VOLUME 11, ISSUE 8, AUGUST 2022

Abstract: Internet searching has become the latest craze among teenagers and the youth. Communication through social media is the go of the day. For the reason and no reason, we are slaves to the internet. An Online game is video game that is either partially or primarily played through the internet or any other computer network available. Online games are ubiquitous on modern gaming platforms, including PCs, Consoles and mobile device. The game we made is very interesting and interacting for the users, so the user will not be bored. This game is very user-friendly, which can be used by anyone. Our aim is to make user happy and stress- free. The languages used in this project are HTML, CSS, Javascript(JS), php, My SQL.

Abstract: Human face recognition is a field of study in Computer Vision, and is also considered a research area of Biometrics (similar to fingerprint recognition, or iris recognition). In terms of general principles, facial recognition has a great resemblance to fingerprint recognition and iris recognition, but the difference lies in the specific extraction step of each field. While fingerprint and iris recognition has reached maturity, which is widely applicable in practice, facial recognition remains challenging and remains an interesting area of research with many. people. Compared to fingerprint and iris recognition, facial recognition has a richer data source (you can see human faces in any photo or video clip related to people online) and requires less more controlled interaction (to perform fingerprint or iris recognition, human input requires cooperation in a controlled environment). Currently, the face recognition methods are divided into many directions according to different criteria: still image based FR (2D) recognition is the most popular, but the future will probably be. 3D (because the layout of many 2D cameras will give 3D data and give better and more reliable results), it can also be divided into two directions: doing with image data and doing with video data.

Keywords: Human faces, iris recognition, facial recognition, fingerprint recognition

Abstract: The Pulse Rate Monitoring system is designed using the Internet of Things with the goal of monitoring the heartbeat of the patient to screen for the risk of heart disease. Health monitoring is vital to us to ensure our health is in good condition. One of the fundamental parameters for this system viable is the pulse rate. In this paper, I describe the model of a low-cost pulse rate monitoring system from fingertips using the Bluetooth technology. The whole design is included several sections such as the Pulse Rate module, mobile application, and Bluetooth module. The Heart Rate (HR) module gets pulse signals by a painless strategy called Photoplethysmography from the patients and sends them remotely to a computer or mobile application via the Bluetooth module.

Keywords: Internet of Things (IoT), Pulse monitor, and cardiovascular disease.

Abstract: Traditional attendance systems using pen and paper are time consuming, so in modern world new attendance systems are introduced which are fast and also accurate. One of such attendance systems is using Face Recognition. Currently there are two types of Face Recognition viz Using manual method and Automatic method. We have created manual method for increasing accuracy. In this Attendance System the student will be registered first then he can use the system to mark his attendance. The whole system has been created using python only using Premade module OPEN CV which is a very powerful image processing module based on machine learning algorithm.

Keywords: Machine Learning, Face Recognition, Attendance System.

Abstract: Mechanized malware utilizes honeypot identifying instruments inside its code. When honeypot usefulness has been uncovered, malware, for example, botnets will stop the endeavoured split the difference. Ensuing malware variations utilize comparative methods to sidestep identification by known honeypots. This decreases the expected size of a caught dataset and the ensuing investigation. This paper includes many research done on honeypot with machine learning. And also include our methodology for detecting the attackers and learning the attacker’s method for intrusions through reinforming learning and capturing different data about attackers.

Keywords: Cybersecurity, Machine Learning, Python, Hacking, Cyber-Crime

Abstract: Mobile Internet is that category of service which the people can operate from their smartphone or any kind of mobile device not only to communicate with anywhere in the world, but also getting benefits in the field of education, E-commerce, social-network etcetera through internet according to their needs. Moreover, in the period of emergency time, i.e. "COVID-19" it has also seen that, the people of the world successfully communicated with the sector of medical service for their health and safety in whole 24 hours. But, without the proper utilization of bandwidth, it will be possible to ineffective for getting the people’s benefit from the Mobile Internet because firstly, bandwidth is very expensive, and secondly, without bandwidth the Mobile Internet is useless. Therefore, we have proposed a solution for a proper utilization of bandwidth for the area of Mobile Internet, which will provide the best utilization of bandwidth for the user of Mobile Internet and also influences to protect the unnecessary bandwidth and money waste.

