Papers by Balkrishna Yadav
International Journal of Artificial Intelligence of Things (AIoT) in Communication Industry, 2025
Cybersecurity is crucial for safeguarding data, networks, and systems against cyber threats. Howe... more Cybersecurity is crucial for safeguarding data, networks, and systems against cyber threats. However, cybersecurity solutions may not always align with existing hardware and software, potentially leading to security vulnerabilities. The advancements in artificial intelligence (AI) have significantly improved cyber-attack detection by leveraging machine learning, deep learning, and reinforcement learning algorithms. This article reviews various AI-driven approaches for cyber-attack detection, examining their efficiency and effectiveness. Additionally, various cryptographic techniques, including symmetric, asymmetric, and homomorphic encryption, are explored for security enhancement. Authentication schemes such as multifactor authentication, digital signatures, and biometric authentication are also studied to assess their reliability in securing sensitive information. The performance of these algorithms is analyzed based on key parameters, including accuracy (96.24%), positive predictive value (96.10%), hit rate (95.80%), F1 score (95.95%), error rate (21.33%), encryption time (96 sec), and decryption time (85 sec). The findings indicate that deep learning-based deep neural networks (DNN) outperform other models in cyber-attack detection. Additionally, the data encryption standard (DES) algorithm provides improved security, while biometric authentication offers enhanced security and user convenience compared to traditional authentication methods. This study highlights the effectiveness of AI and cryptographic approaches in strengthening cybersecurity frameworks.
Concept, Planning and Implementation of Rainbowing During Maintenance Dredging for the Improvement of Navigable Depths in a Channel Leading to Haldia Dock of Kolkata Port Trust
EasyChair, Feb 3, 2021
Scientific International Publishing House (SIPH), 2024
Artificial Intelligence (AI) has become a transformative force in the modern era, revolutionizing... more Artificial Intelligence (AI) has become a transformative force in the modern era, revolutionizing industries and redefining human interaction with technology. From autonomous vehicles to personalized healthcare, AI-powered systems are shaping the future of innovation and efficiency. This abstract explores the significant advancements in AI technologies, including machine learning, natural language processing, and computer vision, and their implications for society, economy, and ethics.
International Multi-disciplinaryJournal of Applied Technolo, 2024
Multimodal generative artificial intelligence (MGI) is a field that combines text, image, and aud... more Multimodal generative artificial intelligence (MGI) is a field that combines text, image, and audio data to produce more comprehensive and richer outputs. It has applications in various industries such as human-computer interaction, entertainment, and healthcare. However, MGI must overcome challenges such as high computational costs, alignment of data types, and ensuring output consistency and coherence. The field's foundations are found in machine learning, audio processing, computer vision, and natural language processing (NLP). Diffusion models, Transformers, VAEs, and GANs are important study fields in MGI. Models like DeViSE, multimodal fusion methods, and shared multimodal embeddings like CLIP and ALIGN are essential for cross-modal learning and representation. Text-to-image models have been created to produce high-resolution pictures from textual descriptions, while models like the Tacotron, VALL-E, and Jukebox have investigated the relationship between text and audio. Applications include virtual assistants, human-computer interaction, and creative material creation. The study aims to investigate the current level of machine learning (MGI) state-of-the-art, examine key image, text, and audio integration methodologies and models, and identify obstacles and possibilities in this rapidly developing area. It also addresses technological challenges such as data alignment, high computational costs, and model consistency, as well as ethical issues like bias, fairness, and privacy. Multimodal generative AI (MGIA) combines the advantages of GANs and VAEs to produce superior quality outputs. Examples of applications include multimodal translation, cross-modal synthesis, VAE-GAN architecture, autoregressive models, self-supervised and contrastive learning models, and truly multimodal models. However, MGIA poses several challenges, such as exorbitant computing costs, dataset biases across modalities, data and privacy concerns, cross-modal alignment, and ethical and social consequences. To address these challenges, more modalities should be integrated, crossmodal learning strategies should be improved, semi-supervised and unsupervised approaches should be investigated for multimodal tasks, scalable and effective training strategies should be developed, and ethical AI frameworks should be created. In conclusion, multimodal AI has the potential to solve ethical issues while reshaping various sectors, improving content creation, and enhancing
International Journal of Advanced Control and System Engineering, 2024
By combining sophisticated robotics and visual awareness, computer vision operated robotic arms h... more By combining sophisticated robotics and visual awareness, computer vision operated robotic arms have revolutionized technology. These devices are having a profound effect on several industries, from manufacturing processes to healthcare. Computer vision controlled robotic arms are altering the game with their ability to see, comprehend, and interact with their surroundings. In this study we have tried to develop and implement software and hardware's to improve the freedom of movement in a robotic arm which will have a similar look to hand gestures. In the modern world, robotic arms are becoming essential, with uses in a wide range of sectors including military, defense, healthcare, and industrial automation. These amazing devices can replicate the movements and hand gestures of a human, which makes them incredibly useful in a variety of scenarios. The most innovative automation technology available now is robotic systems. Initially, robots were employed on manufacturing floors in the 1960s and early 1970s. Evaluating the precision of system was done by sort of methods such as sorting, kinematic modelling, and Centre recognition which elevate the movements of robotic arms. More innovation if performed to elevate the free movement of robotic arms can enhance its applications at defense and industrial level.
