Authors - Manjusha Pandey, Rajeev Kumar, Satyam Tiwary, Yuvraj Singh, Oindrella Chatterjee, Siddharth Swarup Rautaray Abstract - This paper delves into the complexities of providing equitable access to multimedia content across India's diverse linguistic landscape. It proposes innovative strategies for translating English video content into Indian regional languages, leveraging cutting-edge technologies such as machine translation, speech recognition, and text-to-speech synthesis. The suggested approach involves a systematic four-phase process, encompassing audio separation, text conversion, machine translation, and speech synthesis. [1] By utilizing open-source tools like IBM's Watson supercomputer and the Flite engine from Carnegie Mellon University, the system achieves a commendable 79% accuracy in terms of naturalness and fluency, as evaluated by native speakers. However, challenges persist in handling multi-speaker conversations and accommodating a broader range of Indian languages. Despite these limitations, the research lays a solid foundation for future advancements in the field. By fostering cross-cultural communication and knowledge dissemination, the proposed solution holds the potential to bridge linguistic barriers, empower marginalized communities, and foster an inclusive digital ecosystem in India.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - This paper explores the phenomenon of Artificial Intelligence (AI) transformation in organizational culture evaluation, discussing capabilities, advantages, obstacles and future direction. While traditional means of mining forms like surveys and interviews are often lengthy and flawed due to human biases, AI tools rely on real-time data, natural language processing, and predictive analysis to deliver objective insights instantly. Such applications, including sentiment analysis, behavioural analytics, and cultural diagnostics, allow organizations to mitigate cultural misalignments in advance at the organizational level or within specific teams, idem for the employee's engagement and inclusivity. Nonetheless, ethical issues related to data privacy, security and algorithmic wage discrimination continue to pose significant challenges. The implications of this study highlight the increasing importance of artificial intelligence in enabling organizations to build dynamic, resilient, and agile organizational cultures.
Authors - Sakshi Sharma, Tanisha Verma, Shailesh D. Kamble Abstract - Accurate, timely detection of plant disease is critical to protect crop from being damaged and increase agricultural productivity. Many disease identification methods are labor intensive and only practical with an expert set of trained eyes. A mobile application for real time plant disease detection using CNNs presented in this paper allows farmers to have a simple yet powerful access to a diagnostic tool. CNN was trained on a big collection of plant leaf images to discriminate between diseases using Keras and TensorFlow. The application was built using Flutter for cross platform mobile development, trained model deployed on mobile devices using TensorFlow Lite, which allows offline inference. Users can capture images of affected plant leaves and get immediate diagnostic feedback as to the potential disease involved. Following data preprocessing and model optimization, the application uses a lightweight architecture that achieves high accuracy while meeting requirements for mobile deployment. This research shows integration of AI with mobile technology can provide a scalable, efficient and accessible solution to crop disease detection. The system as proposed is capable of improving crop health management, reducing losses, and working towards global food security.
Authors - Utkarsha Wanjari, Shubham Kadam Abstract - Gamification in HRM through AI is thus a total revolution that can maximize the engagement and productivity of employees. Game-like qualities such as rewards, badges, leaderboards, and challenges incorporated in the HR processes create a captivating environment that motivates and pushes an employee into an achievement culture. AI amplifies the effect of gamification: it enables data-driven insights, personalized experience, and real-time feedback loops. The paper also looks into the psychological underpinnings of gamification intrinsic and extrinsic motivation and their alignment with the organizational goals. It analyzes some of the challenges in incorporating gamification, including ethical considerations, potential overuse, and the balance between entertainment and productivity. It also reflects on some success stories and presents a pathway to implementing gamified AI solutions into the existing HR framework. This is because gamification, combined with AI, will alter the way human resource practice prevails, uplift employee productivity, boost employee satisfaction, and contribute to the long-term success of an organization. The present research study aspires to provide business organizations with the actionability of a very innovative method to remain ahead of their game in the changed wilderness of the workplace.
Authors - Shubham Kadam, Chhitij Raj, Pankajkumar Anawade, Deepak sharma, Utkarsha Wanjari Abstract - The paper investigates Green ICT leadership in e-governance towards carbon footprint mitigation from the digital government. E-governance uses information and communication technology (ICT) to deliver administrative services through enhanced technology in this service chain, thus increasing the efficiency of their services, which is guided by an aim for complete transparency that requires accurate information. However, digitalization is responsible for environmental problems such as carbon emissions produced by data centres, digital infrastructure, and devices. The paper emphasizes the importance of vision-oriented leadership in promoting sustainability through processes of strategic thinking, collaboration and innovation. The Guide presents a series of critical strategies, including energy-efficient data centres, virtualization and cloud computing, sustainable procurement, and citizen engagement to build green practices. Innovative technologies such as AI, IoT, and blockchain are labelled enablers for optimizing energy consumption and increasing transparency.