Keywords: Bandwidth, Mobile Internet, Internet Traffic, Internet Users.

Abstract: Exam malpractice is defined as any intentional wrong doing that is contrary to the examination's standards and intended to provide a candidate an unfair advantage. Exam malpractice, commonly referred to as cheating, is the unethical behavior that students engage in during tests in an effort to improve their grades by taking shortcuts.Exam malpractice is any act or irregular way of testing applicants that violates the laws and customs governing how exams are conducted.In order to pull off the magic they are accustomed to in every exam, many students have neglected their books, which has caused a great deal of harm to the students.Examinee fraud has received a lot of attention in the Nigerian educational system and is considered as a significant problem by not just the test bodies but also school administrators, the entire educational system, the government, and society at large. Detecting impersonators in examination halls is important to provide a better way of examination handling system which can help in reducing malpractices happening in examination centers. Biometric approach could give a best strategy to decide assessment misbehavior exhaustive the utilization of impersonator. Face Recognition Technology is generally utilized in different applications and competitor can be recognized through facial highlights been removed and carried out by utilizing on calculations or the others. In order to solve this problem, an effective method is required with less manpower. With the advancement of deep learning algorithm, it is easy to solve this problem. In this project developing the framework to recognize the face and also analyze the behavior patterns of students which includes HAAR cascade and Convolutional neural network algorithm.

Keywords: Online exam, Deep learning, Convolutional neural network, Facial features, Behaviors

Abstract: The identification of plant diseases is a quite difficult process in the field of agriculture. If the identification process is incorrect, then there will be a huge loss. The identification of Leaf diseases requires knowledge about plant diseases, a big amount of research study, research work, and more processing time. Vegetable and fruit plant supports the lives of approximately 7.5 billion people worldwide and plays a crucial role in the survival of the planet. The economic development of any nation depends on agricultural productivity. The livelihood of around 58 percent of India's population depends on agriculture which is the primary income source. A plant disease is an abnormal condition that alters the appearance and performance of the plant. It is a physiological process that affects some or all plant functions. In this paper, a convolutional neural network (CNN) model is used to distinguish between healthy and diseased Citrus fruits and leaves. If the image is diseased then the proposed CNN-based model can identify the type of leaf or fruit disease. In the proposed method, we have tried to classify diseases from images of citrus fruit and leaves using the CNN model. Common citrus fruit and leaf diseases are black spot, canker, scab, greening, and melanose. The CNN Model performs better than the several traditional methods used for identifying citrus fruit and plant disease. This method is accurate and gives the results quickly. For farmers wishing to categorize citrus plant leaf or fruit diseases, the CNN Model is a helpful tool for decision-making with a test accuracy of 98.61 percent. This CNN model was checked on the Citrus dataset.

Keywords: deep learning, plant disease recognition, CNN, computer vision.

Abstract: Impact of COVID-19 was massive with job losses, shrinking of economies and loss of livelihoods. During the COVID-19 situation it was not possible for job seekers to attend walk-ins by visiting from one place to another as the transportation systems were closed due to lockdown. People had to stay indoors. To facilitate the situation lead to Online Recruitment System provides a platform where recruiters and job seekers can directly interact with each other without actually having to meet in person. This system provides security and safety to candidate’s information. The system consists of authentication and verification for candidate and company in order to prevent fraud websites and bugs. It includes updates related to latest jobs. It also includes all the details required for particular jobs. Candidates can upload their resume and set job search criteria. The jobs are bifurcated with respect to the search criteria selected by the candidate to provide the candidate with a personalized experience. Recruiters can post the particular jobs with their requirements, check out the resumes of the candidates, create their profiles, etc. It also provides recruiters with a platform where they can conduct online tests for specific requirements and can also conduct the coding test. Thus it overcomes the disadvantages of COVID-19 pandemic and also provides an easy way to the people.

