VOLUME 11, ISSUE 9, SEPTEMBER 2022

Abstract: Electioneering processes can be much more convenient by the introduction of Electronic Voting Systems (EVS). However, there are some lacunas associated with EVS, for instance, if a blank vote is signed into the server before voters cast their votes, it results into multi-voting. In addition to that, if users cast their votes before signing into the server, the information of the election could have leaked out from the server and the election would have been compromised. So, we introduced Blind signatures to help prevent leakage of the election information through the server. However, hackers may try to bring in a non-candidate signature for non-legal usage at that moment or in a later time. For these issues to be addressed, this research suggests a novel work, oblivious signature scheme with proxy signature to meet security requirements that includes; message verification, information protection, and personal privacy, in order to ensure that rigging is drastically reduced between voters, candidates and the system (the server). That is why, an EVS that uses a combination of blind signature, oblivious signature and proxy signature scheme is proposed and we have also implemented this scheme electronically to show that users can vote conveniently and securely.

Keywords: Blind signature, Oblivious signature, Proxy signature, Electronic Voting System (EVS), Privacy and Security.

Abstract: The light obtained from the Sun is a renewable source of energy which is free from environmental pollution and noise. It can easily replace the energy drawn from the non-renewable sources of energy such as fossil fuels and petroleum deposits inside the earth. In this paper a hybrid model of concentrated photovoltaic (CPV) technology based solar panel is proposed. This hybrid model is integrated with automatic dual axis solar tracking system, nano-fluid cooling and automatic dust cleaning system for improving its efficiency. Efficiency of solar panel can be improved by using solar tracker with CPV panel which continuously tracks sunlight throughout the day to get maximum solar energy. Second method to improve the efficiency is dust cleaning. Dust is barrier between sunlight and solar panel. Third method is cooling technique. As panel temperature increases output voltage of solar panel decreases so cooling of panel is necessary for improvement of efficiency.

Keywords: Concentrated photovoltaic (CPV) technology, automatic solar tracking system, nano-fluid cooling and automatic dust cleaning system.

Abstract: Steganography is the practise of concealing a secret message within another, more mundane message. Messages can take the form of images, text, video, audio, and so on. The goal of modern steganography is to covertly communicate a digital message. Various transporter record designs are in many cases utilized, yet computerized pictures are the chief well known because of their recurrence on the web. For concealing privileged data in video outlines, there exist an outsized kind of steganography procedures some are more perplexing than others and all of them have serious areas of strength for separate flimsy spots. For hiding secret information in video frames, there exist an outsized sort of steganography techniques some are more complex than others and every one of them have respective strong and weak points. The critical extent of this work is about high-limit visual steganography procedures that conceal a regular variety video inside another. The author experimentally approves that high-limit picture steganography model doesn't normally reach out to the video case for it totally disregards the fleeting overt repetitiveness inside successive video outlines. Our work proposes a clever answer for this issue (i.e., concealing a video into another video). The specialized commitments are two-crease: first, propelled by the way that the lingering between two back-to-back outlines is exceptionally inadequate, author propose to expressly consider between outline residuals. In particular, our model contains two branches, one of which is uniquely intended for stowing away between outline lingering into a cover video outline and different conceals the first mystery outline. And afterward two decoders are conceived, uncovering remaining or outline individually. Besides, the author fosters the model in light of profound convolutional brain organizations, which is the first of its sort in the writing of video steganography. In tests, exhaustive assessments are directed to contrast our model and exemplary steganography techniques and unadulterated high-limit picture steganography models. All results unequivocally recommend that the proposed model appreciates benefits over past techniques. The author likewise cautiously explores our model's security to steganalyzer and the strength to video pressure. A convolutional brain network for concealing recordings inside different recordings. It is executed in keras/tensorflow utilizing the ideas of profound learning, steganography and encryption.

Keywords: Steganography, deep learning, vstegnet, deep neural networks (DNNS), deep 3D CNN.

