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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： Mathan Kumar Mounagurusamy P A Manoharan J. Anvar Shathik A. Pandiaraj N. Ramshankar Naveen G Department of Computing Technologies SRM Institute of Science and Technology Chennai Chennai Institute of Technology Chennai AIML and Data science Anjuman Institute of Technology and Management Karnataka India Department of Computer Science and Engineering R.M. D. Engineering College Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Normally, the banking sector gains profit from different kinds of sources, but the main factor in their profitable growth is giving loans to their customers. A bank's profit or loss is determined by the quantity of the loan. A huge number of people are taking loans from banks and not clearing their dues. So there is a tedious process for banks to identify them and collect them. This in turn leads to following up on a complex verification process and issuing loans to their customers. Customer satisfaction is mandatory in every field, but in the banking sector, it is essential to identify genuine applicants and satisfy their needs, as well as to keep bankers free from non-payable customers. The manual method of identifying the right person and providing a loan to them may cause misconceptions. Several studies in this field suggest various kinds of approaches to identifying applicant status. Therefore, the use of machine learning (ML) approaches in the banking sector to resolve difficulties faced by bankers is becoming more popular. This reduces the problems faced by bankers and makes their applicants satisfactory. The time taken for this process is too long. It is essential to select the best ML approach from the different kinds of classifier algorithms available. The attributes chosen for this study include the applicant's education, type of work, applicant income, loan amount, co-applicant income, credit history, and property area. The proposed study reviews ML approaches such as K-nearest Neighboring (KNN), Logistic Regression, Decision Tree and Support vector Mechanism (SVM). Among those, KNN outperforms the other three algorithms, and the efficiency obtained by KNN is 87.50%, Logistic Regression is 83.512%, Decision Tree is 77.09%, and Random forest (RF) is 74.49%.

关键词： Support vector machines Logistic regression Machine learning algorithms Reviews Banking Nearest neighbor methods Vectors Sustainable development Recommender systems Testing

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Machine Learning-Enhanced Self-management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN Computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Identification of Different Medicinal Plants Through Image Processing

Identification of Different Medicinal Plants Through Image P...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： J. Anvar Shathik S. Hashini A. Pandiaraj N. Ramshankar Naveen G Amirthavarshini K B AIML and Data science Anjuman Institute of Technology and Management Uttarkannada Karnataka India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Department of Computer Science and Engineering RMD Engineering College Kavaraipettai Tamilnadu Department of Electronics and Communication Madha Institute of Engineering and Technology

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Identification of medicinal plants and raw materials is crucial for various applications including pharmaceuticals, herbal remedies, and conservation efforts. Conventional methods of recognition often rely on expert intelligence and instructions investigation which container be time-timewasting and lying to oversights. In this study, we propose a novel approach for automating the identification process using image processing and machine learning algorithms. Specifically, we leverage advanced techniques such as deep learning with ResNet-50, and Random Forest algorithms to accurately classify images of medicinal plants and raw materials. Additionally, we integrate a leaf disease detection system using Keras to further enhance the functionality of the platform. The developed system includes a user-friendly website with features such as a blog page for sharing information and seamless integration of the image processing algorithms using the Django framework. Investigational results display the efficacy of the anticipated method in precisely discovering medicinal plants and raw materials, paving the way for applications in various domains.

关键词： Medicinal plants Deep learning Machine learning algorithms Medical services Raw materials Classification algorithms Sustainable development Random forests Diseases Pharmaceuticals

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A Proposed Architecture for Quality based Decision Making Approach for Software Re-engineering 4

A Proposed Architecture for Quality based Decision Making Ap...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Govil, Nikhil Sharma, Nidhi Thakral, Deepti Garg, Shalini Dixit, Rinku Chaoudhary, Shailee Lohmor Gla University Department of Cea Mathura India Galgotia College of Engineering and Technology Department of It Greater Noida India Manav Rachna University Department of Computer Science & Technology Faridabad India Mit Art Design and Technology University MH Pune India New Delhi Institute of Management Department of Aiml - Data Science New Delhi India

ISBN: (纸本)9798350300093

Software re-engineering is the modification of a software system after it has been reverse engineered, typically to add new functionality or fix errors. Software re-engineering involves a set of activities aimed at reorganizing a legacy system into a new target system that conforms to quality constraints. This paper explores the reasons behind the failure of software re-engineering. This study has analyzed that some of the most common factors for failure of software re-engineering are misestimated costs and quality inefficiencies. As far as the quality factor is concerned, first the basic tool architecture is analyzed which is used by most of the tools for reverse engineering, reorganization and re-engineering. In this research study, a quality evaluation factor as well as parameters for quality evaluation are added to that particular architecture, so that customers and maintainers can analyze whether they achieve the desired goals for re-engineering. Ifthe target is achieved then the finished product is obtained again with quality and within budget, otherwise more attention should be given to the quality evaluation process. © 2023 IEEE.