Keywords: recruitment, jobs, job seekers, candidate, etc

Abstract: Image captioning, has been one of the most intriguing topics in deep learning. It incorporates the knowledge of both image processing and natural language processing. Most of the current approaches integrate the concepts of neural network. Many pre-defined convolutional neural network (CNN) models are used for extracting features of an image and bi-directional or uni-directional recurrent neural network (RNN) for sentence creation as decoder. This paper discusses about the commonly used models that are used as image encoder, such as VGG16, VGG19, Inception-V3 and InceptionResNetV2 while using the uni-directional LSTMs for the sentence generation. The comparative analysis of the result has been obtained using the BLEU score on the Flickr8k dataset.

Keywords: Image Captioning, CNN, LSTM, BLEU

Abstract: Lung Diseases is the most compelling research talking point in recent years. although a lot of research has been done on this subject still this field is arduous and confusing and There are numerous techniques to classify medical images. Deep learning techniques have achieved an magnificent result in the field of Medical Engineering and computer vision. One of the current disadvantages of pneumonia detection is it requires high- elucidate data sets. Convolution Neural Network requires lots of training data and not equipped to recognize pose and distortion of object, Due to these reasons Capsule Network is introduced. After reviewing the topic, this paper presents the advantages of capsule network over convolutional neural network and architecture of capsule network.

Keywords: Lung Diseases Detection, Chest X-ray images,CNN,Capsule Network

Abstract: 65% of the drinking water in our country comes from rivers and streams. Every year, millions of tonnes of trash find their way into the rivers and streams of our country. It can poison drinking water and endanger the lives of everyone who depends on it, making it more than simply an eyesore.In this paper we have discussed various methods of river cleaning to minimise water pollutants. We can effectively remove great quantities of physical waster through an unmanned river cleaning bot. Several effluents can be removed from river water is by the application of moving bed biofilm reactor and integrated fixed activated sludge. Another method is phytoremediation of contaminated waters using aquatic plants and artificial aeration. Due to its environmentally favourable characteristics, bioremediation (the use of microorganisms to clean up contaminated areas) has proven practical and trustworthy. Several variables, including but not limited to cost, site features, type, and concentration of contaminants, might determine whether bioremediation is done in situ or ex situ. The aim of this paper is to analyse and discuss the most efficient and effective methods of river water cleaning and decontamination.

Keywords: River Water Cleaning, Bioremediation, Phytoremediation, Unmanned River Cleaning Bot, Moving Bed Biofilm Reactor, Integrated Fixed Activated Sludge

Abstract: Professional psychologists need to be knowledgeable about how to safeguard children from sex predators and the risks associated with internet sex abuse. Although the internet has many positive aspects as well, one of its most negative characteristics is the possibility of sexual postulation. Online, sexual predators have easy access to many children while going mostly unrecognized. The major goal of our project is to identify child predators through comments and postings on social media so that the administrator of the cyber jail is aware of the predator's past. According to a recent national poll, one in five young adults engage in annual online sex activities (finkelhor, mitchell, & wolak, 2000; mitchell, finkelhor, & wolak, 2001). This project report details the most current changes we made to the system to make it function. Any accounts discovered to be child predators will be reported to the admin for further action in accordance with the established policy.

Abstract: Energy harvesting; that is, harvesting small amounts of energy from environmental sources such as solar, air flow or vibrations using small-scale (≈1cm3) devices, offers the prospect of powering portable electronic devices. Numerous studies have shown that power densities of energy harvesting devices can be hundreds of μW; however, the literature also reveals that power requirements of many electronic devices are in the mW range. In this research paper simulation of MEMS based energy harvesting and effect of piezoelectric thinfilm in carried to evaluate the efficiency and power density of the device.

Keywords: MEMS, piezoelectric, thickness, thinfilm.

Abstract: The massive expansion of online digital information has increased the demand for storage solutions. The Total Cost of Ownership, which includes storage infrastructure costs, management expenses, and human administration costs, grows in tandem with the volume of data. Reducing the quantity of data that has to be transported, stored, and maintained becomes critical in large-scale distributed archival storage systems, and it also helps application performance, storage costs, and administrative overheads. De-duplication is a storage-saving method that has shown to be extremely effective in business backup setups. A same data block in a file system may be saved numerous times across different files; for example, several copies of a file that are substantially similar may exist. It localizes data replication and eliminates redundancy; by storing data just once, all files that employ identical areas refer to the same unique data. In this project we extend a existing cloud of clouds service to make the process of routing and storing of data chunks on a cloud network more efficiently.

