Journal of Research in Information Technology and Its Applications ISSN: 3139-504X (Online)

Author: S. Haseena

Abstract: The best part of accessible traffic sign detection system make use of color or shape information, but the process stays limited in regard to identify and segmenting traffic signs from a complex background . In this paper, we intend a novel graph based traffic sign detection path that contains a saliency measure stage, multi-threshold segmentation stage and a graph based ranking stage. Since the graph based ranking algorithm with specific color and saliency associates the information of color, saliency, structural and dependent relationship of nodes , it is more particular and robust than the other systems in terms of use of various illumination state ,shape rotations and scale modify from traffic sign images . Additionally, the proposed multithreshold segmentation algorithm target of all nodes with a nonzero ranking score, which can efficiently solve problems such as various lighting conditions, complex background, occlusion and so on. The results for three public traffic sign position demonstrate that out proposed approach guide the better performance than the existing state of the art technique. Additionally, the results are suitable even for images hold traffic signs that have been rotated or experienced occlusion with the images that were photographed under dissimilar weather and illumination state.

Keywords: Graph based image segmentation, Graph analysis, traffic sign detection, support vector machine

Authors: AkankshaUpadhyay, AnkitaRawat, AyushSrivastava, MayankSinghFartiyal

Abstract:  In today’s world of growing technology internet plays a very important role in connecting everyone around the globe. Internet gave rise to a new concept known as Online Magazine. Today these magazines covers a wide range of topics such as sports, news, fashion, gadgets, college etc. Online College Magazines provide a platform for students to exhibit their talent. Online Fashion magazine updates its readers about the new trends in the world of fashion. Online magazines provides its readers with some leisure time as well as knowledge about latest trends, sports, glamour etc.

Keywords: Online magazine, bulletin board system, customization

Authors: V. Prasathkumar, A. Deepalakshmi, N.Ramkumar

Abstract: Classification is a step by step procedure for designating a given piece of input into any of the given category. Classification is an important Machine Learning technique. There are many classification problem occurs in different application areas and need to be solved. Different types are classification algorithms like lazy, tree-based, rule-based, etc are widely used. This work studies the performance of different lazy classifiers for classification of Multivariate data set from UCI machine learning repository using the open source machine learning tool. A comparison of different lazy classifiers used and a practical guideline for selecting the most suited algorithm for a classification is presented in this work. Apart from that some empirical criteria for describing and evaluating the best classifiers are given.

Keywords: Classification; IB1 Classifier; IBk Classifier; K Star Classifier; LWL Classifier

Authors: Aastha Bhagat, Arushi Jain, Shubhii Verma, Siddharth Aggarwal

Abstract: While the internet has long been a source of Doctor information, it has only recently been used for online private patient-doctor consultations. Online Doctor Consultation (ODC) provides a platform wherein patients can consult doctors directly without physically visiting them. It is now offered by many providers internationally with diverse models and features.

Keywords: Telemedicine, Remote Consultation.

Authors: Aarushi Gupta, Sakshi Agarwal

Abstract: The growth rate of India’s e-commerce industry is increasing day by day. Companies are using the internet to put across and spread the information. Simultaneously it helps the companies to give an idea about their products and instantly getting the feedback of customer’s fulfillment to get stimulated turnover for future. The primary objective of the current study is to understand the online buying behavior of consumers in India. It has also made an attempt to get information about the scope of improvement in online shopping website.

Keywords: On-line shopping, Jewellery Shopping, E-Commerce, Delhites

Authors: Ravi Gorli, Yamini G

Abstract: Farming is an occupation which is playing the ultimate role for survive of this world. It supplies maximum needs for the human being to live in this world. But in the advancement of the technologies with invention of Internet of Things, the Automation (Smarter technologies) is replacing the traditional methodologies which in cause resulting in wide range improvement of the Fields. Now we are in the state of automation where the up gradation of smarter technologies are improving day by day in maximum sectors starting from smart homes, garbage, vehicles, industries, Farming, health, grids and so on. In the field of Farming, the improvement with the implementation of Automation is also taking place with the invention of Internet of Things. The main idea of this paper is focused on the review of the improvement in the Smart Farming sector.