关键词： Reverse engineering

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Understanding Legal Document using Natural Language Processing Approach

Understanding Legal Document using Natural Language Processi...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Rohini Shinde Ch. Dathasai V. Jyothir Aditya S Srinivasreddy Shanmugasundaram Hariharan Dadala Geetha Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of CSE (AIML) Vignan’s Institute of Management and Technology for Women Hyderabad Telangana India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Legal documents, with their complexity and sheer volume, are prime candidates for a revolution powered by Natural Language Processing (NLP). This technology holds immense potential to transform how legal documents are created, analyzed, and managed. NLP can automate tasks like extracting key information from contracts, speeding up research by analyzing vast legal texts, and even classifying documents for efficient organization. Furthermore, NLP can assist in reviewing and drafting contracts by identifying ambiguities, and improve accessibility by translating legalese and summarizing documents in plain language. While NLP offers significant benefits in terms of efficiency, accuracy, and consistency for legal professionals, it’s important to remember that human expertise remains crucial. Ultimately, NLP has the power to significantly revolutionize the legal document terrain. The results analyzed proved the significance of the categorization of legal document analysis which leads to promising illustrations.

关键词： Pervasive computing Translation Text analysis Law Social networking (online) Transforms Organizations Natural language processing Complexity theory Contracts

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GeoAgriGuard: AI-Driven Pest and Disease management with Remote Sensing for Global Food Security

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

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Diabetic Condition Classification using Supervised and Unsupervised Machine Learning Techniques: A Use Case for HealthTech Industry 4

Diabetic Condition Classification using Supervised and Unsup...

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4th IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Bongale, Anupkumar M. Biradar, Santoshkumar Pise, Priya Suke, Amita Priyadarshan Uke, Nilesh Dharrao, Deepak S. Symbiosis Institute Of Technology Department Of Aiml Pune Campus Pune India G H Raisoni College Of Engineering And Management Department Of Cyber Security And Data Science Pune Wagholi India Indira College Of Engineering And Management Department Of Ai And Ds Pune India Trinity Academy Of Engineering Pune India Symbiosis Institute Of Technology Department Of Cse Pune Campus Pune India

ISBN: (纸本)9798350384628

Diabetes is a very underestimated and, in many cases, an ignored disease. People do not understand the seriousness of diabetes. Many people think diabetes is just something because of which one cannot consume sweets. This, however, is not true. Diabetes is a very serious problem where symptoms can range from being very thirsty all the time to even death. There are many cases where diabetic people had to get their leg, or some other limb amputated because of it. Thus, people have to be made cognizant of diabetes' seriousness. This article dives deep into this disease and lists the different types of diabetes, and the way people can know if they are diabetic. Also, there is an in-depth literature review of research papers ranging from 2002-2020. Furthermore, a deep dive in the project of diabetes prediction is present. The techniques used to classify into 'diabetic' or 'non-diabetic' are Random Forest, Logistic Regression and K Nearest Neighbor algorithms which all belong to the supervised machine learning algorithms. Logistic Regression uses sigmoid function to classify into different classes, Random Forest approach uses multiple Decision Trees and the majority decision of them all for the final output. Moreover, K Nearest Neighbor technique uses the concept of Euclidean Distance to decide which class is suitable for any new coming datapoint. © 2023 IEEE.

