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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vijayaraj, J. Hari Yogith, S. Rithikravivarma, V. Siddharth, S. Easwari Engineering College Dept. of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Barcode scanning errors in retail environments contribute to inventory discrepancies, financial losses, and inefficiencies in stock management. Traditional optical barcode scanning methods are prone to failures due to occlusion, poor lighting conditions, and barcode damage. While recent advancements such as computer vision, deep learning-based error detection, and RFID integration have improved scanning accuracy, these approaches still face challenges related to high computational requirements, environmental dependencies, and implementation costs. This paper proposes a novel method utilizing X-ray imaging technology and advanced machine learning algorithms to detect improperly scanned or missing barcodes in real time. The system captures detailed X-ray images of items as they enter retail stores or warehouses and applies deep learning models to analyze barcode visibility. Real-time alerts are generated when scanning anomalies are detected, enabling prompt corrective action. Experimental evaluations demonstrate that the proposed approach significantly improves barcode detection accuracy, enhances inventory management, and reduces operational costs. The findings highlight the potential for widespread deployment in retail and logistics environments, offering a robust solution to barcode scanning challenges. © 2025 IEEE.

关键词： Sales

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Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Analysis of GAN for Melanoma Skin CancerClassification with ...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational intelligence, RAEEUCCI 2024

作者： Saranya, N. Jowin Alfred, C. Rr, Rishikesh Gilbert Idayan, I. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location. © 2024 IEEE.

关键词： Dermatology

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Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated Learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 IEEE.

关键词： Federated learning

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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An Intelligent Framework of Microcalcifications Breast Cancer Detection using Heuristic Approach aided Residual Attention Network 2

An Intelligent Framework of Microcalcifications Breast Cance...

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2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Anitha, J. Malathi, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

Background: Breast cancer detection has advanced in recent years, and one such recent technique is detecting the presence of Microcalcifications in the breast tissue from Mammogram images. Also, it is crucial to examine the favorable outcomes to examine whether the microcalcification samples to benign or tumor. Methods: A deep learning-based breast cancer microcalcifications detection model is developed in this work. The initial step involves in gathering images from reputable sources to ensure a diverse and comprehensive dataset. Following collection, these images are given to the detection phase, where the detection process is done using Adaptive Residual Attention Network (ARAN). During this phase, the parameters of the ARAN are optimally tuned using the Pine Cone Optimization Algorithm (PCOA) to ensure better performance. Result: Overall 92% of accuracy attained in the developed ***: Experimental analysis is conducted on this deep learning-based model to validate its effectiveness in microcalcifications detection. © 2024 IEEE.

关键词： Lung cancer

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Extended Reality E-Commerce: The Evolution of E-Commerce through XR Based Technologies

Extended Reality E-Commerce: The Evolution of E-Commerce thr...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Jawale, Supriya Bondade, Aparna Sontakke, Manish Sohagpure, Aditya Muneshwar, Akash Wankhede, Nisarga Priyadarshini College of Engineering Artificial Intelligence and Data Science Department Nagpur India

ISBN: (纸本)9798350373783

As the usage of apps and e-commerce platforms continues to rise, especially with the integration of virtual tryon features, the importance of being able to compare products and assess their compatibility in 3D becomes increasingly evident. This capability not only allows stores to keep their products available round the clock but also enhances the overall shopping experience for consumers. Moreover, it sets the stage for the integration of artificial intelligence into online retail environments. The very definition of products in this digital landscape is deeply influenced by the advancements in mobile networks and the adoption of Extended Reality (XR) applications. XR, serving as an umbrella term that encompasses a range of immersive technologies such as Augmented Reality (AR), Mixed Reality (XR), and Virtual Reality (VR), is revolutionizing how consumers interact with products and brands online. This article seeks to delve into the intricate factors that shape customer experience and satisfaction when engaging with XR and AI-enabled applications for online shopping. By incorporating dimensions like relative trust, social value, and novelty - parameters commonly explored in similar XR studies - we aim to measure various aspects including intention, perceived utilitarian value, immersion, and hedonic value. Through our research, we have discovered that each of these dimensions significantly influences consumers' willingness to utilize basic XR and AI applications during their shopping journey. When evaluating the collective impact, encompassing both direct and indirect effects, we have identified a sequence of influence, with adequate trust, innovativeness, immersion, social value, perceived health value, and utilitarian value playing crucial roles. Our preliminary findings underscore the significance of social value, assurance of satisfaction, usefulness, and immersion in shaping consumer behavior within the realm of XR and AI applications for online shopping. By u

关键词： Virtual environments

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Effective Farming for Zero Wastage and Enhancement of Farmer's Income 4

Effective Farming for Zero Wastage and Enhancement of Farmer...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Gomathy, G. Esther, C. Manaswini, S. Swedharani, R.V. Hyden, J. Jerry Anand, A. Ajay Sri Sairam Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798331508845

