Category Archives: Uncategorized

Authors: Ria Ranjan Kumar, Ms. Mitali Srivastava

Abstract: The growing adoption of Artificial Intelligence and its intervention in the legal sphere has been a welcome sight for everyone in the industry. From judicial institutions to corporations, AI has rapidly shown an overarching effect on judicial functioning. However, with this bargaining effect, what comes is the danger of Artificial intelligence. Due to the lack of regulatory mechanisms and compliance laws, a varied but unexpected stream of jeopardy is underway for all of us. This is somehow related to the growing correspondence being provided by human beings and the pervasive acceptance not just by the elite class of the world but across all classes. The only laws that are currently under the purview of this domain are the Digital Personal Data Protection Act, 2023, for privacy and personal data, the Information Technology Act, 2000 for cyber offences and intermediary liability, and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021. With this dissertation, I shall try to lay down an intricate evaluation of the recent AI interception and reflect on the dire need to incorporate it with a proper legal regulatory system. Although the term "artificial intelligence" is not used in the DPDP act, one crucial word mentioned therein is "automated," which refers to a digital procedure that can process data automatically. That is the closest the Act gets to discussing artificial intelligence. The act does not specifically address algorithmic bias, deepfakes, facial recognition, automated decision-making, AI explainability, or generative AI training data. There are a lot of inconsistencies in the law itself, and the dystopian tone of the act adds insult to injury.

DOI: https://doi.org/10.5281/zenodo.20069843

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Authors: Khushi Wadhwa, Rajat Takkar, Vaani, Kashish Sharma, Kartikay Singh Manhas

Abstract: In the world of medical imaging, Edge artificial intelligence (AI) is driving a revolution by enabling real-time analysis and diagnosis decision making. The aforementioned article examines the constantly developing subject of edge AI-powered healthcare imaging, describing the most recent ad-vancements, creations, and concepts that could transform a variety of medical fields by instantly interpreting medical images, which can be crucial in life-saving circumstances. The Edge AI can be used in remote clinics and other medical imaging situations. In rural diabetes camps, diabetic retinopathy can be diagnosed with Fundus cameras and point-of-care ultrasound without radiologists. In emergency situations, portable X-ray devices can diagnose fractures. The three main types of diagnos-tic procedures—imaging-based, pathology-driven, and protective diagnostic approaches—as well as the alterations and adaptations brought about by the application of Edge AI are also covered in this article. Using medical records raises several ethical issues because they are very sensitive documents. These challenges have also been discussed in this article. The necessity for further developments in Edge AI-based diagnostic techniques is also covered in the article. Additionally, there is a great deal of potential for the future in the creation of tools and techniques that are easy to use and incorporate into routine operations. The increasing usage of clinical decision support systems makes edge AI a promising topic in healthcare and diagnostics. Despite a number of obstacles to its application and adoption, the research concludes that Edge AI in healthcare has a promising future. However, in order to guarantee that facilities are available for this, a high degree of precision must be attained and patients must have better medical outcomes. The potential of AI to transform healthcare and enhance patient outcomes is also highlighted in this paper, with a focus on responsible implementation and ongoing assessment.

DOI: http://doi.org/

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Authors: Mahantesh M. Ganganallimath, Dr. K. Vizayakumar, Dr. Umesh M. Bhushi

Abstract: By providing cast components to the automobile, aerospace, railroad, construction, defense, and heavy engineering industries, the Indian foundry sector is essential to the manufacturing sector. Casting flaws, process unpredictability, material waste, high rejection rates, energy inefficiency, and growing international competitiveness are some of the industry's major obstacles. In this regard, sustainable industrial growth now depends on quality assurance and quality-centric methods. The necessity of methodical quality assurance procedures, process control systems, and continuous improvement techniques in Indian foundries is examined in this study. The study highlights that quality-driven systems enhance customer satisfaction and product dependability while simultaneously lowering costs and promoting long-term competitiveness and environmental sustainability. The combination of Industry 4.0, automation, and statistical quality tools for stable growth is further supported by recent research on KPI-driven foundry quality systems and sustainable control models.

