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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Bhosale, Parth Gopale, Kunal Taware, Karan Bewoor, Laxmi Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

E-commerce has revolutionized the retail landscape, offering consumers unparalleled convenience and a vast array of choices from the comfort of their homes. Enabling e-commerce in native languages is crucial for creating an inclusive shopping experience, allowing businesses to effectively reach and engage diverse linguistic communities. India is renowned for its multilingual and cultural legacy and this work is an attempt to reach a wider consumer base and increase market potential in India by providing online shopping for cultural festivals. The proposed work is an attempt to develop native language E-commerce platform for various festivals celebrated in India by using Large Language Models (LLM) to generate contextually rich and relevant contents making the user experience more intuitive and personalized. Finally, we talk about making search features better by using modern technology. By using smart algorithms and models, we help users search for products in their own language, and show them results that are most relevant to what they're looking for. Through careful testing and analysis, we show that our solution works well in improving how people interact with e-commerce websites, reaching more customers, and making more sales. Our research is important because it helps bridge the language gap online, giving businesses new opportunities to grow and making online shopping more accessible for everyone. © 2025 IEEE.

关键词： Marketplaces

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Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) 5th

Dynamic Emotion-Adaptive Attention Mechanism (DEAAM)

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Garugu, Suresh Kumar Madhuri, P. Shyamala Atchana, Venu Gopal Penmetsa, Gopala Krishnam Raju Bandaru, Venkata Naga Rani Sastry Eemani, V.R.J. Department of Computer Science and Engineering Vishnu Institute of Technology Bhimavaram India

ISBN: (纸本)9783031770807

The Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) offers a groundbreaking framework for analyzing emotions in real-time video streams, leveraging a state-of-the-art convolution neural network (CNN) for rapid and accurate emotion detection. By integrating an adaptive attention mechanism, DEAAM dynamically adjusts video sampling rates and focuses on areas of emotional intensity, optimizing computational resources for the most expressive moments. This system intricately combines facial landmark detection with saliency mapping to pinpoint critical expressive features, enhancing the depth and precision of the analysis. Furthermore, DEAAM incorporates temporal emotional coherence tracking, utilizing advanced recurrent neural networks to capture the evolution of emotional states, adding a rich contextual layer to the emotion recognition process. This comprehensive approach not only increases the accuracy of emotion analysis but also ensures efficiency by focusing on key emotional expressions. Ideal for applications in virtual communication, mental health assessment, and any domain requiring nuanced emotion recognition, DEAAM sets a new standard in empathetic technology, offering a sophisticated, contextually aware system for extracting real-time emotional insights from video feeds. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Recurrent neural networks

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Comparative Prediction Analysis for Credit Scoring System 3

Comparative Prediction Analysis for Credit Scoring System

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

作者： Rai, Anjali Mehfooza, M. Department of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The importance of predictive modelling for green selection-making in a variety of fields has expanded because of the fast development of information-driven technologies. The basic and primary goal of this research is to analyse and find an appropriate model which gives the best results in order to perform credit scoring on a given dataset. This study gives a thorough investigation of predictive modelling strategies, assessing their makes use of, benefits, and disadvantages. We discover the theoretical underpinnings and actual-international programs of famous predictive algorithms, including ensemble tactics, choice trees, neural networks, and regression-based totally fashions. This emphasises how vital variables like feature choice, model tuning, and facts exceptional have an effect on predictive accuracy. The revelation of every method’s overall performance in various datasets and eventualities through empirical analysis. By supplying insights that can assist practitioners pick appropriate fashions ideal to precise problem contexts, this studies adds to ongoing efforts to maximise predictive accuracy. The efforts highlight the opportunities of hybrid and ensemble techniques for complicated prediction tasks, suggesting avenues for future research to refine those methodologies. © 2025 IEEE.

关键词： Prediction models

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Optimized CNN Model to Develop a Decision-Making System for Spraying on Cotton Crop 3rd

Optimized CNN Model to Develop a Decision-Making System for ...