Authors - Alpa R. Barad, Ankit R. Bhavsar Abstract - Analysis of grass quality is essential to improve cattle health. To improve animals' health and productivity, it is necessary to survey quality food. Grass is a primary and major source of food for every cattle. As a part of vegetation quality of grass is decreasing day by day, and it’s also not possible to survey fresh grass on a daily basis. Proposed research is used to analyze the quality of grass based on its color space. The quality of grass differs over the grass species and weather, and it's become more difficult with a single model to recognize its quality. To solve this problem proposed research uses machine learning based hybrid approach. The proposed research uses Median filter with kmeans clustering. Based on the clustering, the Simulation uses color deflection code to identify threshold values for a given species of grass. Proposed research finds the remarkable performance of three different qualities of grass. Simulation of study uses a Wiener filter and data augmentation to identify the impact of the proposed k-means based hybrid approach for grass quality recognition.
Authors - Aryan Jain, Shrirang Joshi, Vatsal Jain, Dinesh Kumar Saini Abstract - 5G network roll-out is expanding globally, which further shows that low-cost and good modem design remains to be absolutely integral. Scaling here is tough, not to mention the complexity and cost of production involved in traditional hardware-based 5G modems. This analysis explores how advances such as Open Radio Access Networks (Open RAN), Software-Defined Networking (SDN) and Network Function Virtualization (NFV) could reduce the hardware requirements, leading to lower costs for 5G modems. We marvel over the functionalities which we take for granted in a modem, such as digital signal processing and base-band processing, are being virtualized so that it is done on general-purpose hardware rather than on parts custom designed to do these particular tasks. Adopting cloud-native and software-based solutions for these traditional hardware-driven processes can bring huge savings without compromising on performance. In addition, we discuss Dynamic Resource and Change in Network efficiency which are improved by Modem Allocation, Edge Computing, Network Slicing — SDN NFV open day light. This collection of methods is described in a comprehensive article on the application of virtual network technologies to improve 5G modem design, reduce deployment costs, and enable more flexible, scalable, and energy-efficient 5G solutions.
Authors - Mallu Praneeth Reddy, T. A. S. Vardhan, Kura Bhargava Gupta, Nagireddy Deekshitha, Pudari Shrainya Goud, Khalvida Pamarty, Sushama Rani Dutta Abstract - This paper aims to predict the calories burnt by a person using machine learning models built on several regression algorithms like Linear, Random Forest, XGBoost,and CatBoost based on gender, age, height, weight, duration of exercise, body temperature, and heartbeat of the person. In addition, the analysis compares the algorithms based on performance metrics like MAE (Mean Absolute Error), MSE (Mean Square Error), and R2 score and determines the most effective algorithm for calorie prediction.
Authors - Shailender Vats, Prasadu Peddi, Prashant Vats Abstract - Blockchain technology's explosive growth has created previously unheard-of potential in several industries, but it has also revealed fresh security flaws. To improve threat detection and response mechanisms, this paper provides a complete intrusion detection system (IDS) designed especially for distributed blockchain ledger security. It makes use of sophisticated smart contracts. We demonstrate the efficacy of the suggested IDS in detecting possible intrusions while preserving the integrity of the blockchain environment by validating it using simulation-based scenarios. According to the research, combining IDS with blockchain technology and smart contracts greatly improves security and is a viable way to address current cybersecurity issues.
Authors - M Nanda Kumar, Harsh Sharma, Rajan Kakkar, Tushar Naha, Atul, Rishabh Yadav, Naveen Abstract - The demand for autonomous vehicles (AVs) has grown rapidly due to their potential to revolutionize transportation by enhancing safety, efficiency, and convenience while reducing human error, a leading cause of road accidents. AVs leverage advanced technologies like machine learning, LIDAR, GPS, cameras, RADAR, and ultrasonic sensors for precise navigation, obstacle detection, and real-time decision-making. However, their reliability and safety in di-verse environmental conditions remain a significant challenge. Extreme weather events such as heavy rain, snow, fog, ice, hail, and dust storms can impair sensor performance, reducing visibility, traction, and the ability to detect road markings, obstacles, and other vehicles. These conditions degrade the accuracy of critical systems like LIDAR, RADAR, and cameras, raising concerns about AVs’ reliability, particularly in emergencies or unpredictable scenarios. This review paper explores the effects of adverse weather on AVs’ performance, analyzing the limitations of key sensors and assessing various mitigation strategies to enhance their resilience. By identifying technological gaps and emphasizing the need for weather-resilient solutions, the paper aims to guide future research and innovation to improve AVs’ safety and reliability in challenging real-world conditions, ensuring their readiness for broader deployment.