关键词： Decision trees

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Implementing and Analyzing Behaviour of ML based Tamil Songs Recommendation System 2

Implementing and Analyzing Behaviour of ML based Tamil Songs...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Garg, Shalini Pooja Chaoudhary, Shailee Lohmor Dixit, Rinku Govil, Nikhil Design and Technology University Mit Art MH Pune India Galgotias University School of Cse Delhi - NCR Gr. Noida India New Delhi Institute of Management Department of Aiml - Data Science New Delhi India Gla University Department of Computer Engineering & Applications U.P. Mathura India

ISBN: (纸本)9781665491990

Music gives listeners a chance to relax, relieve fatigue and connect one culture with another. In such a situation, if the favorite song-music is heard one after another and according to the emotion, it gives a unique and wonderful experience to the listener. It is determined based on the emotion and interest, ratings provided by listeners and search patterns. In this research article, the proposed method presents Tamil songs compilation and recommendation system with the help of these factors. The behavior of this recommendation system is also analyzed in this system. The proposed system is developed in Java programming language by adopting the techniques of Machine Learning. Through this system, listeners have the choice to play category wise songs. As per the listening history and patterns, upcoming songs are predicted to the listeners. This system also provides ratings to each song. This developed application can also be used commercially as it has been validated that the ratings provided by the listeners are in line with IMDb ratings. © 2023 IEEE.

关键词： Recommender systems

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Real Time Lung Cancer Prediction Using Lazy Learning Technique

Real Time Lung Cancer Prediction Using Lazy Learning Techniq...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： K. Gowri Jaswanth P. Pavan Adithya G. Sai Eekshith Shanmugasundaram Hariharan Vinay Kukreja Dadala Geetha Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Centre for Research Impact & Outcome Chitkara University Rajpura Punjab India Department of CSE (AIML) Vignan's Institute of Management and Technology for Women Hyderabad Telangana India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The lungs are vital organs responsible for oxygenating the body and filtering out harmful substances. The main causes for lung cancer are smoking, Alcohol use and Air pollution. Lung cancer is crucial for improving patient predicting and reducing mortality rate. The mortality rate from lung cancer continues to rise across all age groups. The primary objective is to evaluate the effectiveness of these algorithms in early lung cancer detection which improves the patient survival rates. Machine learning revolutionizes health care by facilitating early disease detection through robust data analysis techniques. This study utilizes supervised machine learning algorithms, including Support Vector Machine (SVM), Naive Bayes, Logistic Regression and also K-Nearest Neighbors (KNN), to identify early stages of lung cancer risk. Among the tested models, Logistic Regression and also K-Nearest Neighbors (KNN) has the highest accuracy, achieving an accuracy rate of 99.99%. This models achieves highest performance and evaluated based on precision, recall, accuracy and area under the curve.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Lung cancer Mortality Medical services Nearest neighbor methods Prediction algorithms Real-time systems

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Secure-AI-Shield Model Leveraging Advanced Transformer Networks for Enhancing Cybersecurity in Blockchain Applications

Secure-AI-Shield Model Leveraging Advanced Transformer Netwo...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Akshay Dilip Lahe B. Saravanan Ambrish Kumar Sharma J. Anvar Shathik Rashmi K Santosh Shivlal Dhamone Department of MCA Saraswati College Shegaon Maharashtra Department of CSE Sri Eshwar College of Engineering Coimbatore Department of MCA Noida Institute of Engineering and Technology Greater Noida Department of Computer Science & Engineering Anjuman Institute of Technology and Management Karnataka India Department of CSE(AIML) Vidyavardhaka Engineering College Mysuru Kamataka Department of Mathematics SGV &SSP's Arts Commerce and Science College Onde (Affiliated to University of Mumbai) Maharashtra India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Many artificial intelligence (AI) algorithms look for useful patterns using the data. data on the Internet is spread around and controlled by separate subgroups of people that do not necessarily trust each other, making it hard to authorize and validate use of data in cyberspace bureaucracy for AI and big data. This paper presents the Secure-AI-Shield (SAI-S) model, an architecture that allows secure storage, computation and sharing of large scale data via Cloud platform. Its intent is to establish a more secure cyberspace with abundant data resources that can improve AI. The model integrates three essential components: first, a blockchain-based data-sharing mechanism that guarantees ownership, enabling reliable data sharing to generate substantial datasets; second, an AI-driven secure computing platform that formulates advanced security protocols, contributing to a more trustworthy cyberspace; and third, a reliable value-exchange system for acquiring security services, which incentivizes participants to share their data or services in exchange for economic rewards, thereby promoting data sharing and improving AI performance. In addition, we analyse its alternative deployment, conventional application scenario of the SAl-S Model, and its effectiveness in addition to network security and economic profitability aspects.

关键词： Cloud computing Computational modeling Smart contracts Cyberspace Network security Transformers data models Blockchains Reliability Artificial intelligence

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