JOURNAL OF SOFTWARE ENGINEERING TOOLS & TECHNOLOGY TRENDS
ISSN: 2394-7292 Scientific Journal Impact Factor (SJIF): 6.062 Index Copernicus (ICV: 53.42) Journal DOI no.: 10.37591/JoSETTT
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
Revolutionizing Software Engineering: Harnessing AI-driven Continuous Integration for Seamless Collaboration and Rapid Development
In the dynamic landscape of software engineering, this paper introduces a revolutionary approach by integrating artificial intelligence (AI) into continuous integration processes. The research aims to redefine software development methodologies, leveraging AI-driven continuous integration (AI-CI) to enhance collaboration among development teams and accelerate software development cycles. The comprehensive study delves into the theoretical foundations and practical implications of AI-CI. The integration involves infusing AI algorithms into the continuous integration pipeline, providing intelligence, adaptability, and efficiency throughout the development lifecycle. Real-time case studies and experiments demonstrate AI-CI's impact on collaboration dynamics, reducing response times in code reviews, streamlining issue resolution, and fostering efficient knowledge-sharing. Moreover, the research highlights the substantial acceleration of software development cycles through predictive deployment models empowered by AI. Reduced deployment times, optimized resource allocation efficiency, and overall cycle acceleration signify a shift towards agile and responsive software development practices. Validation through real-time case studies and experiments reinforces the effectiveness of AI-CI in controlled environments, paving the way for real-world applications. This paper envisions a future where AI becomes integral to software engineering, guiding the community towards unexplored territories of efficiency and excellence. As the industry navigates this transformative journey, the integration of AI into continuous integration stands as a testament to its commitment to innovation and adaptability. The paper acts as a trailblazer, illuminating a path where the synergy between human and artificial intelligence propels software engineering towards unparalleled innovation.
Copyright (c) 2023 JOURNAL OF SOFTWARE ENGINEERING TOOLS & TECHNOLOGY TRENDS
An Overview, Changes, and Uses of the Novel Social Spider Optimization Algorithm
The rising complexity of real-world problems has prompted researchers and computer scientists to look for more effective ways to solve difficulties. Generally inspired by nature, bioinspired metaheuristics based on swarm intelligence algorithms and evolutionary computation have been widely employed to solve complicated, real-world optimization problems because of their adaptability to a broad variety of factors. The social spider optimization (SSO) approach is introduced in this article as a swarm-based algorithm, drawing inspiration from the cooperative behaviors exhibited by social spiders. Search agents in SSO describe a group of spiders that collectively move in accordance with a biological behavior in the colony. Many changes have been made to the SSO algorithm in the years since it was first introduced, improving its performance and enabling its use in other sectors. The developments and uses of the SSO are reviewed in this study.
Current Trends in Secure Software Engineering: Novel Perspectives
The security-focused approach in software engineering emphasizes the essential incorporation of security measures throughout the entire software development lifecycle. In an era marked by escalating cyber-threats and data breaches, this paper delineates novel perspectives and principles to fortify software systems against evolving adversarial landscapes. Motivated by the need to protect sensitive data, prevent financial losses, and adhere to stringent regulatory frameworks, the paper advocates for a proactive stance in software security. The principles outlined encompass threat modeling during the design phase, implementing the principle of least privilege, embracing defense in depth, and adhering to secure coding standards. With a focus on securing user authentication, robust data encryption, and meticulous input validation, the paper underscores the importance of a comprehensive security-oriented approach. The significance of continuous monitoring, incident response planning, and user education is also emphasized to bolster the overall security posture. By discussing these principles and practices, this paper serves as a comprehensive guide for software developers, architects, and stakeholders seeking to integrate robust security measures seamlessly into their software engineering endeavors. The novel perspectives presented herein aim to inspire a paradigm shift in the way security is conceptualized and integrated throughout the software development lifecycle.