Keywords: Automation, DSS, ICT, IoT

Authors: Punam Khandar, Mohini Upasani

Abstract:A real-time system is required to complete its work and deliver its services on a timely basis. A system is said to be real-time if the total correctness of an operation depends not only upon its logical correctness, but also upon the time in which it is performed. Malfunctioning of some real-time systems can have serious consequences. We want such systems to work correctly and responsively. This paper comprehensively discusses applications of RTS. Real-time systems are used commonly in digital control, optimal control, command and control, signal processing, telecommunication system, tacking, real-time databases, multimedia, etc. Traditionally, realtime communications are used in hard and soft real time applications running in distributed process control applications such as power plants, avionics, chemical process control, automobiles, etc.

Keywords: RTS, logical correctness, hard and soft RTS.

Authors: Prince Raju, Snippy Valson, Tom Sebastian

Abstract: The convergence of modern technologies (especially machine learning) have unleashed a new era in the field of computer science and BCI. It can provide a way for physically disabled people to use computers. But there are close to none ways by which a normal person can get hold of such systems with a limited budget. Our project is primarily focused on using an EEG headset which is available and affordable to a normal person (less than $100) to provide a solution by which users can control a computer interface by mere thinking. We use an EEG headset from Neurosky which costs about $99. The headset is normally used for keeping track of the mental calmness of a person (hobbyist purpose). We have tweaked the output from the headset and uses its single channel raw EEG data together with its other sensor datas (eSense datas) to provide the brain computer interface.

Keywords: Mind Reading, EEG headset,

Authors: Rama Kant tipathi

Abstract: Cloud computing has emerged as one of the most transformative technologies for businesses across the globe. Small and Medium Enterprises (SMEs) are increasingly turning toward cloud-based solutions to improve operational efficiency, reduce infrastructure costs, and gain competitive advantage. However, despite the numerous advantages, cloud adoption in SMEs is not always straightforward. Challenges such as limited financial resources, lack of technical expertise, data security concerns, and vendor lock-in risks often influence the adoption decision. This paper provides a comprehensive review of cloud adoption strategies in SMEs, examining drivers, barriers, models of deployment, and implementation approaches. It also discusses strategic frameworks that SMEs can adopt to ensure successful migration and long-term sustainability. Through analysis of recent literature and industry reports, this study highlights best practices and provides a structured roadmap for SMEs considering cloud transformation. The findings suggest that a phased adoption strategy, supported by strong leadership and risk management practices, significantly improves success rates.

Keywords: Cloud Computing, SMEs, Cloud Adoption Strategy, Digital Transformation, SaaS, IaaS, PaaS, Risk Management, IT Infrastructure

Authors: Neha Kulshrestha, Arvind Kumar Sharma, Meenal Bansal, Sanjay Sekhawat

Abstract: Blockchain technology has emerged as a transformative innovation in financial transactions by enabling decentralized, transparent, and tamper-resistant record keeping. Traditional financial systems depend heavily on intermediaries such as banks, payment gateways, and clearing houses, which often results in high transaction cost, processing delays, and limited transparency. Blockchain introduces distributed ledger architecture that allows secure peer-to-peer transactions without central authority. This paper reviews the concept of blockchain technology in financial transactions, explores its architecture, applications in banking and digital payments, advantages, and existing challenges. The study also highlights regulatory concerns, scalability limitations, and adoption barriers faced by financial institutions. A comparative analysis between conventional and blockchain-based financial systems is presented through tables. The paper concludes that blockchain can significantly improve efficiency, trust, and financial inclusion, but large-scale adoption still requires technological maturity and regulatory clarity.