DOI: https://doi.org/10.5281/zenodo.20068127

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Authors: Ch Naga Lakshmi, B Tejaswini, B Anusha, R Nandini, N Tasleem

Abstract: The increasing incidence of chronic illnesses, including cardiovascular and respiratory disorders, has emphasized the importance of continuous health parameter monitoring. Conventional systems for vital sign assessment are primarily hospital-based, costly, and limited to periodic medical consultations, which may delay the detection of abnormal physiological variations. To overcome these limitations, this paper presents a Smart Health Surveillance System designed using Internet of Things (IoT) technology integrated with low-cost biomedical sensors. The proposed model employs a MAX30100 pulse oximeter to measure heart rate and blood oxygen saturation (SpO₂), a DS18B20 digital sensor to record body temperature, and a Node MCU ESP8266 microcontroller to process and transmit data. Measurement outputs are displayed on an LCD screen, while IoT functionality enables remote monitoring through wireless connectivity. Experimental evaluation demonstrates that the system achieves a heart rate accuracy of ±3 bpm, a SpO₂ accuracy of ±2%, and a temperature accuracy of ±0.5 °C when compared with standard medical devices. The prototype’s affordability, portability, and reliability make it an effective solution for continuous home-based health monitoring, telemedicine services, elderly care, and epidemic surveillance. Future work aims to integrate additional sensors—such as blood pressure and ECG modules—and to utilize cloud-driven analytics for predictive and preventive healthcare applications.

DOI: https://doi.org/10.5281/zenodo.20067343

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Authors: A Chenna Kesava Reddy, K Apurupa, K Akhila, N Abhinaya, N Trisha5

Abstract: Cloud storage has emerged as the backbone of modern digital ecosystems, enabling seamless data access, sharing, and collaboration across individuals, enterprises, and government organizations. However, the centralized nature of conventional cloud architectures makes them vulnerable to critical security challenges such as data breaches, manipulation, unauthorized access, and single-point failures. To address these issues, this study proposes a hybrid intelligent cloud security framework that integrates Artificial Intelligence (AI) and Blockchain technologies. Blockchain ensures decentralized trust through cryptographic immutability, distributed consensus, and smart contracts that automate data access and policy enforcement without third-party intervention. Simultaneously, AI specifically Long Short-Term Memory (LSTM) networks is employed for anomaly detection, analysing user activity logs and behavioural patterns to identify irregularities or potential intrusions in real time. The system dynamically adjusts resource allocation and access privileges based on AI-driven insights, enhancing operational efficiency and security adaptability. Experimental evaluation demonstrates that the model achieves high performance in terms of accuracy, precision, recall, F1-score, latency, and throughput, validating its robustness and scalability under varying network conditions. By combining AI’s predictive intelligence with blockchain’s decentralized integrity, the proposed approach delivers a secure, transparent, and self-optimizing cloud storage framework suitable for data-sensitive domains such as healthcare, finance, e-governance, and smart industries.

DOI: https://doi.org/10.5281/zenodo.20067047

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Authors: Badisa. Adhilakshmi, Mula Srilatha, Golla Manusri, Bodepudi Tejaswini, Daggubati Maneesha

Abstract: Faking resumes is one of the greatest challenges in the contemporary recruitment systems where most applicants tend to embellish or lie about their academic and professional experience or technical abilities in order to have an advantage in employment. The manual verification systems are tedious, time consuming and they are also subject to error, which makes them ineffective in large-scale hiring. Previous automated systems based on classical machine learning systems like Support Vector Machines (SVM) or Random Forest are only capable of dealing with structured data and do not effectively deal with unstructured, multilingual and complex resumes. The consequences of these limitations are low accuracy, low contextual knowledge and low scalability. To address these issues, in this paper, a hybrid AI-inspired resume verification system incorporating the methods of Natural Language Processing (NLP), deep learning, and classical machine learning will be suggested. The system preprocesses resumes of different types (PDF, DOCX, text) and finds significant data, including education, skills, and experience, and it describes it with contextual embeddings with Transformer-based models. Convolutional Neural Networks (CNNs) are used to capture local linguistic patterns whereas traditional ML models like Random Forest and Gradient Boosting are used to analyse engineered numerical features. An ensemble classifier is a stacked ensemble of these components that is used to give a final score of authenticity, or what percentage probability a resume is a fake resume. The experimental evidence shows that the hybrid model is much better in comparison to traditional methods, as the accuracy of the models is 85-95 with the greatest accuracy of the Transformer based model of 94, and better precision, recall, and F1-score. High-performance, scalable, and automated approach to resume fraud detection through the combination of NLP, deep learning, and classical ML will make recruitment processes more efficient, transparent, and more credible.

