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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Lande, Jayapal Syamala, M. Priyadarsan, Padhyala Sunil Kumar, V. Global-A Genpact Company Bangalore India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

In social media platforms, there is a growing interest in developing automated solutions for toxicity classification using machine learning algorithms. This paper presents a novel approach to automated toxicity classification using semantic topic models, achieving high accuracy and transparency. The report emphasizes the importance of detecting and adjusting for biases to ensure fair categorization. Moreover, the report highlights the challenges of natural language processing tasks, importance of understanding and addressing potential biases in machine learning models. Datasets are produced by the Origami project at Google, labeled by human labelers with adjectives such as 'toxic,' 'very poisonous,' 'accusation,' 'danger,' 'obscene,' and 'identity hatred.' From the analysis, it shows that proposed method achieved better results on toxicity classification in terms of accuracy (0.85), precision (0.87), recall (0.84) and f1-score (0.85) respectively. For non-toxicity classification, the proposed method achieves accuracy (0.92), precision (0.89), recall (0.88), f1-score (0.88) and AUC-ROC (0.96) respectively. © 2023 IEEE.

关键词： Latent Dirichlet Allocation Machine Learning Methods Natural Language Processing Semantic Topic Models Social Media Safety Support Vector Machine Toxic Comments

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Deep Learning based Energy-Efficient Task Scheduling in Cloud Computing 18

Deep Learning based Energy-Efficient Task Scheduling in Clou...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Anuradha, K. Kumar, T. Sampath Sr University Department of Computer Science and Engineering Telangana Warangal India Sr University Department of Cs&ai Telangana Warangal India

ISBN: (纸本)9789380544519

A growing number of manufacturing businesses are becoming more concerned with energy efficiency because of rising energy costs and environmental consciousness. The enormous shift of an organization's resource requirements from on-site technology to cloud-based systems is resulting in a significant rise in the expenses that cloud reviews face when it comes to building, maintaining, and providing hardware for servers, storage, networks, and processing. Because cloud reviews operate on such a massive scale, even a little decrease in performance can result in significant increases in energy or resource usage expenses. Numerous studies have put forth techniques designed to optimize throughput and resource consumption while accounting for varied cloud settings. This review's scope is reviewing several survey studies on cloud offloading methods based on GGCN and discussing the advantages and drawbacks of each. An exhaustive and scientific examination of the latest GGCN methods related to cloud unloading. Subsequently, the energy efficiency studies are examined. Lastly, a few recommendations are made for further research. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cloud computing Deep learning Energy Efficiency Resource Consumption Scientific Examination

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Text-to-Sketch Synthesis via Adversarial Network

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computers, Materials & Continua 2023年第7期76卷 915-938页

作者： Jason Elroy Martis Sannidhan Manjaya Shetty Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya Department of Information Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia Department of Computer Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia School of Computing Skyline University CollegeUniversity CitySharjahUAE Department of Electronics and Communication Engineering SR UniversityWarangalTelanganaIndia Department of Computer Engineering–AI&BDA Marwadi UniversityRajkotGujaratIndia

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security ***,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch *** the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch *** this study,we propose an adversarial network that generates human facial sketches using such cues provided by an ***,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated *** evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance *** proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

关键词： Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques

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Big Data Analytical Engine for Exploring the Power in Modern Business

Big Data Analytical Engine for Exploring the Power in Modern...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Padhi, Manoj Kumar Yamsani, Nagendar Anitha, M. Revanasiddappa, Revanasiddappa Global-A Genpact Company Bangalore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Most current applications of Big Data focus on examining consumer preferences and feedback expressed through social media and legitimate websites. Data is generated by all parties involved in the company, including mobile device location data and data created by machines with high-tech sensors, automated controls, and relevant software. We are currently living through an Internet revolution fueled by Big Data following the Industrial and Internet revolutions. Therefore, the implementation of Big Data platforms represents a significant opportunity for enterprises today. This article presents three case studies that highlight the potential of Big Data in the cellular phone business, online retail, and insurance sales over the Internet. To help companies locate, gather, and analyze huge amounts of data, a Big Data Analytical Engine has been proposed. From the analysis, it clearly shows that proposed model achieves better results in terms of accuracy (0.92), precision (0.93), recall (0.91) and f1-score (0.92) respectively. © 2023 IEEE.

关键词： Big Data Analytics Comparative Study Customer Churn Data Preprocessing Industry-Specific Recommendations Machine Learning Algorithms Performance Evaluation

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MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness 41

MILP-FBGen: LP/MILP Instance Generation with Feasibility/Bou...

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41st International Conference on Machine Learning, ICml 2024

作者： Zhang, Yahong Fan, Chenchen Chen, Donghui Li, Congrui Ouyang, Wenli Zhu, Mingda Yan, Junchi AI Lab Lenovo Research Beijing China School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Machine learning (ml) has been actively adopted in Linear Programming (LP) and Mixed-Integer Linear Programming (MILP), whose potential is hindered by instance *** synthetic instance generation methods often fall short in closely mirroring the distribution of original datasets or ensuring the feasibility and boundedness of the generated data - a critical requirement for obtaining reliable supervised labels in model *** this paper, we present a diffusion-based LP/MILP instance generative framework called *** strikes a balance between structural similarity and novelty while maintaining feasibility/boundedness via a meticulously designed structure-preserving generation module and a feasibility/boundedness-constrained sampling *** method shows superiority on two fronts: 1) preservation of key properties (hardness, feasibility, and boundedness) of LP/MILP instances, and 2) enhanced performance on downstream *** studies show two-fold superiority that our method ensures higher distributional similarity and 100% feasibility in both easy and hard datasets, surpassing current state-of-the-art techniques. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including ai-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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Futuristic Authentication: Development of an Artificial Intelligence based Enhanced Face Detection and Recognition System 7