Empowering Banking Operations Through Excel Innovations for Data Precision and Security
Excel plays a pivotal role in the banking sector due to its unparalleled versatility and robust data processing capabilities. It serves as a fundamental tool for handling complex financial calculations, data management, risk assessment, and generating comprehensive reports. Its ability to organize, analyze, and visualize vast volumes of financial information empowers banking professionals to make informed decisions crucial for managing investments, determining lending strategies, and optimizing financial portfolios. Excel's adaptability and proficiency in financial modeling, analysis, and reporting make it an indispensable asset in ensuring the effectiveness and success of banking operations. The research paper delves into Excel's pivotal role in the banking sector, highlighting its versatile functions vital for robust data management and analysis. Excel's capability to handle vast financial information, perform complex calculations, and generate comprehensive reports stands as a cornerstone in banking operations. This paper discusses the significance of Visual Basic for Applications (VBA) and macros in Excel, detailing how these tools empower automation and customization, enhancing Excel's capabilities beyond basic functionalities. Moreover, the paper outlines specific enhancements desired in Excel's functionality for security and number formatting in the banking domain. It proposes a model using VBA procedures to address these issues, incorporating functionalities such as importing numbers with leading zeros and hiding/unhiding worksheets with user-supplied passwords. The experimental results showcase step-by-step procedures, VBA implementation, and validations for the proposed enhancements. The research aims to improve Excel's efficiency and usability in banking operations, offering solutions to streamline processes and ensure data accuracy.
Deep Learning-Based Sentiment Analysis of Twitter COVID-19 Vaccination Responses
The COVID-19 pandemic has caused significant fear, anxiety, and complex emotions or feelings in a large number of people. A global vaccination campaign to end the SARS-CoV-2 epidemic is now in progress. People's feelings have become more complex and varied since the introduction of vaccinations against the coronavirus. The use of social media platforms such as X (formally known as Twitter) enables users to communicate with one another and share information and perspectives on a wide variety of topics, spanning from local to international concerns, from global to personal.Twitter will prove to be a helpful source of information that can be tracked regarding views and sentiments regarding the SARS-CoV-2 vaccination. This study employs deep learning techniques to discern prevalent themes and sentiments within public discourse on Twitter concerning COVID-19 immunization. The goal is to gain a comprehensive understanding of public views, concerns, and emotions that could impact the attainment of herd immunity targets and mitigate the pandemic's effects. Moreover, this paper consists of a detailed explanation of the sentiment analysis with their challenges, classification, approaches, applications, and VADER (Valence Aware Dictionary and Sentiment Reasoner).
Valence Aware Dictionary and Sentiment Reasoner (VADER)
Enhancing Healthcare Record Modeling Through Graph Convolutional Networks: Overcoming Challenges and Advancing Data Management and Analysis
Modeling healthcare records data as a graph database presents various challenges due to the complex nature of healthcare information and its interconnectedness. In this research endeavor, our primary objective is to pinpoint the major obstacles within this field and present potential remedies to tackle them effectively. By leveraging machine learning techniques, we aim to enhance the efficiency and accuracy of healthcare record modeling, facilitating better data management and analysis. One suitable algorithm for addressing these challenges is the Graph Convolutional Network (GCN). GCN is a deep learning algorithm that operates on graph-structured data, making it well-suited for modeling healthcare records represented as a graph database. GCN allows information propagation between interconnected nodes, capturing the dependencies and relationships within the data. To apply GCN to healthcare record modeling, we can represent patient records as nodes in the graph, with edges indicating various relationships, such as diagnoses, treatments, and patient demographics. The algorithm can then learn the node embeddings, which encode the underlying features of each record, by propagating and aggregating information through the graph. The proposed GCN algorithm can address several challenges in modeling healthcare records as a graph database. Firstly, it can handle the heterogeneous nature of healthcare data by capturing different types of nodes and edges. Additionally, GCN demonstrates a strong capability to capture data dependencies and patterns, enhancing the precision of predictions and recommendations. Additionally, the algorithm can handle missing or incomplete data by leveraging the information from neighboring nodes. We can overcome the challenges associated with modeling healthcare records data as a graph database by utilizing machine learning algorithms such as GCN. These advancements can improve the management and analysis of healthcare data, leading to better patient care, efficient resource allocation, and more informed decision-making in the healthcare domain.
Perception Analysis of Social Network Tweets Using a Genetic Algorithm Based Approach
We communicate, post different comments, communicating with different people in Social media. Different people of the world posted their views on COVID-19 during the pandemic. In this study we analysed the posted comments on social media. Analysis of the comment in social media is called sentiment analysis. We performed sentiment analysis on content from social media platforms, classifying it into either positive or negative categories. For this task, we proposed a different hybrid model. We used deep learning and genetic algorithm. GA algorithm and Stacked LSTM gave 95% positive sentiment. This model helps the health administrators in the world to make proper decisions to fight upcoming pandemic.