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. In this paper we have designed the hybrid approach of combination of credit based task length & priority algorithm and credit based deadline algorithm as well as compare the results with FCFS, SJF and task length & priority scheduling algorithms. When we use the credit based task length & priority scheduling algorithm to schedule the task without knowing the deadline of the task, it will cause the dead of the least deadline task. The deadline credit is also included so that assigning number of resources to the tasks in such a way that there will be maximum resource utilization and minimum processing time achieved. This paper presents the simulation results of the proposed methodology implemented with the help of Cloudsim and Net beansIDE8.0 and analysis of results. Keyword: Task length & Priority, Hybrid TLPD, FCFS, SJF, Cloudsim

Abstract: The text in a patent is so rich that it has the capacity to contribute towards creativity and ingenuity for the engineers, policy makers and scientist of those countries which are straggling in the technology [5]. As per the reports of World Intellectual Property Organizations, patent documents contain around 95% of the inventions hence proving to be a vital source for technological fruition and development with the passage of time [6]. The patent text data is broadly classified into two domains as the patent text and metadata. Where, the patent text data encompasses title, abstract, claims, narrative and explanation and the background, while the metadata is associated with information like inventor, issue date, examiner and the one who applied for patent. A patent landscape is an analysis of patent data that reveals business, scientific and technological trends. Landscape reports typically focus on a single industry, technology or geographic region.

Abstract: CDNs on clouds normally communicate with authenticated subscribers using HTTPS to provide privacy and data integrity. The SSL private key is the most critical component in secure communication, and it can be even more important than the protected content itself. The key challenges are a) how to provide security guarantees so that the SSL private key and the content can be stored onto untrusted public clouds and b) how to allow CDN nodes to provide autonomous and effective data transfer over HTTPS encrypted connections, with possible SSL acceleration for better performance. To solve the issues, Effective Hierarchical Key Management System caches both the data and the SSL private key onto the cloud-based CDN nodes using a hierarchical key distribution scheme and ECC algorithm that leverages the cloud distributed infrastructure with trustful fidelity and hardware assistance. The proposed method consists of a Key Distribution Center (KDC), large-range distributed Key Sub- Centers (KSCs) and Backend Caching Services, such as web content caching or in-memory data caching and also the session key establishment center. The key challenge is how to avoid the additional communication between the CDN node and the key server. A good solution is to cache the private keys in CDN nodes to comply with the elasticity principle, and at the same time, guarantee the security of the cached keys on clouds.

Keywords: Public Key Infrastructure

Abstract: In the Existing System, In order to slow down the spread of the disease, known as COVID-19, and reduce the stress on healthcare structures and intensive care units, many governments have taken drastic and unprecedented measures, such as closure of schools, shops and entire industries, and enforced drastic social distancing regulations, including local and national lockdowns. To effectively address such pandemics in a systematic and informed manner in the future, it is of fundamental importance to develop mathematical models and algorithms to predict the evolution of the spread of the disease to support policy and decision making at the governmental level. It is urgent to conduct prediction research on the development and spread of the epidemic. In this project, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction.

Keywords: component, COVID, infectious, prediction, regression

Abstract: The key feature of cloud computing is one can access information any place, anywhere, at any time. So basically, cloud computing is subscription-based service where one can obtain network storage space and computer resources for data storage as well as data sharing. Due to high fame of cloud for data storage and sharing, large number of participants gets attracted to it but it leads to issue related to efficiency, Data integrity, privacy and authentication. To overcome these issues, concept of ring signature has been introduced for data sharing amongst large number of users. Ring signatures are used to provide user’s anonymity and signer’s privacy. It allows a data owner to anonymously authenticate the data which can be stored into the cloud or analysis purpose. Yet the most cost consuming certificate verification for public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Session based ID with ring mechanism helps to implement session based key and access files within a session. Use of ID-based ring signature, removes the need of certificate verification which was done using public key infrastructure, hence reduce cost as well as introduction of forward security, further strengthen this system more. Use of weil pairing, keeps even shorter keys secure and it also requires less processing power. So the motivation of this paper is to propose a secure data reading and sharing scheme using above mentioned scheme.

