Recent Trends in Computer Science and Software Technology

Author: Madhur Sharma

Abstract: This paper explores the transformative impact of 5G networks and edge computing on Software-Defined Networking (SDN), focusing on how these technologies synergize to meet the demands of emerging applications like autonomous vehicles, smart cities, and remote operations. As networks face unprecedented requirements for low latency and high bandwidth, integrating SDN with 5G and edge computing offers a powerful architecture to manage traffic, optimize resources, and improve scalability. This paper analyzes the advantages, technical requirements, and challenges in implementing 5G and edge computing within SDN frameworks, illustrating the potential for more efficient, reliable, and adaptable networks.

Keywords: 5G, Edge Computing, Software-Defined Networking, Latency Reduction, Network Efficiency, Autonomous Vehicles, Smart Cities, Remote Operations

Author: Nikhil Bhatia

Abstract: The rapid expansion of the Internet of Things (IoT) has introduced transformative opportunities across industries, but it has also unveiled a broad spectrum of cyber security vulnerabilities. This paper provides a comprehensive analysis of advancements in IoT network security, focusing on real-time threat detection, encryption techniques, and secure software development practices. By examining these key areas, we aim to identify methods to protect IoT infrastructure from malicious attacks and ensure data integrity, confidentiality, and availability. Practical insights, methodologies, and innovations are discussed to guide effective implementation of robust security measures in IoT ecosystems.

Keywords: IoT security, threat detection, encryption, cyber security, real-time monitoring, secure software development

Author: Vinod Patil

Abstract: Quantum computing has the potential to revolutionize software optimization by enabling the rapid processing of complex algorithms. Traditional computing faces limitations in handling certain problems efficiently, particularly those involving cryptography, machine learning, and large-scale data. Quantum computers, with their unique properties such as superposition and entanglement, promise exponential increases in processing power. This paper investigates the potential applications of quantum computing for optimizing complex software algorithms, exploring how quantum advancements can contribute to more effective cryptographic security, enhanced machine learning model training, and efficient data analysis in large datasets. The study provides insights into the integration of quantum computing in traditional software environments and examines the prospective impact on various industries.

Keywords: Quantum Computing, Software Optimization, Cryptography, Machine Learning, Large Datasets, Quantum Algorithms, Quantum Speedup, Data Processing.

Author: Sanjay Dubey

Abstract: This paper delves into blockchain interoperability, which has become crucial as multiple blockchain networks emerge with distinct structures, consensus mechanisms, and use cases. With an increasing demand for seamless data exchange and interaction across these isolated ecosystems, the development of cross-chain solutions is paramount. This paper reviews existing interoperability protocols, examines emerging technologies, and evaluates their potential applications in finance, healthcare, and supply chain management. It also addresses challenges, such as security, scalability, and standards, while suggesting future directions to advance cross-chain collaboration in the blockchain domain.

Keywords: Block chain, Interoperability, Cross-Chain, Decentralized Finance, Data Exchange, Consensus Mechanisms, Protocols

Author: Arjun Menon

Abstract: In recent years, artificial intelligence (AI) has made significant strides in transforming software development. AI-driven tools like code generation, automated code completion, intelligent code review, and automated testing have begun to play a pivotal role in enhancing developer productivity, reducing time-to-market, and improving software quality. This paper explores the latest advancements in AI-powered tools for code generation and software development, analyzing their capabilities, limitations, and impacts on traditional development workflows. Through a review of emerging technologies, use cases, and real-world applications, we aim to provide insights into how AI is reshaping the software development landscape. We also highlight future research directions to address existing challenges and foster innovation in AI-driven software development.

Keywords: AI-driven software development, code generation, automated testing, code review, developer productivity, workflow automation

Author: Rajeev Mehta

Abstract: Quantum computing represents a paradigm shift in computational capabilities, with the potential to revolutionize various fields of science and technology. This paper investigates recent trends in quantum computing, including advancements in quantum algorithms, quantum hardware, and quantum software development. The study explores how quantum computing could impact software development, from algorithm design to computational efficiency. The paper also examines the current state of quantum computing research and the challenges associated with transitioning from classical to quantum computing paradigms. By analyzing these trends, the paper provides a comprehensive overview of how quantum computing is poised to influence the future of software development and its implications for researchers and practitioners.