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3rd International Conference on Intelligent Systems and Applications, ICISA 2024

作者： Sudhir, Supriya Kumar, Kalyani S. Reddy, P. Santosh Department of Computer Science & Engineering BNM Institute of Technology Bangalore India

ISBN: (纸本)9789819798384

Dynamic crop morphology capture aids real-time precision in field spraying decisions. Ultrasonic radar and costly LiDAR can't maximize their high precision. They may slow down decision speed because the current spraying amount control needs to include more information about branches and leaves, which highly relies on the statistical features of regional morphology. Since most agricultural tools in China are medium in size and relatively inexpensive, it makes sense to create a unique identification system that combines deep learning with cheap monocular vision. A crop's growth cycle was a helpful gauge for direct spraying because of the knowledge gained. For this reason, let's examine cotton as an example: This study aims to develop a single-camera acquisition system and propose an algorithm based on the Convolutional Neural Network (CNN) to identify different growth stages of cotton plants. An optimal CNN model structure is chosen among nine possible method structures using an optimization procedure based on the matrix of confusion and recognition efficiency, and its dependability is established by repeatedly switching the training and test sets by the k-fold test concept. This CNN model has an accuracy of 94.27%, an F1-score of 96.39%, a recall of 95.76%, a precision of 95.31%, and a recognition speed of 72.46 ms per picture. Also, the convolution neural network, the model suggested in this study, performs better than those of VGG16 and Alex Net. Finally, spraying deposition tests were conducted over three distinct cotton growth stages to validate the accuracy of the developed identification method and the viability of the sprayed decision-making process based on CNN. The findings of the studies confirm that the ideal spray parameters were applied during distinct growth times correspondingly, leading to an increase of up to 63.24% in the optimum index over the operations that did not discriminate growth periods, yielding an overall increase of 43.29%. © The Author(

关键词： Cotton

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Enhancing DNA Sequence Analysis for Disease Diagnosis and Prediction Using Parallelised Hash-Based Approach 2

Enhancing DNA Sequence Analysis for Disease Diagnosis and Pr...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sreeram, Anusha Bhukya, Raju Department of Computer Science and Engineering National Institute of Technology Warangal India

ISBN: (纸本)9798331530389

DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotides that make up DNA, DNA sequencing allows for the identification of genetic information in specific DNA fragments, entire genomes, or complex microbiomes. It has transformed disease diagnosis and treatment and has applications in biotechnology, forensic science, and evolutionary biology. Comparing DNA sequences helps researchers identify genetic disorders, cancers, and antibody repertoires, leading to tailored treatments for individual patients. However, the diagnosis of diseases using DNA requires an efficient algorithm due to the large number of nucleotides in DNA sequences. The hash-based approach, an optimal string pattern-matching algorithm, is used to identify relevant patterns in DNA sequences. This approach has shown significant improvement in speed compared to the single pattern matching approach for finding multiple string patterns and a parallelized approach for hash based using GPU is also proposed. © 2025 IEEE.

关键词： DNA sequences

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Phish Check: Content-based Phishing Website Detection Web App 6

Phish Check: Content-based Phishing Website Detection Web Ap...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Ghadiyaram, Anil Kumar Reddy, G. Srikanth Basith, Mohammed Abdul Reddy, Naini Rithvik Department of Computer Science and Engineering Vignana Bharathi Institute of Technology India

ISBN: (纸本)9798331522667

This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTML parsing using the Beautiful Soup module, our study scrutinizes 26,584 websites sourced from "***" and "***." Employing seven diverse machine learning classifiers from the sci-kit-learn library and implementing k-fold cross-validation, this research study assesses the efficacy of different models in accurately distinguishing phishing websites. Evaluation metrics include confusion matrices, accuracy, precision, and recall scores. The proposed approach refrains from using URL-based attributes, focusing solely on the characteristics of HTML content. This study presents the results and comparisons of various classifiers, highlighting the effectiveness of our methodology. © 2025 IEEE.

关键词： Phishing

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Gesture Recognition of Indian Sign Language Using Hybrid CNN-SVM Classifier

Gesture Recognition of Indian Sign Language Using Hybrid CNN...

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International Conference on Systems, Control and Automation, ICSCA 2023

作者： Singh, Utkrisht Singh, Awadhesh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India

ISBN: (纸本)9789819773831

Sign language is a type of nonverbal communication method used by people with hearing impairments and the deaf to convey their sentiments and emotions. According to the 2011 Census of India data, there are over 50 lakh people in India who are deaf, and nearly 20 lakh people have speech impairments. In this paper, we have presented a technique to recognise the hand movements of Indian Sign Language efficiently by using a hybrid model of a strong convolutional neural network (CNN) and support vector machine (SVM). In this work, an Indian Sign Language (ISL) dataset is used to train the model. This dataset contains various two handed Indian Sign Language images. In the proposed hybrid model, CNN is used to serve as an automated feature extractor that extracts the most informative features, whereas SVM serves as a classifier. The experimental findings show the correctness of the suggested framework by reaching an identification accuracy of 99.78% for this model on the ISL dataset, and after including Gaussian and random noise to the images, the CNN-SVM model’s accuracy drops to 92.72%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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3-D AAV Deployment for Maximizing Harvested Energy in WRSNs: A Soft Actor-Critic Approach

