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

作者： Shabi, Sathika Begum Mohammad Bhuvaneshwari, M. Kanimozhi, S. Pavithra, S. Department of Computer Science and Engineering Chennai Institute of Technology Tamilnadu Chennai600069 India

ISBN: (纸本)9798331527549

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry. © 2025 IEEE.

关键词： Smart manufacturing

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A Hybrid Learning Model for Tomato Plant Disease Detection using Deep Reinforcement Learning with Transfer Learning 4

A Hybrid Learning Model for Tomato Plant Disease Detection u...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Raghuram, Kadambari Borah, Malaya Dutta Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

Plant diseases play a significant role in damaging crop production and food security. Detecting and diagnosing plant diseases in the early stages obtains better management of diseases. Many plants are affected by these diseases, which are very dangerous for crop yield. This paper introduces advanced plant disease detection using an advanced preprocessing technique and a Hybrid Learning Model (HLM). The advanced preprocessing method uses a digital camera to capture high-resolution images of plant leaves from multiple angles. These images are then processed using an enhancement algorithm to improve the visual quality and clarity. The preprocessed images are subjected to a HLM model, which utilizes Deep Reinforcement Learning with Transfer Learning (DRL-TL). The DRL-TL architecture is designed to extract features from the preprocessed images in a three-dimensional manner, considering the spatial information of the leaves. It enables the model to capture precise patterns and variations indicative of disease symptoms. The pre-trained model MobileNetV2 trained on a tomato disease dataset belongs to labeled images;it consists of standard and affected plant leaves to learn the discriminative features associated with different diseases. Results obtained the effectiveness of the hybrid learning model. The preprocessing technique significantly increases the input images' quality, enhancing the subsequent HLM's performance. The model accurately identifies and classifies various plant diseases, outperforming existing methods. Furthermore, the hybrid learning model shows robustness and abstraction, successfully detecting diseases across plant species and environmental conditions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Deep reinforcement learning

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Autism Spectrum Disorder (ASD) Detection from Facial Images using MobileNet 9

Autism Spectrum Disorder (ASD) Detection from Facial Images ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Dodia, Shubham Meshram, Vidula Kasle, Janai Gomase, Shrirang Amrit, Harshal Sarse, Renuka Vishwakarma Institute of Information Technology Department of Computer Engineering Maharahtra Pune India

ISBN: (纸本)9798350394474

A developmental disability linked to brain development, autism spectrum disorder (ASD) results in difficulties with behavior, socialization, and communication. Children with autism exhibit significant morphological alterations to their faces that distinguish them from normally developing (TD) children. Our aim is to assist psychiatrists by identifying children with ASD at an early age, which will lessen their symptoms and enhance their cognitive functioning. In this research study, we deploy a neural network-based binary classifier on a pre-trained model of MobileNet to a publically available data set named Autism-Image-Data that we obtained from Kaggle to present a functional model using a facial imaging for autism detection. The dataset contains 2540 training images with 300 test images. This model achieves an accuracy of 89.58% on the dataset that was trained using pre-trained MobileNet architecture model through transfer learning. © 2024 IEEE.

关键词： Image classification

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A Novel Machine Learning-Based Approach for Analysis, Prediction and Prevention of Accidents on Indian National Highways 7

A Novel Machine Learning-Based Approach for Analysis, Predic...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Deshpande, Anjali Tribhuvan, Vinit Pune Institute of Computer Technology Department of Computer Engineering Pune India Coep Technological University Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Road traffic safety is a matter of crucial concern for the government as well as citizens. Despite all the measures taken to promote road traffic safety in India, there are a lot of regions of vulnerability. This leads to the need of an effective analysis to subdue alarming figures by significant amount. In an attempt to address this problem, we apply statistical analysis and data mining algorithms for analysis, prevention and prediction of road accidents on Indian national highways. To enhance the process of road risk analysis, proposed system puts forth an idea of analyzing the National Highways Authority of India (NHAI) datasets using Hidden Markov Model (HMM) and regression model supported by fuzzy classification. Our proposed system also has an alert module to provide an alarm and also some suggestions to the user through SMS depending upon the travel route. The implementation results indicate that the proposed method yields an accuracy of 84.55% approximately. © 2023 IEEE.

关键词： Baum-Welsh algorithm data mining fuzzy classification Hidden Markov Model (HMM) machine learning regression

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An Assistive Vision for a Visually Challenged Person using Alexa 4

An Assistive Vision for a Visually Challenged Person using A...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Dheeraj, Chilakapati Reddy, Kothapalli Sujith Rajalakshmi, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

It has been associated with converters and inverters. The system has been found to be feasible in efficiently utilizing Photovoltaic energy and integrating it with the electrical grid without any disturbances. The successful integration of this two-stage system assures not only that the power generated comes from the photovoltaic panels but also that grid stability and power quality are assured. This project brings in a new control methodology for active power regulation and is the main constituent for this system. Based on real-time monitoring and advanced control algorithms, it assures very accurate matching of power generation with grid demand in dynamic power adjustment capabilities. This feature maximizes the consumed power but also improves the grid stability through voltage and frequency control, thus minimizing the impact of variable Photovoltaic irradiance. Alongside, the converters and inverters applied in the system provide an essential interface between the D.C. output from the Photovoltaic panels and the A.C. grid;thus, it is possible to provide bidirectional power flow seamlessly into the grid. The active power regulation control approach to be adopted in this double-stage single-phase gridconnected P.V. system, with the converter and inverter facilities, is a sustainable and reliable solution for Photovoltaic power generation and grid integration. Such development and deployment will not only become a part and parcel of the global shift to renewable energy but also provide the current grid integration, power quality, and efficiency challenges with a feasible and promising solution. Considering the huge role that Photovoltaic energy can play in our profile, this project actually becomes a contribution to furthering renewable energy systems. © 2024 IEEE.