Keywords: Quantum Computing, Quantum Algorithms, Quantum Hardware, Software Development, Computational Efficiency

Author: Pooja Singh

Abstract: Software development methodologies and practices are undergoing significant innovations to address the demands of modern software engineering. This paper explores recent trends in software development, including the adoption of Agile and DevOps practices, the rise of continuous integration and continuous deployment (CI/CD), and the growing importance of software quality assurance and testing. The study examines how these methodologies improve development efficiency, enhance collaboration, and accelerate time-to-market. Additionally, the paper discusses the impact of emerging technologies such as artificial intelligence and blockchain on software development practices. By analyzing current industry practices and innovations, this paper provides insights into how software development is evolving and the implications for software engineering professionals.

keywords: Software Development, Agile Methodology, DevOps, Continuous Integration, Software Testing

Author: Sneha Mehta

Abstract: The field of cybersecurity is continuously evolving in response to the growing sophistication of cyber threats. This paper reviews recent developments in cybersecurity techniques and tools, including advancements in threat detection, incident response, and vulnerability management. The study covers the integration of artificial intelligence and machine learning in cybersecurity, highlighting their role in identifying and mitigating emerging threats. Additionally, the paper explores the impact of new security frameworks and standards, such as Zero Trust Architecture, on organizational security posture. The paper also addresses the challenges associated with these developments, including the need for skilled professionals and the balancing of security with usability. By evaluating the latest trends and technologies, this paper provides a comprehensive overview of the current state of cybersecurity and future directions.

Keywords: Cybersecurity, Threat Detection, Incident Response, Zero Trust Architecture, Machine Learning

Author: Nisha Sharma

Abstract: Cloud computing has undergone significant evolution, leading to the emergence of diverse architectures and services that cater to various business needs. This paper examines recent trends in cloud computing, including the development of serverless architectures, multi-cloud strategies, and advancements in cloud security. The paper delves into the benefits and challenges associated with these trends, such as improved scalability and flexibility offered by serverless computing, and the complexities of managing multi-cloud environments. Additionally, the study explores the role of containerization and microservices in modern cloud architectures and their impact on software development and deployment. By analyzing current industry practices and emerging technologies, this paper provides a comprehensive overview of the state of cloud computing and its future trajectory.

Keywords: Cloud Computing, Serverless Architectures, Multi-Cloud Strategies, Cloud Security, Containerization

Authors: Aarti Verma

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies with significant implications for various sectors. This paper explores recent advancements in AI and ML, focusing on deep learning techniques, reinforcement learning, and the integration of AI with edge computing. Key trends such as the rise of generative adversarial networks (GANs), transfer learning, and the application of AI in natural language processing (NLP) are discussed. Additionally, the paper examines the challenges and ethical considerations associated with these technologies, including bias in AI models and data privacy concerns. By analyzing current research and practical applications, this study provides insights into how AI and ML are shaping the future of technology and industry.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Generative Adversarial Networks, Natural Language Processing

Author: Gayatri Thakral

Abstract: This paper delves into the adoption of DevOps methodologies and agile practices in software development, investigating their impact on accelerating the development process and enhancing collaboration between development and operations teams. It explores key concepts such as continuous integration/continuous delivery (CI/CD), infrastructure as code (IaC), and site reliability engineering (SRE). Through a comprehensive analysis of industry practices and case studies, this paper provides insights into the benefits and challenges associated with implementing DevOps and agile methodologies. By examining real-world examples, it offers practical recommendations for organizations seeking to optimize their software development processes.

Keywords: DevOps, Agile Practices, Continuous Integration, Continuous Delivery, Infrastructure as Code, Site Reliability Engineering

Author: Prof. Neha Patel

Abstract: This paper explores the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) technologies, particularly focusing on deep learning, reinforcement learning, and natural language processing. It investigates how these cutting-edge technologies are revolutionizing various domains such as healthcare, finance, autonomous vehicles, and recommendation systems. By analyzing recent developments and real-world applications, this paper provides insights into the transformative potential of AI and ML in addressing complex challenges and driving innovation across diverse sectors.

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Healthcare, Finance, Autonomous Vehicles, Recommendation Systems.

Authors:  M. Shalima Sulthana, Mrs. K. Bhanu Sri, Ms. B. Ramya Sri

Abstract: Map Reduce is a scheme for processing and managing large scale data sets in a distributed cluster, which has been used for applications such as document clustering, generating search indexes, access log analysis, and numerous other forms of data analytic. In the existing system, a hash function is used to partition intermediate data among reduce tasks and most of the previous algorithms proposed concentrated on other parameters like data uploading, time reduction etc. None of them dealt with network traffic. Our proposed system consists of a decomposition-based distributed algorithm to deal with the large-scale optimization problem for large data application and an online algorithm is additionally designed to adjust data partition and aggregation in a dynamic manner. The cost of Network traffic under both offline and on-line cases is significantly reduced as demonstrated by the extensive stimulation results by the various proposals considered and used.