In the field of agriculture, reducing waste while increasing revenue is still a difficult task. However, this initiative tackles this problem head-on by integrating Internet of Things (IoT) Sensors with Machine Learning (ML). By harnessing the power of IoT sensors to gather real-time data on soil moisture, humidity, temperature, and weather conditions, coupled with ML algorithms analyzing past agricultural crop harvested data, market demand, pricing trends, and previous wastage records, the project paves the way for predictive farming. This proactive approach enables farmers to make informed decisions regarding crop selection, planning times, and investment strategies, ultimately leading to minimised wastage and enhanced profitability. Furthermore, the development of a user-friendly mobile application serves as a central hub for disseminating valuable insights to farmers. Through intuitive interfaces and comprehensive data visualization, the application provides farmers with detailed information on investment projections, recommended crops for cultivation, and optimal planting schedules. Moreover, by incorporating Natural Language Processing (NLP) capabilities, the application ensures accessibility for farmers of diverse linguistic backgrounds, facilitating seamless interaction and decision-making. By empowering farmers with actionable information and accessible tools, the project aims to revolutionize agricultural practices, driving towards a future of sustainable farming and increased profitability. © 2024 IEEE.

关键词： Soil moisture

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Employee Attrition Analysis Using Machine Learning 2

Employee Attrition Analysis Using Machine Learning

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Solomon, Clorin Haffrin D., Mohankumar C., Sivanandam Panimalar Engineering College Dept of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350328202

The production, morale and financial health of an organization can all be significantly impacted by staff attrition or the voluntary withdrawal of employees. An employee attrition study is a crucial step that enables organizations to identify the cause of the attrition and resolve the issue in order for them to grasp and successfully manage this challenge. In order to perform the research, extensive data on employee exits, job satisfaction, and other factors are initially acquired. data preprocessing and cleaning ensure that the data are accurate and suitable for analysis. It is possible to identify patterns and trends in attrition using statistical analysis and data visualization tools, and discover how turnover is connected to factors like income, work-life balance, performance, and a variety of other factors. In this project, we experiment with numerous algorithms in order to determine which one would best match the model. We are able to comprehend the top most important factors after selecting the ideal algorithm. We will examine employee attrition across a number of businesses in this article to determine its causes, which will assist the organization understand and manage a challenging position and resolve the issue. © 2024 IEEE.

关键词： Job satisfaction

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Compound Screening and Prioritization in Drug Discovery Using Machine Learning

Compound Screening and Prioritization in Drug Discovery Usin...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Mariyam, A. Asiya Varshni, R.R. Samyuktha, S.P. Kumar, V. Anush Narmadha, B. Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350348460

Drug discovery is a technique to uncover and nurture a new medication for several medical conditions and diseases. It entails a range of scientific and research endeavors designed to identify substances that effectively manipulate biological processes to lessen, control, or cure illnesses. Delivering safe and efficient medications that can enhance patients' quality of lives is the ultimate goal of drug discovery. The first and foremost step in the development of drug is compound inspection. Compound screening which is also referred to as High Throughput Screening (HTS) is a component in the drug discovery which identifies and assesses the potential compounds that can be incorporated to contribute to the target compound. We propose a machine learning model named Quantitative Structure-Property Relationship (QSPR) model that can predict the amount of toxicity, solubility, metabolic stability, bioavailability, and the chemical reactivity level when a variety of compounds are combined with each other. This model can determine data from various sources, like chemical databases and already formed compounds, to predict which compounds are most likely to exhibit the desired biological activity and chemical reactiveness. They can also learn from existent data, the active and inactive compounds and also recognize their patterns and characteristics. They can also predict the effect of one compound binding with a specific target. This machine learning model simplifies the drug discovery process by incorporating advanced technological techniques that predicts the potential compound binding, toxicity and reactive level and also the hit identification and approval. © 2024 IEEE.

关键词： chemical reactivity compound inspection Drug discovery hit identification and approval metabolic stability and bioavailability potential compound binding Quantitative Structure-Property Relationship (QSPR) Model solubility toxicity

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Secure AI based Chatbot Web Application with IBM Watson 3

Secure AI based Chatbot Web Application with IBM Watson

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvi, A. Abishak, D. Harish Sriraj, N. Navaneeth, S. Prasanna, R. Department of Artificial Intelligence and Data Science M Kumarasamy College of Engineering Karur India

ISBN: (纸本)9798331527549

This project focuses on developing a secure, AI-powered chatbot for retail chains, integrated within a web application to efficiently handle Frequently Asked Questions (FAQs). Utilizing IBM Watson’s advanced Natural Language Processing (NLP), the chatbot delivers precise, context-aware responses, enhancing the customer experience across retail environments. The platform offers a user-friendly interface for seamless interactions, a robust backend powered by IBM Watson’s AI model, and a database optimized for managing customer queries. A dedicated security module ensures data protection through encryption, authentication, and access control. This chatbot manages high volumes of inquiries with real-time responses, boosting customer engagement and operational efficiency while generating valuable insights for retail decision-making. Designed to be scalable and secure, the system upholds high standards of accessibility and data integrity, meeting the specific demands of the retail sector. © 2025 IEEE.

关键词： Sales

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