Futuristic Authentication: Development of an Artificial Inte...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rama Satish, K.V. Jadhav, Dattatraya Arun Siva Prasad, M.V. Gopal Thatipudi, Jayendra Tejasri, V. Divya, J. Rnsit Dept of Ai Ml Bengaluru India Keystone School of Engineering Electronic And Telecommunication Department Pune India Anurag Engineering College Department of Cse Kodad India University of North Texas Dept. of Computer Science India S.R.K.R.Engineering College Department of It Chinaamiram Andhra Pradesh Bhimavaram India R.M.K.Enginnering College Dept of Computer Science And Design India

ISBN: (纸本)9798350359299

This research presents the design and implementation of an advanced Face Detection and Recognition System employing an Artificial Intelligence Assisted Face Recognition Model (aiFRM). The proposed system leverages the powerful Cascaded VGG16 architecture to enhance the accuracy of face detection and recognition tasks. The methodology involves a two-step approach, beginning with robust face detection using the cascaded VGG16 model. This is followed by feature extraction and recognition using the same architecture, resulting in a highly efficient and accurate facial recognition system. The cascaded VGG16 model is trained on diverse datasets to ensure adaptability to various scenarios and lighting conditions, enhancing the system's real-world applicability. The experimental results demonstrate the effectiveness of the proposed system in achieving state-of-the-art accuracy in face detection and recognition. The cascaded VGG16 model proves to be a formidable backbone for the aiFRM, contributing to the system's success in handling complex scenarios. The system's high accuracy, reaching 97%, positions it as a reliable solution for security, surveillance, and authentication applications in diverse environments. © 2024 IEEE.

关键词： Face recognition

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M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought

M3Hop-CoT: Misogynous Meme Identification with Multimodal Mu...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Kumari, Gitanjali Jain, Kirtan Ekbal, Asif Department of Computer Science and Engineering India Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761643

In recent years, there has been a significant rise in the phenomenon of hate against women on social media platforms, particularly through the use of misogynous memes. These memes often target women with subtle and obscure cues, making their detection a challenging task for automated systems. Recently, Large Language Models (LLMs) have shown promising results in reasoning using Chain-of-Thought (CoT) prompting to generate the intermediate reasoning chains as the rationale to facilitate multimodal tasks, but often neglect cultural diversity and key aspects like emotion and contextual knowledge hidden in the visual modalities. To address this gap, we introduce a Multimodal Multi-hop CoT (M3Hop-CoT) framework for Misogynous meme identification, combining a CLIP-based classifier and a multimodal CoT module with entity-object-relationship integration. M3Hop-CoT employs a three-step multimodal prompting principle to induce emotions, target awareness, and contextual knowledge for meme analysis. Our empirical evaluation, including both qualitative and quantitative analysis, validates the efficacy of the M3HopCoT framework on the SemEval-2022 Task 5 (MAMI task) dataset, highlighting its strong performance in the macro-F1 score. Furthermore, we evaluate the model's generalizability by evaluating it on various benchmark meme datasets, offering a thorough insight into the effectiveness of our approach across different datasets. © 2024 Association for Computational Linguistics.

关键词： Chains

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LEO Satellite Oriented Wildfire Detection Model Using Deep Neural Networks: A Transfer Learning Based Approach 6

LEO Satellite Oriented Wildfire Detection Model Using Deep N...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Gain, Mrityunjoy Raha, Avi Deb Biswas, Bristy Bairagi, Anupam Kumar Adhikary, Apurba Debnath, Rameswar Khulna University Computer Science and Engineering Discipline Khulna Bangladesh Ai Innovation Lab Khulna Bangladesh Noakhali Science and Technology University Department of Information and Communication Engineering Bangladesh

ISBN: (纸本)9798350385779

Wildfires are a recurring concern in several regions around the globe, often leading to catastrophic outcomes. The increasing accessibility of small satellites operating in Low Earth Orbit (LEO) has led to the potential for enhancing the speed of wildfire detection b y utilizing constellations of m any miniature satellites in the areas of interest. This research presents a system model for detecting and reporting wildfires to fire stations using LEO satellites. In this paper, we propose a transfer learning-based lightweight deep learning wildfire detection model at the base station to analyze pictures captured by LEO satellites and make predictions on the occurrence of wildfires. We use MobileNetV2 as our backbone model. MobileNetV2 is a lightweight model with low parameters. Lightweight models for image categorization offer faster inference, lower power consumption, efficient memory u sage, a nd ease of deployment, making them well-suited for real-time applications. However, a highly parameterized model can perform better than a poorly parameterized model. We apply transfer learning to ensure high performance in a low-parameterized model. The server must continuously check the images of all forest regions within a limited interval period to detect wildfires quickly. The lightweight model is very suitable for continuous image checking. Our experimental setup uses four baselines: VGG16, ResNet152, DenseNet161, and MobileNetv2. This study shows the efficacy of models in acquiring information from many sources within a given topic, as opposed to starting with no prior knowledge. We can also find from t he experimental results that our proposed low-parameterized transfer learning-based mobileNetV2 model achieves near 100 % accuracy, outperforming the other high-parameterized baselines. © 2024 IEEE.

关键词： Orbits

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