A Survey on Image Forgery Detection Techniques Using Machine Learning Approaches
With the advent of newer technologies and usage of data in various service fields like education, commercial and healthcare, etc. The possibility of tampering and altering the data in the form of media like images is easily seen to derive various illegitimate benefits and outcomes. The rapid advancements in technology and the increased utilization of data in various service sectors such as education, commerce, healthcare, etc., have raised concerns about the possibility of data tampering and image manipulation for illicit gains. This can result in a loss of data integrity and have significant repercussions. In smart healthcare frameworks, new communication technologies, features, and facilities are being developed with the goal of providing easy-to-use, accurate, and real-time healthcare services to clients. Given the sensitive nature of health-related data, it is imperative to prioritize security and caution in order to ensure seamless and real-time system operations. A new approach for detecting image forgery, utilizing deep learning techniques, particularly convolutional neural networks (CNNs), has been created to identify fraudulent medical images. This method employs CNNs for detecting image forgery and ensuring the authenticity of medical images.
Fuzzy Algorithm Based Sentiment Analysis: Enhancing Accuracy and Interpretability
Sentiment analysis is the technique of determining the point of view, polarity, and intensity of a text. A poll found that 93% of consumers read online evaluations before making a decision. Manually analysing these judgements is challenging. To determine the review's overall sentiment or thinking polarity, sentiment analysis may be employed. This study's goal is to classify product assessment qualities as positive, negative, or neutral. Polarity labelling, feature extraction, and associated descriptor extraction are all included. The suggested method expands on feature-based categorisation by considering the impact of different linguistic hedges. In order to mimic the effects of modifiers, concentrators, and dilators, this technique uses fuzzy functions. The system was put to the test, and the outcomes show how well fuzzy logic based sentiment analysis performs.
The process of identification is quite hectic and recurring lots of fake identification are created for fraudulent transactions. It is a tedious work of carrying and managing all our documents. E-identity verification is the system which is proposed to make the process of identification easier and faster with one time user identification and then identifying the user based on the saved data. Recently, a new authentication protocol based on blockchain has been proposed to facilitate the sharing and management of IoT devices information in a distributed manner. It is claimed that this protocol ensures both user privacy preservation and security. However, a significant challenge arises due to the direct proportionality between data integrity and the associated costs of communication and computation. A central authority will store user data in a blockchain system, which can later be used to identify the user with their consent. Blockchain-enabled user identification reduces fraud user identification.
Misinformation has exploded in the last two decades with the development of digital technologies. The damage caused by it to social harmony, peace, social trust, justice, and democracy has increased the necessity of automatic identification of bogus news and intervention. As more and more content is being available, a person may not deliver much attention to a news piece and may assume it to be reliable just from reading headlines. Also, as customer attention is divided, news portals are tending to publish clickbait or catchy headlines to seek more attention. This vicious cycle has resulted in furthering spread and damage of fake news. This study reviews and discusses methodology to detect fake news and suggest possible limitations along with appreciating their contribution. This survey reviews selected papers from 2017 to 2021 which have focused on developing state of the art models, benchmarks, or new datasets for fake news detection. We think that this survey will provide a good inception point for various technologies related to fake news and a more comprehensive understanding for anyone interested in researching it as well.
Fake User Identification and Duplication Detection Using Aadhaar Card
Various universities are facing several issues concerning students' fake enrollment, duplication, and authentication of student details. Here we are providing solutions to improve student enrollment process for various exams by using convolutional neural networks (CNN) and OpenCV (Open-Source Computer Vision Library). A solution to the existing system must be versatile and precise. If the project aims to be used for large-scale deployments, then it must be adaptable and accurate in order to be successful. We have used advanced technologies and algorithms such as Convolutional Neural Networks (CNN) and OpenCV for accurate face detection and recognition, as well as the MERN stack for verifying student details. Our implementation fulfills the problem statement by solving the primary concern of universities regarding authentication, duplication, and fake enrollment of the students. After successful verification, the student will be directed to the exam page where any educational activities related to exam can be performed.