Keywords: Blockchain, Financial Transactions, Cryptocurrency, Distributed Ledger, Smart Contracts, FinTech, Digital Payments

Authors: Akhilesh Pandey , Sana Hasmi , Bijesh Pathak, Santosh Jha

Abstract: In the last decade, organizations across industries have experienced an explosive growth of data generated from digital platforms, transactions, sensors, and social media. This massive accumulation of structured and unstructured data, commonly referred to as big data, has significantly influenced the way strategic decisions are formulated and implemented. Big Data Analytics (BDA) provides advanced tools and methodologies to process, analyze, and interpret large datasets, enabling managers to make evidencebased decisions rather than relying solely on intuition. This research paper reviews the concept of big data analytics in the context of strategic decisionmaking, highlighting its components, applications, benefits, and challenges. It also explores the technologies that support analytics, such as machine learning, predictive modeling, and cloud computing. Furthermore, the paper discusses real-world applications in sectors including finance, healthcare, retail, and manufacturing. While big data analytics enhances competitive advantage and organizational performance, it also raises concerns related to data privacy, ethical considerations, data quality, and implementation costs. The study concludes that organizations that effectively integrate analytics into their strategic planning processes are more likely to achieve sustainable growth and innovation. However, careful governance and skilled workforce are essential to fully utilize its potential.

Keywords: Big Data, Strategic Decision-Making, Business Analytics, Predictive Modeling, Data-Driven Strategy, Competitive Advantage

Author: Om Prakash Sharma

Abstract: Artificial Intelligence (AI) has emerged as a transformative force in healthcare, particularly in medical diagnosis systems. AI-based diagnostic tools leverage machine learning algorithms, deep learning models, and natural language processing to support clinicians in detecting diseases, predicting outcomes, and recommending treatments. This review explores the current state of AI-based medical diagnosis systems, their architectures, applications, benefits, challenges, and ethical considerations. Emphasis is placed on AI models used in radiology, pathology, cardiology, and general clinical decision-making. The paper also highlights the role of big data and Internet of Things (IoT) integration in enhancing diagnostic accuracy. While AI offers significant improvements in efficiency, accuracy, and cost reduction, challenges such as data privacy, algorithmic bias, and regulatory compliance remain critical. This study aims to provide a comprehensive understanding of AI-based diagnostic systems, their practical applications, and future research directions.

Keywords: Artificial Intelligence, Medical Diagnosis, Machine Learning, Deep Learning, Clinical Decision Support, Healthcare Technology, Big Data, IoT.

Authors: Gajender Sharma, Manish Verma, Pooja Kulkarni, Arvind Chauhan

Abstract: Financial fraud has become a major challenge for banking and digital financial ecosystems due to rapid growth of online transactions, e-commerce, and mobile payments. Traditional rule-based fraud detection systems often fail to detect complex and evolving fraud patterns. Artificial Intelligence (AI) techniques such as machine learning, deep learning, and anomaly detection have emerged as powerful tools to enhance fraud detection accuracy and efficiency. This paper reviews the concept, techniques, applications, and challenges of AI-based fraud detection in financial systems. It explains how AI models analyse large transaction datasets, detect suspicious patterns, and support real-time monitoring. The study also highlights implementation issues such as data quality, model interpretability, privacy concerns, and adversarial fraud tactics. The paper concludes that AI-driven fraud detection significantly improves financial security and risk management, though integration with regulatory and ethical frameworks remains necessary.

