Daily Archives: May 19, 2026

Authors: Singareddy Saritha, M. Sivaparavathi

Abstract: A rising number of individuals are displaying signs such as excessive phone usage, loss of productivity, and even physical and psychological health concerns, making Smartphone addiction a major worry in recent years. The development of reliable instruments for the prediction of Smartphone addiction and the identification of those at risk is, hence, necessary. Using survey data on Smartphone use, we constructed a machine learning model to forecast Smartphone addiction in this research. There was a wide variety of mental health concerns addressed in the survey, including demographics, phone use patterns, and anxiety, despairs, and stress. The model was constructed using a well-liked and efficient machine learning technique. In this work, numerical variables are normalized and categorical variables are encoded as part of the data preprocessing to make sure the model can train properly. Also, we used measures like accuracy to measure the model's performance on the remaining data after training it on a subset of the data. The algorithm has successfully predicted Smartphone addiction with a high degree of accuracy, according to the findings. Use habits of mobile phones, including how often notifications were checked, how many hours spent on the phone daily, and the applications used most often, were the most critical variables for predicting addiction. Age, gender, and stress levels were other important factors. The constructed model has a number of possible uses. Healthcare providers might use it to identify patients at risk of Smartphone addiction and intervene accordingly. Also, app makers may utilize it to make their applications less addicting and more conducive to healthy phone habits. In a nutshell, the results show that machine learning algorithms can effectively predict Smartphone addiction. We need to conduct further studies to confirm our results on bigger and more varied datasets and to investigate other possible uses for this approach.

DOI: http://doi.org/10.5281/zenodo.20286754

Published by: vikaspatanker

Authors: Marella Maheswari, P ASHOKA

Abstract: Revolutionizing the verification of bank checks, this innovative technology simplifies the process by integrating deep learning, image processing, and an intuitive Django-based web interface. It streamlines the process with little human participation, making it easier than ever before. Our Convolutional neural network (CNN) trained on the IDRBT check dataset and executed in PyTorch has a 99.14% success rate in recognizing handwritten digits, as shown in the introductory article. Adaptive thresholding and Gaussian blurring are implemented in the source code to enhance the picture preparation. The optical character recognition (OCR) in MATLAB can recover machine-printed text with 97.7 percent accuracy, including IFSC codes and account numbers, when Pytesseract is used in the code for region-based text extraction. The approach uses SVM classification and SIFT feature extraction for real-time authenticity checks, allowing signature verification powered by SIFT and SVM to reach 98.1% accuracy. The web-based interface allows more users to upload photos of checks, train models, see datasets, and get immediate categorization results ("Genuine" or "Not Genuine"). The system complies with CTS-2010 standards for Indian banks and the extraction of critical details such as signatures, amounts, and check numbers is possible even if it supports formats from other countries. In order to automate the verification process and decrease processing time, operational expenditures, and fraud risks, it makes use of contour detection and region-based analysis. This scalable solution sets a new standard for secure, efficient financial transactions by combining the rigors approach from the paper with the actual code implementation. Future versions may support more than one language and format.

DOI: http://doi.org/10.5281/zenodo.20286653

Published by: vikaspatanker

Authors: Harshada Magar, Yash Bhalekar, Sarthak Belvalkar, Om Jadhav

Abstract: This paper presents ContractSphere AI, a system designed to help organizations manage their contracts more easily and securely. Managing contracts in companies involves many steps such as writing, reviewing, checking legal rules, and storing the final signed document. Doing all these steps manually takes a lot of time and often leads to mistakes. ContractSphere AI uses artificial intelligence to automate these steps and uses blockchain technology to make sure that signed contracts cannot be changed or faked. The system can understand contract language in multiple languages and can handle contracts from different countries with different legal rules. It uses a language model trained on legal documents together with a search system that finds relevant rules and contract examples. The final signed contract is stored securely by saving its unique hash on the blockchain, which proves the contract is genuine. This paper describes how the system works, explains the main processing steps, and discusses how well the system performs in terms of speed, security, and cost.

