opportunity to refine these models by integrating vast amounts of structured and unstructured data from multiple sources, such as social media, financial records, and even IoT devices. This data-driven approach facilitates a more dynamic and responsive risk assessment, evolving continuously as new data becomes available. Moreover, the integration of AI in commercial insurance extends beyond risk evaluation—it influences policy customization, underwriting processes, and claims management. These technologies enable insurers to predict potential risks with greater accuracy and adjust policies accordingly, improving both coverage and pricing strategies. Despite the promising potential, the deployment of AI in this sector also raises important questions about data privacy and algorithmic transparency. Thus, it is imperative to address these challenges to fully realize the benefits of AI in risk profiling. Through this analysis, we aim to provide insights into the transformative potential of AI technologies for small business insurance, highlighting both opportunities and obstacles in redefining how risks are assessed and managed.

Keywords: Commercial Insurance,Artificial Intelligence (AI),Risk Profiling,Small Business Insurance,InsurTech,Machine Learning Models,Predictive Analytics,AI Risk Assessment,Underwriting Automation,Data-Driven Insurance,Digital Transformation,Insurance Innovation,AI-Powered Underwriting,Business Risk Management,Smart Insurance Solutions.

Abstract: The convergence of artificial intelligence (AI) and big data analytics stands at the forefront of revolutionizing agricultural practices, particularly through the optimization of agricultural equipment. This integration facilitates data-driven decision-making, thereby enhancing operational efficiency, reducing costs, and augmenting yield predictions. By leveraging vast amounts of data generated in the agricultural sector—from sensor data on crop health to weather patterns and soil conditions—stakeholders can employ advanced algorithms to derive actionable insights. These insights enable farmers and agricultural businesses to enhance equipment utilization, predict maintenance needs, and optimize resource allocation, ultimately translating into improved productivity and sustainability in farming. Cross-industry insights play an essential role in this optimization landscape by enabling the transfer of best practices and technologies from sectors such as manufacturing, logistics, and even finance. For example, predictive maintenance models perfected in industrial settings are being adapted to agricultural machinery, allowing for timely interventions that prevent equipment failures. Similarly, sophisticated supply chain analytics utilized in retail and e-commerce can be emulated to refine the logistics of crop distribution and resource input management. This cross-pollination of ideas emphasizes the necessity for interdisciplinary collaboration, ensuring that the agricultural sector can harness technologies that have already proven their value in distinct domains. The implications of these advancements extend beyond mere operational enhancements; they promise a transformative impact on global food security. Through precise data collection and analysis, farmers can respond more effectively to the challenges posed by climate change, fluctuating market demands, and resource constraints. By fostering a culture of innovation and adaptability, the agriculture sector can evolve into a more resilient and productive entity, capable of meeting the demands of a growing population while simultaneously safeguarding the planet's resources. In essence, the synergy between AI and big data analytics not only optimizes agricultural equipment but also paves the way for a sustainable future in agriculture, underpinned by insights gleaned from a multitude of sectors.

Keywords: AI, Big Data, Optimization, Agricultural Equipment, Precision Agriculture, Machine Learning, Predictive Analytics, Sensor Technology, IoT, Crop Monitoring, Yield Forecasting, Resource Management, Supply Chain, Automation, Data Integration, Smart Farming, Real-Time Analytics, Decision Support Systems, Cross-Industry Insights, Efficiency, Sustainability, Equipment Performance, Maintenance Prediction, Remote Sensing, Climate Data, Soil Analysis, Data-Driven Agriculture, Operational Optimization, Interdisciplinary Applications, AgriTech.