关键词： Recurrent neural networks

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The Future of Work: Robotic Process Automation and its Role in Shaping Tomorrow’s Business Landscape

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SN computer Science 2025年第2期6卷 1-19页

作者： Bhardwaj, Vivek Yadav, Shveta Kaur, Navjeet Anand, Darpan School of Computer Science and Engineering Manipal University Jaipur Jaipur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

In the new era of automation, Robotic Process Automation (RPA) has emerged as a powerful suite of tools for automating mundane, repetitive, rule-based, and structured tasks using software bots without disrupting existing infrastructure. Due to its significant potential, RPA has garnered considerable attention in the business world, being both easier and cheaper to implement while enhancing efficiency and productivity. This study begins by examining the characteristics of an RPA system and suitability of processes for RPA. This study delves into the role and impact of RPA in modern businesses, highlighting its transformative potential. It also explores the latest trends in RPA research, providing insights into cutting-edge developments and innovative applications. Furthermore, the paper addresses the challenges faced in implementing RPA and offers strategies for overcoming these obstacles, aiming to enhance the efficiency and effectiveness of RPA deployments in various business contexts. Lastly, RPA software bots were developed to quickly and accurately extract data from various invoices. The results show that, when implemented correctly, an RPA system is both easy to deploy and scalable, while maintaining high accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Business processes Intelligent automation Robotic process automation (RPA) Software bots

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Low Birth Weight Prediction Using Machine Learning 5th

Low Birth Weight Prediction Using Machine Learning

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Harika, G. Venkata Lakshmi, T. Santhosh Prudhvi Raj, P. Prasan, U.D. Jayanthi Rao, M. Department of Computer Science & Engineering Aditya Institute of Technology and Management Tekkali532201 India

ISBN: (纸本)9789819767311

In recent decades, machine learning has its increased problem solving methodologies and applications in various fields of business, marketing, education and medical diagnostics. Among all the ML techniques, some have been employed in the fields of medical sciences to predict various health conditions which require complete analysis of the patient’s health by considering some parameters which impact the health condition of an individual. Low birth weight of infants has been a constant recurring problem which has its visible impacts only after the birth of the baby. It acts as an indicator of sickness in newborn baby’s weight. This can be avoided using suitable ML techniques which predict required values from the information passed to the trained model. The prediction algorithm is implemented using random forest regression algorithms. We are aiming to develop a website which considers the health indicators as inputs from the user and provides them with estimated value by the trained ML model. The measures of metrics used here are the number of gestational days, age of mother, height, weight, plurality, and smoking status of the mother. All of these parameters can be used to estimate the accurate value of the weight of the baby within the web application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： CSS HTML LBW prediction Machine learning Python

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in engineering & Sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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IoT-Enabled Smart Retail Environments 2

IoT-Enabled Smart Retail Environments

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Hemalatha, Raavi Amulya, Godi Lalitha, Ch.S.N.Sai Vishnu Institute of Technology Department of Computer Science & Engineering Bhimavaram534202 India

ISBN: (纸本)9798350364699

The objective of this investigation of Internet of Things (IoT) technology in retail environments is to cause a revolution in efficiency by leading to improvements in both the efficiency of operations and the quality of the customer experience. The purpose of this article is to investigate Internet of Things (IoT) enabled smart retail environments, with a particular emphasis on the revolutionary impact of linked bias and detectors on ultramodern retail practices. Smart retail systems can optimize force operation, streamline force chain processes, and achieve the highest possible level of customer satisfaction through the use of real-time data collecting and analysis. Among the colorful Internet of Things operations that we investigate are smart shelves, automated checkout systems, and dynamic pricing. These are all examples of operations that lead to improved functional workflows and increased customer pleasure. Moreover, the study covers the difficulties associated with enforcing the outcomes of the Internet of Things (IoT), which include data sequestration, security companies, and the requirement for a resilient network structure. The paper highlights how Internet of Things technologies are fostering innovation and effectiveness in retail contexts by using case studies and recent breakthroughs. The results of our research indicate that the abandonment of the Internet of Things in retail settings has the potential to bring about significant benefits;nevertheless, to fully realize its potential, it is necessary to give careful thought to both technological and ethical counterarguments. © 2024 IEEE.

关键词： Customer satisfaction

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Capsule Networks and LSTM Models for Robust Deepfake Detection in Audio and Video 5

Capsule Networks and LSTM Models for Robust Deepfake Detecti...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Abisha, M.B. Kathrine, Jaspher W. Kushmitha, S. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331518097

Due to the fast advancement of deepfakes, notable difficulties have been presented by deepfake innovation in confirming the believability of computerized content, especially on the audio and video front. This paper introduces a new hierarchical model that articulates Capsule Networks within LSTM for the effective and robust deepfake detection in both audio-video frameworks. It is argued that using Capsule Networks, spatial mindfulness capabilities enable the detection of subtle spatial objects in video outlines, whereas, LSTM models capture transient objects in audio arrangements and video frames over time. Thus, our approach implements two designs at once that reached progressed discovery accuracy, addressing spatial and temporary disorders, inherent in deepfake media. It is discovered from the outcomes that this model is very much effective on several datasets suggesting its better generalizing capability in different types of Deepfake media. In its current form, the design of this approach seems capable of being used for the real-time detection task. In this regard, this research provides practical insights into the development of media forensics offering an approach that can be more effective in the role of dissemination of fake news prevention, and fills the gap in the current literature, thus advancing the overall understanding of media forensics. © 2024 IEEE.

关键词： Video analysis

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