Abstract: There are so many investment options out there, but still ETF (Exchange Traded Funds) are mostly an unknown name to new investors. Here, we have picked three top ranked Gold ETF from BSE (Bombay Stock Exchange) and we have shown their trends from their introduction moment in the market divided by three timelines

Abstract: Food Recognition is a computer vision application that has gained huge research interest. Food recognition and classification is a major task in managing health conditions and most importantly in assisting Type 1 diabetic patients in taking decisions on the right food to eat that will not worsen their health conditions. Diabetes has become a global health challenge threatening the well-being of millions of people across the world. Food recognition systems aid diabetic patients in monitoring what they eat, managing their chronic health conditions, and improving their quality of life. This paper presents an extensive review of food recognition and classification methods to aid diabetic patients and the food geographical regions of available datasets already studied. The review explores existing mobile and Desktop food recognition systems and diabetes self-care management applications. The analysis presented in this paper gives the following new insights: the most performing food recognition Methodologies that have been developed; the existing food datasets and the unexplored research areas. The findings in the literature reviewed show that Convolutional Neural Network (CNN) recognition techniques are widely applied in food recognition and classification systems compared to the Bag of Features (BoF) method. Also, the main challenge in this review is the functionalities of the available diabetes applications in the market and it was discovered that none offers recognition of Nigeria Foods to aid diabetes patients.

Keywords: Diabetes, food recognition, food classification, glycemic index, sugar level, deep learning, convolutional Neural Network

Abstract: In present work, a comparative analysis of conventional 6T, 7T and 8T SRAM cells has been performed using 180nm process design kit using Cadence Virtuoso. As the CMOS technology getting shifted in nanometer regime it faces a lot of challenges such as short channel effect and process variations. The SRAM parameters such as stability, power dissipation and delay of these considered cells have been investigated. It has been observed that write delay in 6T cell is improved by 2× and 0.14× as compared to 7T and 8T SRAM cells respectively. Furthermore, average read and writes power consumption of 7T SRAM cell found to be 1.2× and 0.98× less as compared to 6T and 8T SRAM cells respectively. Additionally, the 8T cell is having moderate write SNM i. e 32% more than 6T cell but 9% less than 7T cell.

Keywords: SRAM, delay, power dissipation, static noise margin, T (transistor).

Abstract: The social media platform has served as an entry point for establishing connections, content sharing and social interactions for many users. Exploiting customer's information is very prevalent nowadays by gaining insight into user's habits, preferences, connections, behaviours, content and location. NFTs are tokens that we can use to represent ownership of unique items. They let us tokenize things like art, collectibles, even real estate. They can only have one official owner at a time and they're secured by the Ethereum blockchain – no one can modify the record of ownership or copy/paste a new NFT into existence. A problem occurs when metadata is exchanged along with messages, thereby providing an opportunity for third parties to steal the user's personal details. Blockchain‐based social media provide more benefits than just security and privacy: Cryptocurrencies are used for secure communication by paying and buying content in peer‐to‐peer marketing. Blockchain assists in gaining control over the user’s own content. Unlike cryptocurrencies, they cannot be traded or exchanged at equivalency. This differs from fungible tokens like cryptocurrencies, which are identical to each other and, therefore, can serve as a medium for commercial transactions. NFTs are unique cryptographic tokens that exist on a blockchain and cannot be replicated. NFTs can represent real-world items like artwork and real estate. "Tokenizing" these real-world tangible assets makes buying, selling, and trading them more efficient while reducing the probability of fraud. NFTs can also function to represent individuals' identities, property rights, and more.

Abstract: Full adder is a digital logic circuit that implements addition of binary numbers. The circuit of full adder forms a basic component of ALU (Arithmetic Logic Unit) in Microcontrollers and Microprocessors. They are used in concepts like Fast Fourier Transform in DSPs. In order to generate memory addresses inside a computer and to make the Program Counter point to next instruction, the ALU makes use of full adders. Full adders are a part of Graphics Processing Unit for graphics related applications. The objective of this project is to analyze the 16T low power CPTL full adder and optimize its power dissipation and propagation delay by simulating the circuit in 45 nm and 32 nm processes. By implementing designs across successive process generations, 2-dimensional design space is explored for low power and high performance. The performance of full adder is validated across a range of frequencies upto 1.25 GHz. The SPICE based simulations are carried out using LTspice

Keywords: VLSI, CMOS, CPTL,Full adder,ALU, Power, Delay.