International Journal of Electronics Automation, 2024
A proposed AI system is used to grade exams automatically. It addresses inefficiencies in human a... more A proposed AI system is used to grade exams automatically. It addresses inefficiencies in human assessment. A GPT model trained on graded replies is used for evaluation, and TrOCR is used for precise handwritten text recognition. Efficiency and less bias are provided by this method, although there are still issues. More work is needed to assess open-ended questions and make sure they are understandable. To automate many aspects of exam evaluation, including grading, feedback, and plagiarism detection, it first examines the evolution of AI technologies, including machine learning, deep learning, and natural language processing. It also examines the potential for AI-driven assessment tools to enhance learning outcomes, reduce teacher workloads, and provide students with personalized feedback. Additionally, the study highlights several challenges, such as addressing. Our algorithm makes use of developments in two important fields of AI. To reduce bias, careful curation of training data is required. In its conclusion, the study emphasizes how important it is that the system be able to handle different question formats, deal with ambiguities, and incorporate human assessment. A promising first step toward an efficient, equitable, and AI-powered exam grading system is this research.
International Journal of Engineering and Management Research , 2024
Machine learning (ML) is an AI technology that creates programs and data models that can perform ... more Machine learning (ML) is an AI technology that creates programs and data models that can perform tasks without being instructed. It has three major types: guided learning, uncontrolled learning, and reinforcement learning. ML is essential for big projects like real-time decision-making systems and self-driving cars, robots, and drones. It improves AI systems by making it easier to create models, work with data, and run algorithms. ML algorithms have different types of learning, require different amounts of data and training times, and can be improved by tuning hyperparameters. Techniques like feature selection, dimensionality reduction, model editing, and compression can improve performance and accuracy in various fields. In the real world, making AI apps more efficient can lead to more options, lower prices, and faster processing. Key techniques like model compression, transfer learning, and edge computing are needed to achieve these goals.
International Journal of Engineering and Management Research , 2024
Radio Frequency Identification (RFID) is used to identify the characteristics of an object wirele... more Radio Frequency Identification (RFID) is used to identify the characteristics of an object wirelessly using radio waves. This project combines the power of RFID technology with real-time health monitoring in a hospital setting. Utilizing RFID tags for patient identification and asset tracking, the system seamlessly integrates with temperature sensor, blood pressure sensor and SpO2 sensor. The collected data is then transmitted to an Internet of Things (IoT) platform for continuous monitoring. The system not only ensures accurate patient identification and efficient asset management, but also provides real-time oxygen saturation, blood pressure rate and temperature data. This information is displayed on the LCD screen, offering healthcare professionals immediate insights into patient health. This innovative solution enhances patient care, promotes proactive medical interventions, and exemplifies the IOT in advancing healthcare.
International Journal of Science and Research (IJSR), 2024
Explainable AI (XAI) refers to a specific kind of artificial intelligence systems that are intent... more Explainable AI (XAI) refers to a specific kind of artificial intelligence systems that are intentionally built to ensure that their operations and results can be comprehended by humans. The main objective is to enhance the transparency of AI systems' decisionmaking processes, allowing users to understand the rationale behind certain judgements. Important elements of XAI include transparency, interpretability, reasoning, traceability, and user-friendliness. The advantages of Explainable Artificial Intelligence (XAI) include trust and confidence in the system's outputs, ensuring accountability and compliance with regulations, facilitating debugging and refinement of the model, promoting greater cooperation between humans and AI systems, and enabling informed decision-making based on transparent explanations. Examples of XAI applications include healthcare, banking, legal systems, and autonomous systems. Healthcare guarantees that AI-powered diagnosis and treatment suggestions are presented in a straightforward and comprehensible manner, while finance offers explicit elucidations for credit score, loan approvals, and fraud detection. Legal frameworks promote transparency in the implementation of AI applications, therefore assuring equity and mitigating the risk of biases. As artificial intelligence becomes more embedded in society, the significance of explainability will persistently increase, guaranteeing responsible and efficient utilization of these systems. The study of explainable AI is essential as it tackles the ethical, sociological, and technical difficulties presented by the growing use of AI systems. The level of transparency in AI decision-making processes has a direct influence on accountability, since systems that are not transparent might hide the reasoning behind the judgements. Explainability is crucial for detecting and reducing biases in AI systems, so preventing them from perpetuating or worsening social injustices. The objective of the study is to ascertain significant ethical concerns, comprehend the viewpoints of stakeholders, establish an ethical framework, and provide suggestions for policies. The incorporation of Explainable AI into different industries has a significant and far-reaching effect on both technology and society. This includes potential benefits such as increased trust and acceptance, adherence to regulations, improved AI development and troubleshooting, ethical AI design, empowerment and equal access, advancements in education and collaboration, changes in skill requirements, and the establishment of new ethical guidelines.
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Papers by Balkrishna Yadav