DOI: https://doi.org/10.5281/zenodo.20066837

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Authors: Manogna.Velamakanni, DurgaBhavani.Makkena, Sindhu.Merugu, Harshika.Lekkala, Indira Lakshmi Borigorla

Abstract: Agriculture is very important for food security, but over 40% of farmers use too little or too much fertilizer, which causes low crop yield, soil damage, and financial loss. Smart recommendation systems can help farmers by giving accurate advice on the right type and amount of fertilizer. Many machine learning methods like Decision Trees, Random Forest, Gradient Boosting, and Neural Networks have been used for crop and fertilizer recommendations. However, these methods often need a lot of computing power, do not work well with small or noisy data, and can be hard for farmers to understand. To solve these problems, we propose a Support Vector Machine (SVM)-based fertilizer recommendation model. SVM works well with small and unbalanced datasets, reduces overfitting, and handles complex patterns while needing fewer resources, making it suitable for real farming. Using soil nutrient values and crop needs, the model gives reliable predictions. Tests show that the SVM model achieves 96.77% accuracy, making it effective for smart agriculture and proper fertilizer use.

DOI: https://doi.org/10.5281/zenodo.20066724

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Authors: Sk.Firdaus Fathima, M.Anusha, S.Vennela, Sk.Sadiya Kousar, B.Gayathri

Abstract: Driver drowsiness is one of the major causes of road accidents globally, leading to serious injuries, deaths, and economic loss. To combat this, a real-time Driver Drowsiness Detection System has been implemented using machine learning algorithms combined with IoT hardware. The system tracks the driver's eyes continuously through a real-time video feed obtained via a webcam. With the OpenCV and dlib libraries, the Eye Aspect Ratio (EAR) is computed to obtain a measurement of the degree of eye closure, which is a good predictor of drowsiness. Upon detection of prolonged eye closure, the system sends a serial communication command to an Arduino Uno microcontroller to activate a buzzer alarm and commence a progressive motor deceleration, mimicking a safe vehicle stop. This two-stage mechanism reduces the risk of accidents by giving both an initial warning and an automatic safety measure. Experimental data show that the designed system has a detection accuracy of 96.8% for different illumination conditions, with a response time of less than one second. The approach is cost-effective, non-invasive, and easily implementable on contemporary vehicles, ensuring it to be a promising solution for improving road safety.

DOI: https://doi.org/10.5281/zenodo.20066634

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Authors: V Manogna, B Durga, P Sravani, N Yamuna, B Reshma

Abstract: Alzheimer’s disease (AD) is a progressive brain disorder that leads to memory loss and a gradual decline in thinking and reasoning abilities. One of the major challenges in dealing with Alzheimer’s is detecting it early and accurately using MRI brain scans. Traditional manual analysis of these scans can be slow, complex, and prone to human mistakes over the years, different machine learning (ML) models like Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbors have been used to identify Alzheimer’s. However, these models often face issues such as overfitting, lower accuracy, and weak performance when dealing with complex and high-dimensional MRI data.to overcome these limitations, the proposed approach uses SVM model for detecting and classifying Alzheimer’s disease. The SVM model is well-suited for handling non-linear and complex data. It can effectively separate different disease categories by using advanced kernel functions and optimal hyperplane techniques. This leads to more precise and stable classification results, even with smaller datasets compared to existing ML models, the proposed SVM model achieves higher accuracy, sensitivity, and specificity, making it more dependable for automatic Alzheimer’s detection. It not only reduces errors but also helps in identifying the disease at an early stage, which is crucial for better treatment and patient care. With 98.16% classification accuracy, it outperforms current architectures significantly in the Alzheimer Detection and Classification Using SVM.

DOI: https://doi.org/10.5281/zenodo.20065684

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Authors: D Anveshini, K Chinna Reddemma, M Venkata Akshara, Ch Sai Samanvitha, Ch Sai sirisha

Abstract: Sudden cardiac arrest is a critical medical emergency that demands immediate recognition and timely intervention to improve a patient’s chances of survival. Although hospital-based cardiac monitoring systems are dependable, their high expense and lack of portability make them impractical for everyday personal monitoring. This work presents an Internet of Things (IoT)-based wearable framework that enables continuous and real-time cardiac health observation in non-clinical environments. The device integrates multiple biosensors including electrocardiogram (ECG), pulse oximeter, body temperature, and galvanic skin response (GSR) to acquire physiological data. The acquired data are uploaded to a cloud environment, where algorithms evaluate and categorize the user’s cardiac condition as normal, borderline, or severe. The system is linked to a companion mobile application that visualizes real-time readings and automatically issues alerts to caregivers and medical professionals when abnormalities are detected. Through the integration of wearable sensors, edge–cloud data analysis, and IoT communication, the proposed system delivers an economical approach for early cardiac distress prediction and prompt emergency support.

DOI: https://doi.org/10.5281/zenodo.20065529

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