Abstract: In recent years, the emergence of agentic AI frameworks has revolutionized the landscape of customer lifecycle management within Business Support Systems. These frameworks leverage intelligent agents to automate and optimize the myriad interactions and processes that occur throughout the customer lifecycle, from acquisition to retention. This paper explores the deployment of agentic AI within BSS systems, hypothesizing that these advanced technologies can generate significant efficiencies and improved customer experiences. The complexity of customer lifecycle management necessitates sophisticated solutions capable of interpreting and acting upon diverse data points. Agentic AI frameworks meet this challenge by offering a confluence of autonomy, adaptability, and machine learning capabilities, allowing them to dynamically respond to evolving customer demands and operational conditions. The application of such frameworks extends beyond mere automation, enabling genuine intelligence-driven decision-making in customer engagement processes. Agentic AI models create a seamless integration between predictive analytics, current customer interactions, and future strategic planning. This proactive approach not only facilitates personalized customer journeys but can also identify potential churn, upsell opportunities, and optimal engagement channels. The adaptability of these systems ensures they remain relevant and effective in the face of changing market trends and customer preferences. Moreover, by leveraging AI's ability to process and analyze vast amounts of data, businesses can gain deep insights into customer behavior and preferences, informing both strategic and operational decisions. Consequently, the implementation of agentic AI in customer lifecycle management holds the potential to transform traditional BSS systems, offering enhanced service delivery, increased customer satisfaction, and ultimately, a competitive advantage in an increasingly digital marketplace.

Keywords: Agentic AI,Customer Lifecycle Management,Business Support Systems (BSS),AI Automation,Intelligent Agents,Telecom BSS,Autonomous Customer Management,AI-Driven Workflow Automation,Context-Aware Agents,Adaptive AI Frameworks,Digital Customer Experience,Predictive Customer Engagement,AI-Orchestrated BSS,Multi-Agent Systems,Self-Learning AI Models.

Abstract: Artificial Intelligence (AI) is experiencing a paradigm shift. AI began with large, computationally and energy-intensive server farms doing training and then moved to the cloud to distribute inference. But in the next generation, the compute resources needed for real-time, low-power, localized analysis and decision-making is shifting to the edge. This is enabled by a wide variety of new semiconductor technologies including domain-specific hardware accelerators, custom chips built for efficiency and novel architectures leveraging Many Integrated Core, chiplets, neural processing units, and in-memory compute architectures. The emergent capabilities make possible new services and use cases in the wireless networks, the devices in relation to the networks, and the applications that run on the devices. However, these use cases introduce exponentially higher demands for throughput, latency, training, and power efficiency compared to previous generations. To be effective, semiconductors must support energy efficient low latency inference processing, decentralization of training workloads, and proper device and network integration that recognize and have solutions for the device to data, data to model, and model to data transitions. This document presents several focus topics spanning wireless networks, chip technology for devices, systems and applications for edge AI. These components themselves are but a subset of the next generation challenges, but they provide a good roadmap to drive focus and prioritization. Thematically as AI transitions to the edge, what does that mean in terms of AI services and workloads? What technological capabilities and requirements drive progress in use cases? What devices need be created? And how do we innovate and create solutions in a cooperative way at the pace required to realize these progress and capabilities?

Keywords: Global semiconductor market, growth forecast, chip industry expansion, integrated circuits, AI chips, 5G semiconductors, automotive electronics, consumer electronics, industrial IoT, advanced packaging, silicon wafer demand, fabrication capacity, semiconductor manufacturing, chip shortage recovery, global supply chain, foundry services, semiconductor innovation, R&D investment, chip exports, emerging markets, leading semiconductor companies, technological advancement, market dynamics, semiconductor trends, microelectronics, global demand drivers.

Abstract: Healthcare supply chains have long been viewed as an overly complex, costly ecosystem. Visibility is limited due to data silos and lack of automation, and stringent regulatory certification hinders quality assurance. However, three key vulnerabilities have increasingly attracted attention: (a) dependence on single-source suppliers for critical products, (b) just-in-time inventory management without buffer stock, and (c) fragmented global logistics susceptible to border disruptions during crises. These vulnerabilities do not merely raise costs or reduce service levels; they directly affect patient safety and compromise health systems' resilience. All supply chains are vulnerable to disruption, whether through natural disasters, infrastructure failure, geopolitical tension or, as demonstrated in the COVID-19 pandemic, global health crises. The pharmaceutical and medical device supply chains are no exceptions. These sectors, hitherto operating under a veneer of relative stability, are now reassessing strategies in order to mitigate catastrophe — and attention is shifting to solutions that go beyond individual enterprise-level adaptations.