Cloud Based Document Drafting Tools for Lawyers
Drafting of the documents like notices and plaint, etc. is the most essential thing to file any sort of case in the court. It not only decides what all court can take the cognizance for the case but also gives whole idea about the issues in dispute, what remedy is requested by the court, etc. To understand this, judge requires to be submitted with the document which can be in any form prescribed by the law. So, this makes drafting as a very essential element to learn for a lawyer. While drafting about any certain document it requires following the format as per the law and cannot be changed by the individual person. If the document is not formatted, as it needs to be, there is a high possibility that the document will not be accepted by the court of law. The format needs to be followed so strictly that even a small mistake can amount to rejection and had to made and submitted again. Hence, drafting requires a lot of efforts and considered as the most time-consuming task with the lawyers. Another thing with drafting is that there are different formats for different documents subsequently resulting in research to be done to prepare correct document. This system deals with the drafting of various formats of documents providing a helping hand to the lawyers saving their time. The system is made using which can automatically read which document needs to be prepared and take the essence of cloud in order to provide easy access and management of these documents on behalf of the lawyers.
Comparison of Machine Learning Classification Algorithms for Prediction of Early-stage Diabetes
Diabetes is the most widespread and gruesome disease spread all around the globe. It is a chronic metabolic disease which has mild symptoms which are hard to spot at the initial stage. It is well known that diabetes causes a high blood sugar level. This is because insulin is not able to carry sugar from your blood into your cells in order to be stored or used for energy as it should. Negligence in treatment and identification of this disease can damage a person’s nerves, eyes, kidneys, skin, and other organs. Thus, timely diagnosis and treatment can be effective in overcoming this disease. The purpose of this study is to review a number of frameworks that have been proposed as ways to predict diabetes disease and classify diabetes using Machine Learning (ML) algorithms. The database is a summarized set of data collected from various medical research centres and hospitals from across the India. The emergence of diabetes can be predicted by applying four different classification algorithms, i.e., KNN Algorithm, Logistic Regression, Random Forest, and Support Vector Machine. The motive of this framework is to save the money and time of a patient in diagnosis using various ML approaches.
Project Management Using Topological Sorting
Project management is a complex process involving planning, coordinating, and managing resources to accomplish certain goals within specified time frames. Topological sorting is a method used in graph theory to arrange nodes in a Directed Acyclic Graph (DAG) in order of their dependencies. This method can be used to determine the order in which tasks must be accomplished in order to successfully complete the project. Topological sorting helps identify the critical path of a project, prioritize tasks, manage resources, and optimize project schedules. This study provides a literature review on project management using topological sorting, highlighting the key insights and scope of this technique across various industries and project types. The findings show that topological sorting is a powerful tool that can be applied to project management for engineering, software development, manufacturing, marketing, and event management projects. Overall, this study highlights the importance of topological sorting in project management and provides insight into practical applications in various industries.
Blockchain Application in Civil Aviation and Defense
The emerging technology of Blockchain holds great promise in providing decentralized trust, data security, transparency, traceability, visibility, and auditability across several domains in the aerospace and defense sectors. This survey explores the potential for leveraging Blockchain's features, platforms, and architectures to transform modern aerospace and defense ecosystems and applications. Additionally, it examines complementary architectural technologies like Oracles, IPFS, and Filecoin that can augment Blockchain's capabilities. The survey also discusses the key roles and opportunities that Blockchain can play in aerospace and military applications. We look at a number of use scenarios, including managing combat operations, border security, logistics, and supply chain management, as well as swarm support for rescue operations, to explore the possibilities for Blockchain in the aerospace and defense industries. Furthermore, we assess the suitability of existing Blockchain platforms for aerospace and defense applications. Additionally, we point out unresolved problems and obstacles that could hinder the extensive utilization of Blockchain technology in the aerospace and defense fields. These issues consist of delays in processing, apprehensions about maintaining data confidentiality, adherence to regulations, managing large volumes of data traffic, ensuring the safety of smart contracts, and promoting compatibility among various systems.