Keywords: Public Key Infrastructure

Abstract: In the Existing System, Relating to Traditional traceability system(TTS) which has certain problems of centralized management, not able to be seen through and difficult or Impossible to understand information, unable to be trust the data, and easy generation of fact or knowledge that are provided or learned information islands. So, To solve the above given TTS problems, In this paper we have designed and developed a traceability system which is based on the blockchain technology for the purpose of storage and query of product information in the supplychain of agricultural products. The power of influence of the characteristics of blockchain are used here because it is decentralization, Interfere with something without permission or so as to cause damage(tamper-proof) and traceability of blockchain technology, the transparency and Able to be believed, convincing of traceability information are increased. Here, We have used a dual storage structure of ‘‘database + blockchain’’ , on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. The proposed algorithm is Raft Algorithm, which used to generate the digital signature, this signature is used in the blockchain generation process. Blockchain technology combined with Raft algorithm is proposed to realize the safe sharing of private information in the blockchain network. Here, We know that the Blockchain technology which is combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we have design a Reputation-based smart contract to encourage network nodes to upload traceability data.

Keywords: Blockchain, component, formatting, style, styling, insert

Abstract: Predictive modelling is an important part of learning analytics, whose main objective is toestimate student success, in terms of performance, knowledge, score or grade. The data used for thepredictive model can be either state-based data (e.g., demographics, psychological traits, past performance)or event-driven data (i.e., based on student activity). The latter can be derived from students' interactionswith educational systems and resources; learning management systems are a widely analysed data source,while social media-based learning environments are scarcely explored.Data is collected from a Web ApplicationsDesign course, in which students use wiki, blog and microblogging tools, for communication andcollaboration activities in a project-based learning scenario.In addition to the novel settings and performance indicators, an innovative regression algorithm is used for grade prediction. Very good correlation coefficients are obtained and 85% of predictions are within one point of the actual grade, outperforming classic regression algorithms.

Keywords: component, formatting, style, styling, insert (key words)

Abstract: Blockchain is one of the most advanced technologies that play an important role in many different fields such as healthcare, capital markets and logistics. Among the many existing blockchain platforms, the integration of the Turing-complete virtual programming engine with the blockchain makes the Ethereum blockchain one of the most paramount infrastructures for various types of applications, including but not limited to cryptocurrency trading, smart contracts, decentralised finance and metaverse. Nevertheless, Ethereum like many other computing systems, has fallen victim to vector attacks that exploit its vulnerabilities and have catastrophic consequences. Out of the need to protect Ethereum from such attacks, this paper proposes a novel deep learning model based on convolutional neural networks. The proposed model treats the transaction, which is the atomic entity in this platform, as a stochastic time series and then develops two specific task layers that are compatible with the traditional CNN architecture. The first layer is responsible for detecting the seasonal characteristics of the transactions, while the second layer is used for detecting the trend. These two layers are integrated with the traditional architecture to form a powerful temporal CNN architecture that can classify different types of attacks. The performance of the proposed model was evaluated from a different perspective using real transactions collected from the Ethereum main-net network. The results of the comprehensive evaluations show the ability of the proposed model to perfectly identify malicious transactions in the Ethereum blockchain.

Keywords: Ethereum blockchain, sandwich attack, front-running, block stuffing, temporal CNN.

Abstract: This paper presents the prediction of Rhabdomyosarcoma (RMS) using Transfer Learning model VGG19. While many machine learning techniques have been created to recognise the most common tumour types in histology images, considerably less is understood about the automatic categorization of tumour subtypes. The most prevalent soft tissue cancer in children, rhabdomyosarcoma (RMS), contains a number of subtypes, the most prevalent of which are embryonal, alveolar, and spindle cell. It is important to assign RMS to the appropriate subtype since different subtypes have been shown to react to various treatment modalities. Rhabdomyosarcoma is a common paediatric cancer of malicious soft-tissue tumour that affect 40% to 50% children more than adults. Due of the subtle differences in appearance of histopathology images, manual categorization needs a high level of knowledge and takes time. While many machine learning techniques have been created to recognise the most common tumour types in histology images, considerably less is understood about the automatic categorization of tumour subtypes. The most common sites of RMS tumor are head, neck, genitourinary tract, and extremities. The RMS prediction achieves 97.6% of accuracy for radiology images and 88.4% of pathology images byVGG19 model. Keyword: Transfer Learning, Machine Learning, Deep Learning, VGG 19