Keywords: Healthcare supply chain resilience,Medical supply shortages,Global health crisis preparedness,Pandemic-induced supply disruptions,Critical medical logistics,Pharmaceutical supply chain risk,Medical device availability,Just-in-time inventory failures,Global sourcing dependencies,Health system operational resilience,Emergency procurement strategies,Supply chain risk management in healthcare,Vaccine and PPE distribution challenges,Health infrastructure fragility, Crisis-driven supply chain adaptation.

Abstract: AI holds the potential to transform healthcare delivery by improving decision-making and operational efficiency. However, it is important to address the ethical, governance, and operational issues associated with AI-enabled applications before they can be safely and effectively deployed. AI-enabled healthcare presents unique challenges for beneficence, non-maleficence, and patient autonomy. The AI development lifecycle is often not under the control of healthcare institutions, nor are the outputs of AI systems properly understood. Consequently, the true impact of AI on patient outcomes, equity, and justice cannot be adequately evaluated. Governance frameworks play an essential role in establishing an initial level of assurance. A well-conceived but imperfectly implemented implementation governance framework can help reduce harm and increase public trust. IRBs, in combination with government-sponsored risk management and safety assurance measures, can address most of the requirements of the Safe and Effective Product Regulations. These agencies are best placed to prevent harm emanating from the use of AI-enabled interventions. The next operational development steps focus on building the evidence needed to inform and guide healthcare AI. Proactive information sharing, together with proper documentation and knowledge capes, can mitigate some of the consequences of working without feedback or clinical validation.

Keywords: Artificial intelligence, health, healthcare ethics, ethical theory, beneficence, non-maleficence, patient autonomy.

Abstract: Predictive Maintenance (PM) refers to the utilization of various forms of data for the timely anticipation of system failures. The objective is to schedule maintenance, instead of performing it according to a fixed pattern or after failure, thus maximizing uptime and resource efficiency. Modern PM systems are becoming popular within smart manufacturing as they contribute to automation and operational efficiency improvement, particularly when deployed near the equipment that generates and suffers the data. These models harness diverse forms of sensor data generated by the manufacturing process, and include techniques such as survival, reliability and hazard function estimation; time series analysis; and statistical, machine learning and deep learning methodologies. Such techniques are able to recognise and model ‘normal’ machine behaviour when sensors are not broken, and thus may fail to anticipate faults that lead to abnormal physical behaviours detectable by sensors. Such issues are of growing concern within smart factories, where maintenance modelling needs to remain accurate even when the operating environment or underlying machine behaviour changes. PM model deployment and operationalisation can thus be challenging, and requires effective instrumentation, data engineering and model management around these techniques, especially when real-time, low-latency inference at the edge is necessary. Despite the challenges, there is considerable ongoing research work applying predictive maintenance solutions in production environments. Promising demonstrations have been reported across multiple domains, including automotive, semi-conductor, mining, electronics, and food processing. Use cases span A- and B- lines of automotive assembly, anticipation of failures in cooling units of A-lines, cooling balance modelling for wafer manufacturing and plasma etching, board cleaning process deviation alerting, run-to-failure estimation in semiconductor and electronic assembly, electric drive system on-condition servicing planning, web break prediction for textile manufacturing, and applying predictive and prescriptive analytics to food processing.

Keywords: Predictive maintenance,Smart manufacturing,Industrial Internet of Things (IIoT),Machine learning models,Condition-based monitoring,Equipment failure prediction,Time-series analysis,Sensor data analytics,Remaining useful life (RUL),Anomaly detection,Digital twin technology,Edge computing,Big data analytics,Fault diagnosis,Industry 4.0.