Keywords: Artificial Intelligence, Fraud Detection, Financial Systems, Machine Learning, Anomaly Detection, Financial Technology

Authors: Neha Kulkarni, Ritesh Bansal, Farhan Qureshi 

Abstract: Artificial Intelligence (AI) has become a transformative technology in the service sector, enabling automation of routine, cognitive, and customer-facing processes. Service industries such as banking, healthcare, retail, hospitality, education, and logistics are increasingly integrating AI-driven systems including chatbots, robotic process automation (RPA), recommendation engines, predictive analytics, and intelligent scheduling tools. These technologies help firms to improve efficiency, reduce operational cost, enhance service quality, and provide personalized customer experiences. However, AIdriven automation also creates challenges related to employment displacement, ethical risks, algorithmic bias, and data privacy. This review paper examines the concept, drivers, applications, benefits, and challenges of AI-based automation in service industries. It also analyzes future trends and strategic implications for organizations and policymakers. The study concludes that AI automation is not merely a technological upgrade but a structural transformation of service delivery models, requiring workforce reskilling and governance frameworks to ensure inclusive and responsible adoption.

Keywords: Artificial Intelligence, Service Automation, Digital Transformation, Service Industries, Robotic Process Automation, Customer Experience

Authors: S.Gorantla, K. Selvaraj

Abstract: Mobile ad hoc network (Manet) is a kind of wireless ad-hoc network, and is a self configuring network of mobile outers connected by wireless links. It is a wireless network without infrastructure. Mobile devices are free to join or leave the network.Opportunistic data forwarding has not been widely utilized in mobile adhoc networks (MANETs) and the main reason is the lack of an efficient lightweight proactive routing scheme with strong source routing capability. PSR protocol facilitates opportunistic data forwarding in MANETs. In PSR, each node maintains a breadth-first search spanning tree of the network rooted at it-self. This information is periodically exchanged among neighboring nodes for updated network topology information.

Keywords: Mobile Adhoc Network, Proactive routing, Source routing, DSDV

Author: V. Nagraj, Samitasharma

Abstract: Software quality requirements are a main driver for architectural decisions of software systems. Although the need for iterative handling of requirements and architecture has been identified, current architecture design processes do not provide systematic, quantitative feedback for the prioritization and cost/benefit considerations for quality requirements. Thus, in practice stakeholders still often state and prioritize quality requirements before knowing the software architecture, i.e. without knowledge about the quality dependencies, conflicts, incurred costs, and technical feasibility. However, as quality properties usually are cross-cutting architecture concerns, estimating the effects of design decisions is difficult. Thus, stakeholders cannot reliably know the appropriate required level of quality. In this research proposal, we suggest an approach to generate feedback from quantitative architecture evaluation to requirements engineering, in particular to requirements prioritization. We propose to use automated design space exploration techniques to generate information about available trade-offs. Final quality requirement prioritization is deferred until first feedback from architecture evaluation is available. In this paper, we present the process model of our approach enabling feedback to requirement prioritization and describe application scenarios and an example.

Keywords: Software Quality Requirements, Software Architecture Evaluation, Requirements Prioritization. 

Author: Prashant Dongare

Abstract: In this paper, we present a survey on String Matching Algorithms for network security. It gives a summary of String matching algorithms by focusing on network security and its subjects are mainly focused on the literature. This paper presents different types of string matching examples and search algorithms. All algorithms are classified into different types based on their complexity. All these algorithms require memory space for storing the string which is used in their pre-processing phase. It requires a small amount of memory and shows better performance than any other algorithm for deep packet classification based on their payload data. String-matching algorithms are basic components which are used in implementations of practical software existing under most operating systems.

Keywords: String Matching, Experimental Algorithms, Text Processing, Automaton, Pattern

Authors: Prithvi M.G , Srinivasa K. , Kavitha M

Abstract: The concept of object-oriented modelling makes the real world represented in some kinds of logical forms. ER-diagram is one of those logical forms, which is used in database design. Tools are available to generate conceptual model from requirement specification and structured query language (SQL) queries from natural language queries. In this paper, we address the problem of generating natural language questions from conceptual model by converting the ERdiagram to an understandable format that is in text or sentence form and converting the obtained text into natural language queries (NLQ). This reduces the user efforts in typing NLQ to get the SQL queries needed to access data or information from database.

Keywords: ER-diagram, Natural language processing, NLQ.