Abstract: Bug reports are an integral part of the software development process. They are used by software developers to improve the quality of the software. Bug triaging deals with the selection of a suitable developer to resolve the bug. With the increase in the number of bugs, this process only becomes troublesome and laborious. If the bug is assigned to a developer who is not able to resolve the bug, it is reassigned to another developer. This bug tossing leads to delays in resolving the bug and a lot of wasted resources. The selected bugs are then prioritized based on their severity and fixed according to the priority. If the severity is incorrectly reported, it results in a waste of time and resources. In this paper, we present how classical machine learning classification algorithms can be used in bug tracking systems during the process of bug reporting to suggest the severity of the bug and developers(assignee) using NLP (Natural Language Processing) techniques on the summary of the bug report. The predictions from these classification algorithms are then incorporated in the bug report filing and assignment phase of the bug life cycle. We have collected bug reports from Bugzilla for two open-source projects: Eclipse and LibreOffice and compared the results of various classification algorithms. Even though fully automated assignment is not present, the prediction accuracies are high enough to be used as suggestions to the reporter/assigner in our bug tracking system.

Keywords: Severity Prediction, Bug Triaging, Project management, text-based classification, NLP, TF-IDF.

Abstract: The use of online entertainment has increased significantly with time thanks to the growth of the Internet and has now overtaken all other form of organising in the twenty-first century as the most significant. In spite of this, the increased The impacts of social accessibility are typically unpleasant. such as online abuse, acts of cyberbullying, cybercrime, and web-based savaging, which combine two or three terrible aspects of society. Particularly among women and children, cyberbullying regularly results in real emotional and physical pain and may even drive some victims to make suicide attempts. Due to its profoundly harmful social effects, online badgering stands out. Online badgering has recently led to numerous occurrences, such as the dissemination of sexual remarks, rumours, and private conversations. Because of this, analysts are paying increasingly more attention to the detection of harassing texts or messages from web-based entertainment. The goal of this work is to combine artificial intelligence and natural language processing to create and use a viable system for identifying harassing and damaging online messages. The goal of this work is to combine artificial intelligence and natural language processing to create and apply a viable approach for recognising offensive and harassing internet comments. File Terms: Cyberbullying, Natural Language Processing, Machine Learning, and SocialMedia

Abstract: Right now, shop run-centres, Big Marts track each and every thing's business information to expect con-ceivable purchaser interest and update stock administration. The data stockroom's data stockpiling is regularly dug for irregularities and general examples. For stores, for example, Big Mart, the going with facts can be used to check impending arrangements limits using AI methodology like gigantic shop. For expecting the game plans of a firm, for instance, Big - Mart, a judicious model was made using XG Boost, Linear break faith, Polynomial fall away from the faith, and Ridge break faith approaches, and it was found that the model outmanoeuvres present representations.

Keywords: Straight Regression, Polynomial Regression, Ridge Regression, and XG boost Regression are instances of expressions.

Abstract: Humanity's presence has been aided by innovation in terms of personal happiness. We are always striving to create something new and unique. With the aid of technology, we've come a long way in the financial sector, the up-and-comer receives confirmations orreinforcement prior to endorsement of the credit sum. The framework's decision to support or reject an application is based on the verified information provided by the up-and-comer. There are always a large number of people seeking for credit in the financial sector, but the bank's reserves are limited. Using a few classes-work calculations, the proper expectation would be quite beneficial in this circumstance. A relapsing model, an arbitrary timberland classifier, a support vector machine classifier, and so on. The success or failure of a bank is determined by the amount of credits, or whether the client or client is returning the advance. Credit recovery is the most important aspect of the financial sector. In the financial sector, the improvement cycle plays a key role. Using credible data from up-and-comers, an AI model based on distinct order computations was created. The main goal of this work is to predict whether another candidate will allow the advancement by using AI models based on the real informational index.