Copyright (c) 2022 JOURNAL OF SOFTWARE ENGINEERING TOOLS & TECHNOLOGY TRENDS
Web Development Using Software Development Life Cycle (SDLC)
One of the most powerful, essential, and significant issues in the computer industry is software project development. It is a stage in the software development life cycle (SDLC). The SDLC focuses on minimizing risk and failure while maximizing software product quality. Without a good step-by-step producing technique, the software development process is extremely difficult, hence the software development life cycle was created to make the process easy and systematic. This is the approach of software development that is structured and organized. A framework known as the software development life cycle (SDLC) outlines all the procedures and duties that must be completed while creating software. Several software development life cycle models are employed in the software development process, each with unique benefits and drawbacks that are discussed in this study. This document offers a set of tasks and best practices for all parties concerned with the organization, design, coding, testing, and upkeep of software systems. This research compares the proposed new SDLC model with the existing SDLC model and finds that the latter does not appropriately focus on usability concerns when creating effective, useable software products. Statistical findings are used to demonstrate this conclusion.
Class Imbalance Reduction and Training Data Selection for Cross Project Defect Prediction
The research aims to predict errors in a targeted project using data from other projects. This project is named as the Cross-Project Defect Prediction (CPDP). There are a number of ways available to improve the predictable performance of CPDP models. However, there is no comparison of modern methods. Predictability facilitates the rational distribution of testing resources by detecting software modules that may be problematic before releasing products. If a project does not have defective history data, project projectile prediction (CPDP) is an alternative. The project disability forecasting process is significantly improved by choosing appropriate training data (CPDP). In this study, there is a reduction of the class inequality class (CIR) algorithm proposed to create similarities between impeccable and impact cable records of data sets by considering data distribution structures and comparing learning package tools for the many built-in devices considered as a benchmark in dealing with issues of class inequality such as KNN, Decision tree, Naive Bays, Random-Forest, AdaBoost Algorithms, Gradient Boosting and Extra tree classifier algorithms.
Hypothesis Testing and Life Expectancy: A Review
Data science revolves around the idea of making moves/choices on the basis of past information available. However, data in itself is a broader term. There is raw data, which is collected by using survey and other methods, it may contain the information we need, but it also contains various other non-required information which is removed from the raw data by processing it. The data now recovered is vital information. Data Science is used in predicting trends and forecasting events. Hypothesis testing combines both at a fundamental level and is quite easy to understand and begin with. In addition to that, the testing involves methods currently being taught in our syllabus; so, a side-by-side application helps open our minds towards the subject.
Developing Socially Responsible AI for Smart cities: Critical Analysis, Concerns, Research Challenges
With modern day technologies inclined heavily toward creating a smart ecosystem for living life, the responsibility of the fittest technology is lending help from artificial intelligence. But the provision of ethics and fairness is becoming a hurdle in efficient deployment of AI tools and techniques. With an element of social responsibility cropping into the smartness of technologies used to develop smart cities, our article is an attempt to review the concerns and future challenges faced by researchers in this domain. We have performed a well thought critical analysis regarding the various aspects of socially responsible AI and also brought issues to the forefront which will make this as emerging research area in years to come.
Artificial intelligence (AI), blockchain, socially responsible AI, Ethics, smart city
SDN and NFV Architecture for IoT Infrastructure
It is foreseen that SDN combined with NFV and distributed computing, will turn into a basic empowering innovation to fundamentally change the way network administrators will modeler and adapt their framework. Then again, the Internet of Things (IoT) is changing the collaboration among the internet and the actual space with an enormous effect on regular daily existence. The adequacy of these advancements will require new methodological and designing methodologies because of the amazing size of the issue and the new testing demand as far as execution, security and unwavering quality. This paper presents a straightforward and general SDN-IoT design with NFV usage with explicit decisions on where and how to embrace SDN and NFV ways to deal with address the new difficulties of the Internet of Things. The design will quicken developments in the IoT area, because of its adaptability opening new points.
Critical Success Factor and Barriers to Successful Implementation of Software Process Improvement (SPI) in Global Software Development (GSD)
Many software development companies are currently expanding their operations globally. Competitive advantage and the financial profits it brings to an organization are the driving forces behind the globalization of software. The distributed nature of the software development process presents many challenges for the industry. Software process improvement in international software development is one of the major difficulties. Adopting software process improvement has a large positive impact on most software development organizations. In terms of research and generally accepted standards, this has gotten a lot of attention. To help software process improvement (SPI), practitioners properly plan SPI implementation methods. This study's goal is to arm them with enough knowledge about the types of difficulties that contribute to successful SPI implementation. This study offers a thorough examination of software process improvement in the context of international software development. Through this study, we have identified seven factors (Management Commitment, Active Involvement of Staff, Relationship, Resource Allocation, Project Team training, SPI Goal Setting, SPI awareness) as critical success factors and five factors (Availability of Resources, Staff Experience, Organizational Policies, Time Pressure, Frequent Staff Turnover) as barriers for successful implementation of software process improvement in global software development. Additionally, how the critical success factors and barriers affect the productivity and implementation of software process improvement in global software development are illustrated.