DOI: https://doi.org/10.5281/zenodo.20286629

Published by: vikaspatanker

Authors: Bandaru Jyothi, M.Radhika

Abstract: This article presents a new approach to automated amphibian and reptile categorization that makes use of deep Convolutional neural networks (CNNs) and transfer learning. By developing a reliable and precise MobileNetV2 model for species identification using deep learning, we tackle the limitations of traditional classification methods while also acknowledging the ecological importance of these two vertebrate groups. Using a transfer learning approach on a massive collection of amphibian and reptile images, we train a pre-trained Convolutional neural network (CNN) to overcome the issue of small dataset size. The model is able to generalize well across several species due to its high extraction efficiency. Additionally, the article delves into the significance of image augmentation techniques for enhancing model performance, particularly in cases when labeled data is scarce. Results are favorable when the proposed method is used to overcome challenges caused by changes in size, posture, and environmental factors. Ecological monitoring, conservation efforts, and biodiversity surveys might benefit from the model's classification accuracy, which we prove by comparing it to a large dataset of amphibians and reptiles. With an accuracy rate of 82%, the proposed MobileNetV2 model cans correctly categories amphibians and reptiles. The growing field of computer vision as it pertains to animal ecology and biology has a scalable and successful approach to automated species identification, which this work adds to it. The results show that deep learning techniques particularly transfer learning, have the potential to address the issues with animal categorization. Additional investigation on the connection between AI and biodiversity protection might result from this.

DOI: http://doi.org/10.5281/zenodo.20285882

Published by: vikaspatanker

Authors: Prakash Kumar Sinha, Dr. Navneet Seth

Abstract: Artificial Intelligence (AI) is rapidly transforming the field of Human Resource Management (HRM) by redefining traditional practices and enabling data-driven strategic decision-making. This study explores the role of AI as a catalyst in strategic HR decision-making and examines how intelligent technologies are reshaping the “human” aspect of human resources. The research highlights the integration of AI-powered tools such as predictive analytics, machine learning, chatbots, and automated recruitment systems in key HR functions including talent acquisition, employee engagement, performance evaluation, workforce planning, and retention management. The study emphasizes that AI enhances organizational efficiency by reducing manual workload, minimizing bias in recruitment processes, improving accuracy in decision-making, and enabling personalized employee experiences. At the same time, it discusses the challenges associated with AI adoption, including ethical concerns, privacy issues, lack of emotional intelligence, and resistance to technological change within organizations. The paper further argues that AI should not replace human judgment but rather complement human capabilities in strategic HR practices. The successful implementation of AI in HR requires a balanced approach that combines technological innovation with empathy, ethical standards, and human-centered leadership. The study concludes that AI has emerged as a powerful strategic partner in HR decision-making, enabling organizations to achieve greater productivity, agility, and competitive advantage in the evolving digital era.

Published by: vikaspatanker

Authors: Nitish kumar, Dr Sajad Ahmad Mir

Abstract: Mutual funds have emerged as one of the most relevant investment avenues for Indian retail investors because they provide diversification, professional management, flexibility, and accessibility through Systematic Investment Plans (SIPs). This study examines investor awareness and preference toward mutual funds in India, with special reference to retail investors. It focuses on the level of awareness, preferred investment avenues, mutual fund category preference, SIP adoption, and the key barriers that prevent wider participation. The study is based on a descriptive and analytical survey design using primary data collected from 100 respondents through a structured questionnaire. Secondary sources from regulatory bodies, industry reports, and academic literature were also used to strengthen the discussion. The findings show that awareness of mutual funds is moderate to high among the respondents, SIPs are the dominant investment mode, and equity mutual funds are the most preferred category. Mutual funds were also identified as the most preferred investment avenue in the sample, ahead of fixed deposits and gold. At the same time, lack of knowledge, fear of market risk, and limited surplus income remain major barriers to investment. The study concludes that awareness alone does not guarantee participation; investor confidence, financial literacy, and simple communication are equally important. Practical recommendations are offered for investors, mutual fund companies, and regulators to improve informed participation in the Indian mutual fund market.

Published by: vikaspatanker

Authors: Hadiza Ibrahim Aminu, Abdullahi Mohammed Ibrahim, Buhari Aliyu, Zainab Ibrahim Aminu, Abubakar Safiyanu