Abstract: Recently, with the rapid growth of the demands in wireless communication products such as mobile phones and wireless network, low cost and high performance onchip radio-frequency devices are strongly needed. One important limitation in achieving higher levels of integration and further reduction of fabrication costs in the front-end of microwave transceivers is set by the difficulty of achieving high-Q on-chip inductors with smaller size. In the research work spiral inductors are simulated while achieving appreciable inductance and terminal voltage.

Keywords: On chip, silicon, FEM, inductor, spirals, terminal voltage.

Keywords: Calculus, E-learning, Covid19, Banditry, YouTube, SoundCloud.

Abstract: Heart attack is one of the most pressing problems in health care. Heart-related or cardiovascular diseases are the leading cause of many deaths in the world over the past few decades and have emerged as the most life-threatening disease. We need a reliable, accurate, and feasible system for urgently diagnosing such diseases for proper treatment. Nowadays, machine learning is known to play a huge role in the medical industry and the application of machine learning algorithms and techniques on various medical data sets to automate the analysis of large and complex data using various machine learning models for disease diagnosis, classification, or prediction. Results. Several researchers are recently using various machine learning techniques to help the healthcare industry and professionals diagnose heart-related diseases. This research provides an improvement on the factors and triggers that may lead to a heart attack. This research focuses on developing a simplified framework that combines several machine learning techniques such as Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbor, Decision tree, and Random Forest to help predict early heart attacks for different age groups using patient data. Both quantitative and qualitative approaches are used, which helped to analyze and evaluate data specifically collected from the Saudi community to conduct this research. The results indicated that the proposed developed framework outperformed the model in the initial stage as it gave SVM greater accuracy in less time to predict with an accuracy of 85.99%. Finally, the framework is evaluated using evaluation criteria, in addition to comparing the work with the previous work.

Keywords: Classification Algorithms, Accuracy, Heart Attack, Machine Learning, Cardiovascular Disease.

Abstract: Artificial intelligence is opening up new avenues for value generation in enterprises, industries, communities, and society as a whole. Technology has been researched to be relevant in many aspects of the world. This factor has made it to be included mainly in different businesses and industries. The applications of AI are endless to discuss. The research below examines the applications of artificial intelligence (AI) in cybersecurity. Cybersecurity has also been a growing concept in the technological industry. Many companies have included information technology in their businesses. This factor has required companies and organizations to demand more security measures. The attempt to protect the available data and information has resulted in the growth of cybersecurity, and AI has been seen to influence cybersecurity heavily on a large scale. This factor has made machine learning to be significantly induced in recent technologies supporting cybersecurity. The research paper performs a literature review and examines the overall impacts of artificial intelligence on cybersecurity.

Keywords: Cybersecurity, AI, Cyber threats, Vulnerability, Data Privacy, AI value creation.

Abstract: Cloud computing is a distributed environment that encompasses thousands of computers that work in parallel to perform a task in lesser time than the traditional computing models. Recently there are many emerging cloud platforms to choose for run, deploy and maintain applications, offering a variety of services and tools at the disposal of a user, Clouds bring out a wide range of benefits including configurable computing resources, economic savings, and service flexibility. Cloud users are faced with the dilemma of selecting a suitable platform that meets their specifications. The aim of this paper is to compare three most widely adopted cloud platforms, Amazon Web Services, Microsoft windows azure and Google app engine based on some commonly shared features such as security, storage, Artificial intelligent and Networking to guide customers in selecting a suitable cloud platform. A cloud computing based services also face such kinds of security issues where applications deployed on cloud can face same kind of attacks as that on client-server model. The research method use for this paper was comparative method, primary and secondary data were use as instrument for collecting data. The result of the comparison concluded that AWS fits the needs of large companies due to their vast global reach. This paper is beneficial for potential users such as small mid-size enterprises, start-up developers and large companies for selecting a cloud platform that meets their requirements.