Design and Development of Control and Management Systems for Smart Homes
The smart home and management system in this study are powered by D.C. batteries. Before running out of power, a fully charged battery can last for 2 to 3 days. When you turn on the system, the maximum temperature and the name of the study are shown on the LCD. Additionally, it will demonstrate that no one is in the house. As more individuals enter the house after you click the push button to enter, it will show on the LCD (Liquid Crystal Display) that you are welcome and that there are now more people inside. You get to decide how many people you let inside the residence. If you set the limit to 12, the door won't open again until someone quits the building by pressing the exit button before another person is allowed to enter. You can pick when you want the fan to turn on automatically by setting the maximum temperature. The fan in the house is used to lower the temperature if the weather is hot. The fan won't turn on until the maximum temperature of 30C has been reached, for example. Additionally, if everyone leaves the house, the fan and light will turn off automatically because nobody is inside. For safety purposes, there is a light in front of the house. It will automatically turn on if it becomes dark outside at night. The second Light Dependent Resistance (LDR) outside the home is in charge of controlling this light. The exterior light will turn off during the day if the LDR detects light, and it will turn on automatically at night if it detects dark. This is done for security reasons. Within the residence is the first LDR. It will switch on the lights when someone enters the house. No one will be able to exit the house before the light is turned off.
A Correlative Study of Machine Learning Algorithms
Machine learning (ML) is the term used to define a systematic reading of algorithms and mathematical models of computer structure used to perform a specific task without precise programming. Learning algorithms in many programs we use every day. Nowadays, one of the reasons an online search engine like Google performs so effectively is because the learning algorithm has mastered the art of rating web sites. Additional uses for these algorithms include data mining, image processing, predictive analysis, and more, to name a few. The great advantage of using machine learning algorithms is that, when the algorithm finally learns what to do with the data, it can do it on its own. Numerous industries use machine learning, including bioinformatics, intrusion detection, information retrieval, gaming, advertising, malware detection, and more. A general overview of several machine learning algorithms is given in this article. In the beginning, we concentrate on several ML algorithms and highlight the most promising learning approaches in this area. Then, in order to improve accuracy, we look into the close relationships between machine learning techniques and data mining strategies. This paper offers a concise overview and discusses the potential uses of machine learning algorithms in the future.
Algorithm, Machine Learning, Pseudo Code, Convolution Neural Nets, Auto encoders
DevOps, which stands for development and operational groups, is a method for delivering software that is getting ever more popular. This method helps produce software very quickly and consistently. These days, a wide variety of solutions, including such Continuous Integration/Continuous Delivery (CI/CD) using common research articles, enable DevOps troubleshooting. Because certain components need additional setting, many broad research papers have rather good accuracy costs when it is used in developing games. In order to enable DevOps and CI/CD, this research seeks to establish a collection of automations, also referred as that of the DevOps platform, that would be used in the context of developing games. This programme is concentrated on automation numerous game design activities, including games construction, distribution to Steam, and problem reporting. The DevOps toolchain can boost application performance by up to twice associated with decreased interface expenses and optimisation of numerous workflows, according to the findings of user evaluations performed to innovators and early adopters.
Automation, continuous integration, continuous delivery, DevOps, self-service.
Automatic Temperature Screening System Preventing from COVID-19
In the midst of the COVID-19 (coronavirus disease) pandemic, the most important preventive measure for good health and hygiene is to wash our hands and sanitize them with alcohol sanitizers. IoT-enabled Automated Temperature Screening is a technique that monitors body temperature using a machine learning model. The proposed model can be applied to any shopping mall, hotel, or apartment entrance, for example. As a result, a cost-effective and dependable method of using AI and sensors to create a healthy environment was developed that can be used when the pandemic period is on and gets over. Nowadays, in shopping malls, offices, restaurants, cafes, industries, etc., the security guards check each and every individual person’s temperature before allowing them to enter. After the scanning process is completed, the temperature will be displayed on the LCD. If any person has a temperature that is higher than the normal body temperature that would not be allowed to enter, hence, can also be taken as preventive measure.