Keywords: Data, Loan, Machine learning, Training, Test, Predicting

Abstract: Because of advancements in current innovation and social correspondence, publicising new job openings has recently become an exceptionally common issue in today's world. As a result, everyone will be concerned about the fake job posting expectation task. As with other grouping endeavours, counterfeit work presenting forecast brings with it a slew of difficulties. This paper proposed using various information mining methods and characterization calculations, for example, Support vector machine, KNN, decision tree innocent the Probability classification algorithm, irregular timberland classification model, multi-facet the perceptron, profound brain organisation, to foresee whether the task determine whether a post is real or fake. We examined the Employment Scam Aegean Dataset (EMSCAD), that includes 18000 examples. As a classifier, the profound brain network excels at this characterization task. For this powerful brain network classifier, we used three thick layers. The prepared classifier predicts a deceptive work post with 98 percent order exactness (DNN). Record Keyword: bogus work expectation, profound learning, information mining

Abstract: Most fake review detection techniques begin with text-based features and behavioural capabilities. They’re, however, time-ingesting and have difficulty detecting by using fake customers. The good- sized most of modern mind community-primarily based strategies address the issues raised by means of the puzzling semantics of audits, they do now not constitute positive styles amongst customers, critiques. They do now not don't forget the use cases of data with regard to exceptionally grainy angles when detecting fake surveys. In this paper, we advocate a fully distributed intuitive brain network model based on view imperatives, which is based on a fully distributed intuitive brain network model that uses a distributed and verifiable audit articulation technique and coordinates 4 components to display survey, especially customers, survey reports, objects and first-class-grained perspectives. We version the bindings between the buyer and the object, and use these bindings as time periods for regulation to re- write the version objective. Three widely available experimental data reveal that our recommended model exceeds modern-day techniques, showcasing its feasibility & flexibility. Fraudulent audits, complicated depictions, dating presenting, excellent-grained angles are all terms at the listing.

Abstract: “Weather Forecast Application” which is a web based application using Rest API developed to predict the Weather conditions for a particular location by taking user input. As there are very few weather forecasting models and methods for weather prediction and uses different API providers that is where the question of which could be the best Rest API Provider to provide highly accurate results arises. There are few application which are in existence for forecasting Weather conditions which provides the weather data fetching only the user input which is not that accurate in fetching the weather data and visualization of the forecast. This concept of visualization is where few of the application fails to meet the users satisfaction and reach to there understanding and few application are endorsed with many features which makes the user feel complicated in understanding the weather data. This is a web-based application with features which is added for managing and handling errors which is done by the system which makes the system bug free. Option of searching different locations weather condition as per the user’s interest. It takes the users input a fetches the weather data and visualizes it to the user. Henceforth, an application is developed Using OpenWeatherMap Rest API works based on user input and visualizes the weather conditions, temperature, humidity, wind speed, sunrise and sunset, date and time of each day. Weather forecasting application is developed to be a user friendly tool which is simple to use and handy to get information on particular geographical location.

Keywords: Weather Forecast, humidity Weather Conditions, OpenWeatherMap, temperature, wind speed, date and time.

Abstract: The use of Unmanned Aerial Vehicles (UAVs) has brought drastic security issues in areas considered sensitive like defense zones, industrial installations, and restricted locations. This paper is a double-layered intelligent surveillance system with AI-based drone detection coupled with IoT – enabled ground object monitoring. The air detection module uses a Convolutional Neural Network (CNN) to analyze live video streams and detect unauthorized flying objects like drones and the ground detection module uses a Node MCU- driven ultrasonic sensor with a servo motor for 180° scanning. Both modules use Fire based cloud services to synchronized at a in real-time and send instant mobile notifications to authorized personnel. The system provides end-to-end aerial and ground-level monitoring, providing scalability, cost-effectiveness, and quick response, and can thus be deployed to high- security contexts including borders, airports, and military bases.

Keywords: Drone images, Node MCU, NN, Ultrasonic sensor, Drone Detection / IoT Surveillance YOLOv3, Real-Time Monitoring, Intrusion Detection, Surveillance System, Video Frame Analysis, Sensor Fusion, Node MCU, Firebase, Inertial Sensors, Object Recognition, Obstacle Avoidance, Cloud Alerting.