Keywords: Data Security, Artificial intelligence, Storage, Networking

Abstract: Diabetic retinopathy, which results from persistently high blood sugar caused by Diabetes Mellitus or simply called Diabetes, is linked to harm the microscopic blood vessels in the retina. In order to send signals to the brain via the optic nerve, the retina must first detect light. Vision distortion can result from diabetic retinopathy, which can cause blood vessels in the retina to leak fluid or haemorrhage (bleed). In its most severe form, aberrant blood vessels proliferate (grow in quantity) on the retina's surface, which may cause scarring and retinal cell loss. Due to a complex grading system and the requirement for trained doctors/ Optometrists to recognise the existence and relevance of numerous tiny characteristics, diagnosing Diabetic Retinopathy (DR) with colour fundus pictures is a challenging and time-consuming task. In this article, we suggest a CNN method for correctly identifying DR from digital fundus images and classifying its severity. We create a network with CNN architecture and data augmentation that can recognise the complex elements needed for the classification task, like micro-aneurysms, exudate, and haemorrhage on the retina, and then deliver a diagnosis automatically and without user input. On the image set as acquired in our previous papers [1] on Diabetic retinopathy, we train this network using a top-tier graphics processing unit (GPU), and the results are excellent, especially for a challenging classification test. Treatment for diabetic retinopathy is often delayed until it starts to progress to Proliferate DR/ PDR. Comprehensive dilated eye exams are needed more frequently as diabetic retinopathy becomes more severe. People with severe non-proliferative diabetic retinopathy have a high risk of developing PDR and may need a comprehensive dilated eye exam as often as every 2 to 4 months. So, in our paper we have developed such a model where even a thin line of difference between each stages of DR is well distinguished by our model “retina.model” and is 100% reusable with increasing level of cognition with time as the machine tries to learn new patterns.

Keywords: persistently high blood sugar, matrix handling, Diabetes Mellitus, American Optometric Association (AOA), Deep Learning, CNN architecture, Diabetic Retinopathy (DR), Image Classification, retina of the eye, Optometrist, Gaussian filters, Mild DR, Moderate DR, Severe DR, Proliferate DR/ PDR and NO DR.

Abstract: Most countries face high and increasing rates of heart disease or cardiovascular disease. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve the challenges that face a successful interpretation of echocardiography examination results. To design a predictive model for heart disease detection using data mining techniques from Transthoracic Echocardiography Report dataset that is capable of enhancing the reliability of heart disease diagnosis using echocardiography. Knowledge Discovery in Database (KDD) methodology consisting of nine iterative and interactive steps was adopted to extract significant patterns from a dataset containing 7,339 echocardiography examination reports of patients. The data used for this study was collected by Hospital. The findings of this study revealed all the models built from J48 Decision Tree classifier, Naïve Bayes classifier and Neural Network have high classification accuracy and are generally comparable in predicting heart disease cases. However, comparison that is based on True Positive Rate suggests that the J48 model performs slightly better in predicting heart disease with classification accuracy of 95.56%. This study showed that data mining techniques can be used efficiently to model and predict heart disease cases. The outcome of this study can be used as an assistant tool by cardiologists to help them to make more consistent diagnosis of heart disease.

Keywords: KDD, Data Mining, Decision Tree, Neural Network, Bayesian classifier, Heart Disease.

Abstract: The main aim of implementing this project is to diminish the loss of customers to competitor. The current system is manual & time consuming due to the paper work. It is very costly process and it has low average return. At present, users can walk-in or make a call to book or hire a cab. The employees of the organization will have to check the records in order to see the availability of the vehicle for renting. In the existing system there is a possibility of errors and loss of data. The aim of this project is to book a cab online so that customers do not require to make a call or walk-in to book a cab. They can book a cab as per their requirement and select a cab from the listed available cabs. It also keeps record of all available and reserved cabs. Reports will be generated.

Keywords: Google Map, Cab Booking, Android App, Rest API

Abstract: In this paper, it’s all about Content Management System (CMS). CMS is used to organize, add, delete, and update information data on the web. CMS allows users (authors) to deliver new content in the form of articles as in Post and Blogs. Articles are usually a combination of plain text, images and videos etc perhaps with markup to indicate where other elements (such as Links and tags) should be placed without using a programming language. The main application of the CMS is to manage content throughout its life cycle, that is to say from its creation to its publication. The web publishing system helps establish a consistent look across your entire site, but gives non-technical content authors the power to publish and update their content using simple, yet simple browser tools.