Covid-19, LED, IR Sensor, LCD Display, Temperature Screening
Disease Prediction System Using Data Mining
The project's main goal is to develop a system that allows users to get personalized advice on their health problems using data mining techniques. In today's hectic world, most individuals overlook this asset, which could be due to a lack of time or the intricacy of the massive data available on the internet. Our goal is to evaluate data processing techniques in clinical and health care settings in order to make informed decisions. Machine learning and database management are used in data mining to extract new patterns and knowledge linked to these patterns from large data sets. Particularly the task is to get data. The various parameters enclosed in data processing include path analysis and predictive analysis. Due to the availability of computers, a vast amount of information in the medical and health care disciplines is becoming accessible. Such an oversized amount of information cannot be processed to make health predictions in the early stage and make treatment schedules to diagnose. It is a cutting-edge, powerful technology that is generating a lot of buzz in the computer world. It reworks information from many databases into new research and outcomes.
Prediction system, disease prediction, naive bayes, symptoms, data mining, structured data, text data
Design of a Low Cost and Low Power Smart Wireless Sensor
The accessibility of minimal expense, low-power and little inserted processors, radios and sensors, incorporated on a solitary chip, is prompting the utilization of remote interchanges and registering for connecting with the actual world in numerous regular citizen and military applications. The advances in the incorporation of complicated remote coordinated items and the expansion in execution and usefulness of the plan apparatuses, the prerequisites and properties of a solitary gadget (sensor hub) are explained. It is difficult to design and deploy smart wireless network with the help of smart sensor as it relies on the power and other parameters. Hence with respect to power utilization, the work of nodes and their life is preserved using the smart relay networks. WSN Equipment must be little enough while utilizing an appropriate power cost calculation. These worries delineate the cautious equilibrium and agreements that are required in WSN implementation and its plans. Remote sensor networks are another innovation that can change dispersed estimation frameworks and are prepared to execute in business frameworks. The progress of another innovation is still up in the air by the actual innovation, but there are countless other factors as well.
Estimating the Chances of Winning IPL Match using Machine Learning
Cricket is a popular sport not only in India but across the world. In recent years, the T-20 variant of this game has become extremely popular. The Indian Premier League (IPL), one of the championships associated with this format, has grown rapidly. On the other hand, cricket has long been considered a high-risk sport. Predicting the winner of the tournament or the game has an area of concern for many fans. Innovation, on the other hand, is progressing at an alarming rate. For model training, researchers always turn to machine learning (ML) algorithms to predict something. In this study, we will predict the probable percentage of winning of team in the IPL using different supervised learning methods. To predict our outcomes, we will apply the Logistic Regression (LR) Algorithm in this work. The existing models were trying to predict the match result before the start of the match which leads to automatic less accuracy. But, the proposed model is more effective because it predicts match winning percentage in the second inning that was not done earlier. In comparison to other existing algorithms, the proposed LR algorithm has an accuracy of 80%.
Resources/Funds Sharing through Decentralized Application (DApps) Using Ethereum on Blockchain
A purely decentralized application meant to share resources/funds online with the most hassle-free and provide a reliable solution that is not supported by any intermediate, centralized authority and provides transparency with a perfect peer-to-peer approach. This system works to fulfil two of the most important requirements. Internet promised but was not able to solve and are the biggest question on the security of any application, data, network, etc. This decentralized approach is based on blockchain and hence is paired with concepts of digital signature that can protect the integrity of the data that is registering or passing over channels; and Time stamping that helps to maintain the records of the date, day, and time of the transfer of the data happening in the network. This prevents the unfortunate dual transits of a single block to two different nodes and works as a computational proof for the chronological order of transactions. All together, these create a blockchain network that works like an immutable ledger that is computationally impractical to reverse once passed, saving people from frauds and an unauthentic act of corrupting the system. Majorly, this system can state to be safe until the honest nodes together have control over the CPU power over any cooperating group of attackers.
Copyright (c) 2022 Journal of Software Engineering Tools & Technology Trends