Abstract: Bioprocessing plays an essential role in the large-scale production of biological products, where accurate monitoring and control are key for both yield and quality. This work aims to develop and assess a predictive framework based on Artificial Neural Networks (ANN) for estimating product yield in bioprocess operations. A multi-phase approach was implemented, beginning with data collection from online sensors and laboratory analyses, followed by preprocessing steps that included normalization, outlier removal, noise filtering, and feature engineering, utilizing dimensionality reduction through Principal Component Analysis. A hybrid ANN model was created, integrating Feed-Forward Neural Networks (FNN) for steady-state predictions, Long Short-Term Memory (LSTM) networks for learning temporal sequences, and Convolutional Neural Networks (CNN) for interpreting spectroscopic data.The model, trained using supervised learning and cross-validation, achieved strong predictive performance with a Mean Squared Error (MSE) of 1.0139 and a coefficient of determination (R²) of 0.9756, capturing 97.6% of yield variance. Predicted versus actual values showed high consistency, confirming robustness for real-time monitoring. Minor overfitting was observed at extreme values, highlighting the need for dataset expansion and regularization. Overall, the results demonstrate that ANN-based modeling effectively captures nonlinear dynamics in bioprocessing, supporting proactive optimization, disturbance detection, and integration into industrial-scale monitoring systems.

DOI: http://doi.org/10.5281/zenodo.20282959

Published by: vikaspatanker

Authors: Mrs. J. Annie Jennifer, Dr. R. Gunasundari

Abstract: The mental health indicators can be found in memes, and it is quite complex since memes consist of both text and images, and one must analyze both elements to understand their meaning. This research proposes a novel deep learning technique named Multi-CNN. Its aim is to detect depression-related signs by analyzing their linguistic and visual content simultaneously in memes . The technology uses both the BERTweet model for natural language processing and ResNet18 features for images from a neural network. It was assessed using a dataset of internet memes annotated according to eight depression indicators. Early stopping, data augmentation, and others helped improve its performance, while results were estimated by means of a weighted F1 score. As the study shows, it is more effective to use linguistic and visual components simultaneously than to employ the model based only on language or solely on image analysis for identifying the presence of depressive signs in memes. The multimodal approach resulted in a weighted F1 score of 0.6846, while the language-based model received 0.6716. Using just the picture is ineffective when it comes to recognizing depression-related memes. The study's findings indicate that visual information and text together create strong cues for investigating mental health issues. Besides, the results point to fresh techniques and technologies that can handle the intricate heterogeneous datasets found in social media.

Published by: vikaspatanker

Authors: Sneha Reddy

Abstract: Distributed computing has become a cornerstone of modern IT infrastructure, enabling organizations to process large volumes of data, enhance system scalability, and improve overall performance. This study explores the fundamental concepts, architectures, and technologies that underpin distributed computing systems, including cluster computing, grid computing, and cloud-based distributed environments. By distributing computational tasks across multiple interconnected nodes, these systems achieve higher efficiency, fault tolerance, and resource utilization compared to traditional centralized models. The paper examines key components such as data distribution, communication protocols, load balancing, and synchronization mechanisms that ensure seamless operation across distributed networks. It also highlights the integration of emerging technologies such as artificial intelligence, big data analytics, and edge computing, which further enhance the capabilities of distributed systems. Various application domains, including cloud services, scientific computing, financial systems, and real-time data processing, are discussed to demonstrate practical implementations. Despite its advantages, distributed computing presents challenges related to security, data consistency, latency, and system complexity. The study analyzes these challenges and proposes solutions such as advanced encryption, consensus algorithms, and efficient resource management techniques. The findings emphasize that distributed computing is essential for building scalable, resilient, and high-performance IT infrastructures in today’s digital era.

DOI: https://doi.org/10.5281/zenodo.20282190

Published by: vikaspatanker

Authors: Nurul Izzah Salleh

Abstract: Cloud-native application development has emerged as a modern approach to building and deploying scalable, resilient, and highly available applications in dynamic computing environments. This review explores the fundamental principles, architectures, and technologies that define cloud-native systems, including microservices, containerization, orchestration, and continuous integration/continuous deployment (CI/CD) pipelines. By leveraging cloud platforms, organizations can develop applications that are flexible, loosely coupled, and capable of rapid scaling to meet changing user demands. The paper examines key components such as service discovery, API gateways, and distributed data management, which enable seamless communication and efficient operation of cloud-native applications. It also highlights the role of DevOps practices in accelerating development cycles and improving collaboration between development and operations teams. Various application domains, including enterprise systems, e-commerce platforms, and real-time data processing systems, are discussed to illustrate practical implementations. Despite its advantages, cloud-native development introduces challenges related to security, complexity, monitoring, and cost management. The study analyzes these challenges and presents solutions such as automated security practices, observability tools, and efficient resource management strategies. The findings emphasize that cloud-native application development is essential for organizations seeking agility, scalability, and innovation in today’s cloud-driven digital landscape.

DOI: https://doi.org/10.5281/zenodo.20282113

Published by: vikaspatanker