Keywords: CMS - Content Management System , Write, Publish, React, PostgreSql. APIs- Application Programming Interface

Abstract: This paper aims to develop a model and an algorithm to design an application to save the details of the patient's medical reports in an organized manner. This desktop application is developed in Java Swing and MySQL is used for the backend. Medical (Lab) report digitization helps organize the reports given by doctors and helps determine the summary of lab reports through user-interactive graphs. It is the easiest means of handling medical documents and computerizing all the lab report details regarding patients' medical history in this digital era .

Keywords: Reports, Graphs, Java Swing, MySQL, JFreeCharts, GUI, Medical.

Abstract: Since the World Health Organization announced the Covid-19 pandemic, many countries have made strict decisions to prevent the spread of the Covid-19 epidemic, so they closed borders, prevented travel, prevented roaming within cities, and transition education from physical education to distance education to achieve physical distance as the most important way to prevent the spread of the epidemic. Saudi Arabia is one of the first countries to transfer physical education to distance education, as the transition was rapid. This research aims to predict the impact of COVID-19 on the distance education of King Abdulaziz University students through a comprehensive framework using a machine learning approach. Machine learning algorithms KNN, Decision Tree, R-Forst, XGBOOST and SVM were used. Based on the factors and challenges that have created an impact on students from multiple aspects, namely psychological, social, educational and health aspects during Distance Education in this emergency situation. The factors were extracted through a questionnaire directed to King Abdulaziz University students. The data collected from the questionnaire were analyzed using SPSS, and then the results of the models used for Prediction were evaluated using multiple metrics, namely: accuracy, precision, recall, F1-measure and Receiver Operating Characteristics (ROC). The results indicated that the SVM model predicted with an accuracy of up to 84.407% compared to other models used in this research. The results of this research are expected to greatly serve the education sector and contribute to knowing the extent of the impact of distance education on King Abdulaziz University students and to knowing the factors that may have an impact on students in such an emergency situation.

Keywords: Covid-19 pandemic, Distance education, Machine learning, Education.

Abstract: Heart is the most important organ for all living organisms. The heart related diseases caused numerous numbers of deaths worldwide from past few decades. Prediction and diagnosis of the heart diseases with very high precision and correctness is required for early diagnosis. Machine Learning helps in making predictions and decisions from large data sets of data consisting medical parameters. The paper demonstrated many Machine Learning algorithms such as Decision Tree Classifier, KNeighbors Classifier, Naive bayes, Random Forest Classifier, Grid Search CV, SVM for predicting the heart disease using Erbil heart disease dataset from kaggle having 22 different medical and non-medical attributes. The precision, accuracy, F1-score, and recall of all algorithms used for predicting the heart disease is evaluated. Decision Tree Classifier algorithm provided a good accuracy of 98% among all other algorithms.

Keywords: Machine learning, Naive bayes, KNeighbors Classifier, Random Forest Classifier, Grid Search CV, SVM, Decision Tree Classifier, F1-score, Confusion matrix.

Abstract: With expanding individuals of India, developing speed of birth and diminishing demise rate because of progress in the clinical field it's seen that measures of specialists are less to serve the need of the developing individuals. The current situation can be better seen while strolling around the nearby's association clinical offices where the less accessibility of the specialists is the basic reason for the not great treatment of the patients and in unequivocal situation the resultant end. Eventually even specialists can submit botch in giving the right treatment accomplish passing of patient. To experience such cases there is a need of the sharp and Intelligent chatbot who can provide guidance to the prepared experts and eventually, even patients concerning what to do in such cases which at last accomplishes the saving the presence of various individuals. The AI set up clinical chatbot in regards to which this subject is based courses of action with giving clinical allure in such situation considering the way that eventually specialists could submit bungle while seeing the delayed consequences at any rate the machine which is unequivocally committed for it can't submit such error. This AI based clinical chatbot can recognize choice as shown by the deals of the patient. For this, it utilizes its own educational assortment and in express situation where something isn't open in its enlightening assortments per the mentioning of the client, it collects the data from the web crawler like Google and give it to the client.