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IEEE Internet of Things Journal 2025年第9期12卷 12083-12093页

作者： Bhalerao, Nilesh Vijaykumar Mothku, Sai Krishna Raju, Mekala Ratna National Institute of Technology Tiruchirappalli Department of Computer Science and Engineering Tiruchirappalli 620015 India SRM University AP Department of Computer Science and Engineering Amaravati 522240 India

Autonomous aerial vehicles (AAVs) with wireless power transfer (WPT) technology offer a promising solution to extend the lifetime of wireless rechargeable sensor networks (WRSNs) by swiftly recharging multiple sensor nodes. Existing strategies often prioritize optimizing the AAV trajectory and charging schedule. While overlooking diverse energy needs and spatial distribution of sensor nodes, many approaches rely on positioning the AAV at the center of the region above a fixed height. This also leads to potential energy wastage by unnecessarily recharging already adequately powered nodes near the AAV. A key challenge lies in optimizing the 3-D position of the AAV to maximize energy harvested by sensor nodes, considering their diverse energy demands and spatial distribution. To tackle this challenge, we propose a novel approach that jointly optimizes the 3-D deployment of the AAV and energy harvested by sensor nodes. We formulate this complex, nonconvex problem as a Markov decision process (MDP) and leverage deep reinforcement learning based on the soft actor-critic (SAC) algorithm to address it. The AAV learns to navigate continuous state and action spaces, enabling it to discover optimal hovering locations. Our novel reward function allows the AAV to prioritize energy-critical sensor nodes and regions with higher sensor node density, maximizing energy harvesting within limited hovering times. Simulation results demonstrate that our proposed method significantly outperforms conventional AAV deployment strategies, substantially improving sensor node energy levels and network sustainability. © 2014 IEEE.

关键词： Autonomous aerial vehicles (AAV) 3-D deployment energy maximization wireless rechargeable sensor networks (WRSNs)

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Learning Non-cooperative Driving Practices and its Impact on Road Traffic Dynamics 17

Learning Non-cooperative Driving Practices and its Impact on...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Osho Chakraborty, Suchetana Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India

ISBN: (纸本)9798331531195

Cooperative driving that prioritizes global safety and efficiency over self-interest is crucial in ensuring smooth traffic flow and enhanced road safety. However, experiencing noncooperative driving is common under heterogeneous and chaotic on-road traffic conditions. While non-cooperativeness is often context-dependent and assessed during direct vehicle interactions, it can be inferred through longitudinal analysis of driving patterns from vehicular kinematics data. This behavior not only adversely affects the vehicle itself but also has a cascading impact on nearby vehicles, ultimately disrupting overall traffic flow. In this study, we quantify a vehicle's non-cooperativeness using kinematic data and further model its cascading effects on nearby cooperative vehicles. Experimental analysis was conducted using the SUMO traffic simulator and the TraCI interface to assess the impact of non-cooperativeness on overall traffic dynamics. This research is among the first to examine the cascading effects of a vehicle's non-cooperativeness on its cooperative nearby vehicles. © 2025 IEEE.

关键词： Motor transportation

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Generative Adversarial Networks (GANs) for Education: State-Of-Art and Applications

Generative Adversarial Networks (GANs) for Education: State-...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Kavyashree, K.N. Shidaganti, Ganeshayya Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9789819767090

In the ever-changing educational landscape, technology integration has become essential to improving students’ understanding of basic topics. The COVID-19 pandemic hastened the transition to online education while posing new difficulties for teachers and students alike. The disruption of traditional classroom dynamics was exacerbated by the lack of digital innovation and interactive instructional tools, which made student participation less effective. Traditional teaching paradigms must make way for ones that encourage self-directed learning and fortify technical infrastructure in order to handle these difficulties. Generative Adversarial Networks (GANs), a cutting-edge use of deep learning, are starting to revolutionize educational technology. With the use of GANs, a range of features that are tailored to the individual learning preferences of each student may be created, thereby bridging knowledge gaps and providing a more dynamic and interesting learning environment for students. This research investigates several state-of-the-art features that leverage technology linked to GANs to enhance students’ virtual learning environments. The novel applications of generative adversarial networks are explored, with a focus on applications for student observation, academic performance, questioning and answering, and personalized learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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