Keywords: Big Bata, Data Aggregation, Dynamic Decomposition-based Distributed K- means Algorithm, HC Algorithm, Traffic Minimization.

Authors: A. B. Bedkyale, Siddhi Sachin Shinde, Shreya Amol Sidnale, Asmita Kishor Teke

Abstract: In our disease prediction project, we aim to create a system that can predict diseases based on symptoms reported by patients. The system uses advanced machine learning techniques to analyze symptom data and generate predictions. By accurately identifying potential diseases early on, the system can help healthcare professionals make informed decisions and provide timely treatment to patients.

To achieve this goal, we collect and preprocess dataset containing symptom information and corresponding disease diagnoses. We then train machine learning models, such as Support Vector Machine (SVM) and Logistic Regression, using this data. These models learn from the patterns in the symptom data and can predict the likelihood of various diseases based on new symptom inputs.

The project also focuses on developing a user-friendly interface that allows healthcare professionals to input patient symptoms easily and receive prediction results quickly. This interface enables seamless interaction between users and the prediction system, facilitating efficient disease diagnosis and treatment planning.

Overall, our disease prediction project has the potential to revolutionize healthcare by enabling early disease detection and intervention. By leveraging the power of machine learning, we aim to improve patient outcomes and contribute to better healthcare delivery.

Keywords: Disease, Prediction, Symptoms

Author- Ms. A. A. More , Vaibhav Tembukade, Pavan Lagare, Abidahmed Maner, Jinendra Patil 

Abstract- Part of the Machinist toolkit is the NC File Sequence Generator, a versatile software program for generating numerical control (NC) files used in numerous industrial processes. Lathes, milling machines, and 3D printers are examples of CNC (Computer Numerical Control) equipment that require NC files to operate.

 They come with all the instructions needed to make precise and elaborate pieces. This software will be helpful to engineers, machinists, and manufacturers who want to automate the creation of NC files efficiently.

 This project showcases a VB.NET application created to solve a significant problem in the Power MILL 360 software's production workflow. All programs for a tool are assigned the same program number (O1000) by the software that creates programs for CNC or VMC machines. The machines become confused as a result, unable to understand the order of the programs. Reassigning the program numbers to an ascending sequence manually is the current option, which takes time and reduces productivity. This procedure is automated by our application, which creates an ascending sequence of NC files in a matter of milliseconds. In addition to saving a great deal of time, this increases production efficiency and offers a useful fix for a recurring issue.

Keywords-Machinist Toolkit, NC Files, CNC or VMC machines.

Authors: Abhishek Kanojiya, Gyanchand Shukla

Abstract: The field of data science has evolved significantly in recent years, with an increasing emphasis on leveraging big data technologies to extract valuable insights from large datasets. This paper provides a comprehensive review of the intersection between data science and big data technologies. We delve into key topics such as data preprocessing, machine learning for data analysis, data visualization, and data-driven decision-making within the context of big data. Additionally, we explore the role of essential big data technologies like Hadoop and Spark in enhancing data science practices. Through a structured analysis, this paper aims to shed light on the synergies and challenges associated with the integration of data science and big data technologies.

Keywords: Data Science, Big Data, Data Preprocessing, Machine Learning, Data Visualization, Hadoop, Spark, Data-Driven Decision-Making, Insights

Authors: Ankita Sharma, Tanishka Mehta, Dr. K. R Kapadiya

Abstract: This paper presents an innovative approach to address the growing concerns of energy consumption by introducing smart energy management techniques based on the Internet of Things (IoT). With the rise in connected devices and the integration of IoT technologies, this research focuses on the development and implementation of strategies aimed at minimizing electricity usage in various contexts. The proposed system utilizes IoT sensors and data analytics to monitor, analyze, and optimize energy consumption patterns in real-time. Through intelligent automation and informed decision-making, the system identifies opportunities for energy savings, reduces wastage, and contributes to a more sustainable and cost-effective electricity management framework. This research not only explores the potential of IoT in energy minimization but also provides valuable insights into the future of smart energy management.

Keywords: Smart Energy Management, Internet of Things (IoT), Electricity Minimization, Energy Efficiency, Connected Devices, Data Analytics, Sustainable Technology, Automation, Energy Consumption Patterns, IoT Sensors

Authors: Ayushi Shetty, Lalita Roy

Abstract: This paper provides a comprehensive analysis of three prominent front-end frameworks - React, Angular, and Vue. These frameworks have gained widespread popularity in web development, each offering unique features and advantages. The study involves an in-depth examination of their architecture, performance, ecosystem, and community support. Additionally, this paper includes tables and figures for a visual representation of key metrics, aiding developers and decision-makers in selecting the most suitable framework for their projects.