Abstract: Our work is building a sarcasm detection model which detects the sarcasm in a sentence that may be in news headlines or statements made on social media. There has been an exponential growth of social media in recent years. An immense amount of data is been put into the public domain through social media. To start this work first we have to know what Sarcasm is. When someone says or writes anything that is entirely unrelated to what they actually intended, they are expressing their feelings through sarcasm. Sarcasm is extremely difficult to spot because of how anonymous it is. The accuracy of the sentimental analysis can be increased by performing a perfect analysis and interpretation of sarcastic language. Understanding a person's attitude and opinions is the goal of the sentimental analysis. We attempted to describe the overall architecture of sarcasm, technique, and vector visualization of words using TensorFlow in this work.

Keywords: Sarcasm, Sentimental analysis, TensorFlow, Vector visualization, Social media.

Abstract: We will focus on applications that use Augmented Reality (AR) and Virtual Reality (VR) in educational settings, in this paper exploring the use of 5G technologies. After introducing a few scenarios using AR/VR approaches, it describes the characteristics of 5G and illustrates how it can be applied to education. By using AR and VR to annotate the environment, it facilitates the sharing of knowledge in the education field. Enhancing the learning environment with augmented reality (AR) will add new information. 5G technology will only be able to guarantee high-quality 360° video, low-latency two-way interactions, and precise location and orientation of users. Therefore, it allows educators to deliver educational experiences in a variety of innovative ways, including through mobile devices, remote access, and remote mobility.

Keywords: Augmented Reality (AR), Virtual Reality (VR), 5G technology, Low Latency, Educational experiences

Abstract: Initial Coin Offering (ICO) is considered as a fundraising tool[6]. Any start up can create a cryptocurrency or digital token using various platforms. One of the platforms is Ethereum which provides a toolkit for companies to create new digital tokens. The companies will then do a public ICO where the investors can buy the newly mined Tokens. The number of ICOs and capital raised substantially since 2017. Funding the beneficial and innovative ventures is a prime topic in the entrepreneurial finance. The innovative ventures that use distributed ledger technology(DLT) are supported by ICOs[4]. The most common DLT is the Blockchain Technology. ICO is also referred as “Token Sale” or “Crowd Sale”. In ICO, companies issue tokens and then sell them to the crowd of investors. ICO is similar to crowdfunding but the differential quality is the use of DLT which is the center to these tokens and is required for the issuing of the tokens. Blockchain Technology is a revolutionary, innovative and disruptive technology. Thus, funding this domain using ICO is of great interest to the entrepreneurial finance. This study is about literature on entrepreneurial finance by the introduction to ICO. This Technical review will be done on an individual level and the review will discuss in detail how introduction to ICO can benefit new ventures and also how ICOs are proving beneficial for innovations and its challenges to implement in the finance industry. We will compare different platforms for putting forward the ICO. We will also see how an ICO can affect the growth of the company and its profits.

Abstract: The abstract of the research paper discusses how Shared Services Centers (SSCs), enhanced by cloud-based ERP systems like Workday, offer a strategic framework for organizations, particularly multinational ones, to streamline internal services by centralizing various functions. This approach aims to reduce costs and eliminate unnecessary duplications within an organization. The paper highlights the evolution, technological enhancements, core functions, advantages, and the outlook of the shared services model, emphasizing its efficiency, cost-effectiveness, and the ability to leverage advanced technology features to improve service delivery and operational excellence.

Keywords: Shared Services Centers (SSCs), Cloud-based ERP Systems, Workday ERP, Organizational Efficiency, Cost Reduction, Centralization of Functions, Multinational Corporations, Technological Enhancements in SSCs, Financial Management, Operational Excellence

Abstract: The paper discusses using machine learning algorithms for predictive maintenance in passenger vehicles. It covers the background, problem statement, and objectives. It also explores the importance of engine telemetry data and the challenges of implementing machine learning. The paper explains data collection, preprocessing, and various machine learning models, including supervised and unsupervised algorithms. It concludes with future research possibilities and a summary of the key findings.

Keywords: Engine Telemetry, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM)