Authors - Twinkle K. Patel, Ankit Bhavsar, Arpit Jain Abstract - Automatic Speech Recognition (ASR) systems have become essential tools for the communication in natural human-computer communication across various languages. The growing need for effective communication in public and private sector has increased the need of Natural Language Processing. Gujarat is one of the prominent state in India where most of the official communication takes place in Gujarati language. ASR for Gujarati language has its own importance due to the high usage of Gujarati language. This paper, highlights the emergence of ASR for Gujarati language from the early days of creating ASR commercially to more modern advancement approaches such as deep learning, end-to-end (E2E), and various other methods. Traditional ASR systems utilized statistical methods, most commonly Hidden Markov Models (HMM) and Gaussian Mixture Model (GMM) which often reported low accuracy. With the emergence of deep learning models, including CNN, BiLSTM, and transformer-based models such as Wav2Vec 2.0 and XLSR-53, the recognition accuracy has significantly increased, especially in noisy and spontaneous speech contexts. The paper also provide the comparative study of the work done by various researchers in the field of Gujarati ASR, highlighting that the use of spell correctors as well as hybrid feature extraction methods have reduced phonetic ambiguities and diacritic errors in Gujarati language. The study strongly emphasizes the need for morphological analysis and contextual modeling to adequately address the complexity in the homophones and diacritics of Gujarati language. The paper also reviews the various methods applied to Gujarati ASR and their reported Word Error Rates.
Authors - Prathilothamai M, Nitin Ravi, Rishi Pradeepkumar, Sajith Rajan P, Anurag Nagilla Abstract - Code analysis ensures software quality, readability, and maintenance. Traditional methods such as Static Analysis Tools (SAT) and SZZ algorithms recognize errors and analyze contributions based on predefined rules and historical trends. However, complex code semantics are difficult to understand and often lead to overlooked issues and limited feedback. Despite its benefits, LLM is not practical in some business environments, facing challenges such as high computing costs, slower processing, and data protection concerns. Hybrid models may provide scalable and inexpensive solutions for large enterprise projects. This study contrasts SATs and LLMs, focusing on capabilities to detect bugs and maintain code quality, by investigating hybrid models that fuse the contextual strength of LLMs with the efficiency of SATs. In evaluations with 30 participants, the hybrid model consistently received the highest scores across accuracy, clarity, relevance, and usability (average ratings >8.5/10), outperforming both SATs and AI-alone approaches. Hybrid models may thus provide a scalable and cost-effective solution for problems arising in large-scale corporate enterprise projects. This study supports the use of case studies and benchmarks to assess the effectiveness, cost, and scalability of these various approaches. The findings aim to give practical suggestions for improving traditional code review processes, helping balance rule-based tools with AI-powered insights to enhance software development workflows.
Authors - T. A. Alka, M. Suresh Abstract - The study aims to examine the link between social innovation (SI) and sustainable entrepreneurship (SE). The major focus is to explore how SI is promoted by the SE and what the Sustainable Development Goals (SDGs) are covered by the sustainable entrepreneurship through promoting social innovation identified through a research gap by adopting the TCCM framework. The study is a systematic literature review on the publications in the Scopus database identified through keywords relevant to the theme by adopting the SPAR-4-SLR protocol. The result indicates that the sustainable entrepreneurs promoting SI innovation through innovative business models ensure social well-being. The SI is not only the technological but also non-technological aspects, which are responsible and impactful, having innovative approaches, social practices, and processes. The SEs are developing business models that are socially and sustainability purpose-driven, offering financial advantage and ensuring the social cohesion and the empowerment of the community through their participatory approaches and ensuring the local level stakeholders’ engagement. SE develops partnerships and collaboration among the different actors, which will create knowledge sharing and collaborative innovation. While mapping of SDGs to SI by SEs, the study identifies that the major SDGs covered are: SDG 1, SDG 4, SDG 5, SDG 8, SDG 9, SDG 10, etc. The study offers implications to practitioners, policymakers for actionable strategies to enhance the SI-driven SE ecosystem. This systematic review lacks empirical findings and relies purely on the published works. The limitations will open future research scope avenues by covering more databases, keywords, etc.
Authors - Shaveta Thakral, JyotiVerma, Pratima Manhas, Shankar Amalraj, Neha Shahare, Monali Tingane, Aparna Mahesh Torade Abstract - Urbanization and increasing housing density have necessitated innovative solutions to address both space constraints and accessibility for individuals with limited mobility. This paper presents a novel concept of an integrated lift-toilet system, designed specifically for elderly and disabled individuals living in multi-story buildings. By merging the functionalities of vertical transportation and sanitary facilities, the proposed system aims to provide dignity, convenience, and emergency usability, particularly in high-rise residences and healthcare environments. The research encompasses a multidisciplinary approach, including mechanical design, sanitary engineering, user-centric ergo-nomics, and real-world application assessments.
Authors - Jyotika R. Yadav, Arpit A. Jain, Ankit Bhavsar Abstract - Efficient traffic management is a major focus in smart city projects. Intelligent Transportation Systems (ITS) are revolutionize the way transportation networks are managed, to improve safety, efficiency, and sustainability. Machine learning (ML) has been developed as a powerful means for advancing ITS by enabling data-driven decision-making, real-time traffic management, predictive analytics, and automation. This paper offers a thorough summary of the current trends and advancements in the utilization of ML techniques in ITS. It explores key areas such as traffic flow prediction, autonomous vehicles, congestion management, smart mobility solutions, and road anomaly detection.
Authors - Saranya P., Rejikumar G. Abstract - This study employs a systematic literature review (SLR) to investigate the changing landscape of smart labelling in the food industry. As Customer demand for transparency, traceability, and ethical consumption increases, conventional food labels are evolving into interactive communication instruments. Smart labels facilitated by technologies such as QR codes, RFID, and intelligent packaging are now integral to real time customer interaction and business decision making. Despite increasing scholarly interest, a thorough delineation of research trends, deficiencies, and prospective directions has been absent. To tackle this, adoption of SPAR-4-SLR protocol was done to examine 43 peer reviewed papers (2015-2025) from Scopus utilizing Biblioshiny and Vosviewer. The investigation identified few predominant topics like food safety, customer behaviors, and technological integration while also highlighting under discovered topics like RFID and packaging designs. Result indicated a shift from a compliance orientated labeling to an interactive digital approach that foster consumer trust. This review provides essential in-sights for scholars, legislators, and industry executives seeking to create data-driven, customer-focused labelling systems in the digital era.
Authors - Abhishek Passwan, Srushti Karki, B M Bandgar Abstract - Accurately predicting stock prices remains a challenging task, primarily due to the volatile, dynamic, and non-linear nature of financial markets. This study introduces a robust deep learning-based framework designed to forecast the closing prices of companies listed on the Nifty 50 index. We explore and compare the predictive performance of several state-of-the-art neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), alongside two novel hybrid models: StockAugNet-f and StockAugNet-c. Historical stock data were retrieved from Yahoo Finance and pre-processed using Min-Max normalization. To effectively model temporal dependencies, we applied a sliding window technique for sequence construction during training. Model performance was assessed using key evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To enhance usability and support real-time forecasting, we developed an interactive Streamlit-based application, allowing users to dynamically visualize model outputs. Experimental findings indicate that the GRU and RNN model give better accuracy over the hybrid models accuracy and resilience for the NIFTY 50 index stocks.
Authors - Kumar Gaurav, Sudhir Agarmore, Akshay Kumar, Akshat Jha, Edidiong Akpabio, Chandan Kumar Abstract - Network architecture is examined in-depth in terms of the structural design or the underlying protocols that help in the facilitation of data transmission between networks. The paper discusses the classic models of network architecture, which include P2P, client-server, and hybrid models, and outlines the latest advancements in cloud computing, edge computing, and IoT. The paper further discusses the different types of network topologies and other devices, such as the OSI and TCP/IP models, in the shaping of data communication. This also encompasses new emerging trends, such as 5G, IoT, quantum networking, and zero-trust security, among many others. These will detail discussions of the concepts involved as well as their impacts on network protocols and architecture. As demand for faster, more secure, and scalable networks grows, the integration of these modern technologies opens the way to new opportunities and challenges of future network infrastructure.
Authors - Janani Vijayan, Bala Subramanian R Abstract - This study explores the growing body of research on leader’s gratitude, emphasizing its significance in organizational settings. Gratitude, a complex and multifaceted positive emotion, plays a crucial role in enhancing interpersonal relationships, promoting well-being, and fostering prosocial behavior. The review categorizes gratitude into trait, state, and collective forms, examining their unique characteristics and implications. It highlights key theoretical frameworks such as the Find-Remind-and-Bind Theory, Broaden-and-Build Theory, and Cognitive Emotion Theory to explain how gratitude functions within leadership dynamics. The review further delves into the antecedents of leader’s gratitude, including team error management, feedback, and appreciation programs and its consequences, such as enhanced social intelligence, team innovation, improved leader-member relationships, and increased organizational commitment. Moreover, the paper discusses the broader social impacts of leader gratitude, showing how it fosters a positive work culture. Identified limitations include the scarcity of causal studies and underrepresentation of cultural factors. Future research directions are proposed, such as exploring gender differences in gratitude expression, cultural variations, and the effectiveness of different gratitude expressions. Overall, the review underscores that leader’s gratitude is a powerful yet underutilized tool in cultivating effective leadership and healthy organizational environments.
Authors - S. Kirthica, K. Subha, S. Arul Murugan, V. Deepak Kumar, M. Dhivagar, S. Kanishka Abstract - In contemporary society, effective grievance redressal is vital for transparency, accountability, and public trust. Traditional systems often face issues like manual processing, delays, and poor user engagement. To overcome these challenges, we propose VOICE, an AI-driven grievance management platform that automates the complaint lifecycle—from classification and similarity detection to assignment and tracking. Leveraging natural language processing (NLP), speech-to-text, and automated prioritization, VOICE reduces human intervention and improves response times. It also incorporates community-driven upvoting, real-time notifications, and performance monitoring to enhance user engagement and system transparency. Designed for scalability and ease of use, VOICE can be deployed across institutions, communities, and national systems. By combining automation, intelligence, and user-centric features, VOICE represents a significant advancement in modern grievance redressal.