Keywords: Front-end development, React, Angular, Vue, Framework Comparison, Component-based Architecture, Virtual DOM, Two-way Data Binding, TypeScript, Performance Metrics, Ecosystem, Community Support.

Authors: Arayan Gupta, Puneet Gola

Abstract: In the era of information explosion, Big Data has emerged as a pivotal paradigm revolutionizing the way data is collected, processed, and utilized. This paper explores the multifaceted dimensions of Big Data, focusing on its architecture, diverse applications, and the paramount concern of security. The architecture section delves into the intricate frameworks and technologies that enable the storage, processing, and analysis of vast datasets. Highlighting the transformative power of Big Data, the applications section showcases its versatile role across various domains, including healthcare, finance, e-commerce, and more. However, as the prominence of Big Data grows, so does the need for robust security measures. The security aspect scrutinizes the challenges and strategies associated with safeguarding sensitive information within the Big Data ecosystem. This paper aims to provide a comprehensive overview of Big Data, shedding light on its architecture, applications, and the imperative need for security measures in the ever-evolving digital landscape.

Keywords: Big Data, Architecture, Applications, Security, Data Analytics, Information Technology, Data Storage, Cybersecurity, Data Privacy, Machine Learning.

Authors: Praveen Rai, Vijneesh Lokhande

Abstract: In the ever-expanding digital landscape, the efficient distribution of video files has become a critical challenge. This paper introduces a systematic approach to optimize video file distribution through the implementation of the Odds Algorithm. The Odds Algorithm, a probabilistic method, is employed to enhance the allocation and delivery of video content across diverse networks. The systematic approach outlined in this paper aims to address the complexities of video file distribution, considering factors such as bandwidth constraints, network latency, and user demand. By leveraging the Odds Algorithm, our proposed system optimizes the distribution process, resulting in improved resource utilization and enhanced user experience. This research contributes to the advancement of video distribution systems, offering a practical and effective solution to the challenges associated with large-scale content dissemination.

Keywords: Video File Distribution, Odds Algorithm, Optimization, Content Delivery, Network Efficiency, Bandwidth Management, Systematic Approach, Digital Content, Probabilistic Methods, User Experience.

Authors:Salim Sheik, Deepak Joshi, Birendar Sirpali

Abstract:Stream processing has become increasingly important in the world of data analytics and real-time decision-making. This paper explores the integration of Apache Kafka and Apache Spark, two popular open-source frameworks, to build robust and scalable streaming data processing pipelines. We discuss the architecture, key components, and use cases of this powerful combination, highlighting its capabilities for handling real-time data at scale.

Keywords:Streaming data processing, Apache Kafka, Apache Spark, Real-time analytics, Data integration, Stream processing, Big data, Use cases, Event-driven architecture, IoT data processing, Fraud detection, Log analysis Data pipelines, Structured Streaming, Machine learning, In-memory computing, Data abstractions, Checkpointing, Fault tolerance

Authors:Surbhi Swaraje, Ankita Singh

Abstract:Cross-platform app development has become increasingly popular as it allows developers to create applications that run seamlessly on multiple operating systems using a single codebase. Flutter, a framework developed by Google, has gained significant attention for its ability to streamline cross-platform app development. This paper provides a comparative analysis of Flutter, focusing on its strengths, weaknesses, and key differences when compared to other popular cross-platform frameworks. The objective is to offer insights into when Flutter is the ideal choice for cross-platform development and when alternative frameworks may be more suitable.

Keywords:Flutter, Cross-platform App Development, Comparative Analysis, React Native, Xamarin, Mobile App Development

Authors:Rajat Maheshwari, Mahesh Brahmin, Ritivik Negi

Abstract:Connected cars have become a prominent feature of the modern automotive landscape, promising enhanced convenience, safety, and efficiency. However, as vehicles become increasingly connected and autonomous, they also become susceptible to a wide range of cyber threats. This paper explores the challenges and innovations in securing the connected car ecosystem. We examine the evolving threat landscape, the unique vulnerabilities of connected vehicles, and the solutions and innovations that are being developed to safeguard the future of automotive transportation.

Keywords:  Connected cars, Automotive cybersecurity, Threat landscape, Cybersecurity standards, Intrusion detection systems, Blockchain technology, Secure software development, Hardware security modules, Internet of Things (IoT), Vehicle-to-everything (V2X) communication, Malware, Ransomware