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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Sathwik, Abbaraju Sai Rao, A. Koteswara Saiteja, Bheri Madhavi, K. Reddy Doppalapudi, Asutosh Amudha, P. VIT-AP University Amaravati India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Vijayawada India AI&ML School of Computing Mohan Babu University Tirupati India Coimbatore Ayya Avinashilingam Nagar641108 India

ISBN: (纸本)9783031889912

A conversational agent, also referred to as a chatbot, is computer software that can communicate with users in natural language. Because of a number of issues, developing a successful chatbot model is still difficult. First, understanding the nuances and context of human language remains a complex task, especially when dealing with slang, dialects, or ambiguous expressions. Second, maintaining engaging and coherent conversations over extended periods can be a hurdle, as chatbots need to adapt to changing topics and user moods. Third, ensuring ethical and responsible behavior in chatbots, such as avoiding biased or harmful responses, requires continuous monitoring and refinement. Lastly, staying up-to-date with the evolving field of NLP and ai is essential to incorporate the latest advancements and provide users with increasingly sophisticated conversational experiences. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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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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Plant Disease Detection with Artificial Intelligence Algorithms for Smart Agriculture

Plant Disease Detection with Artificial Intelligence Algorit...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： R. Sudeepthi V. Akshitha K. Navya K. Shalini R. Karthikeyan Hariharan Shanmugasundaram Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Overseeing plant leaf diseases is vital for sustaining crop health, preventing extensive agricultural losses, safeguarding farmers livelihoods and ensuring a stable food supply. The health of the plant leaf is severely harmed by diseases such as early and late blight. Detecting leaf diseases is a time-consuming operation, and many farmers lack understanding about it, resulting in crop failure. Thus, early disease detection is crucial since it affects the quality and quantity of the potatoes. A type of artificial intelligence called deep learning has many uses in several domains and enables accurate and efficient data processing and decision-making. In this study, Leaf illnesses like early blight and late blight are detected and predicted using DL architectures like MobileNetV2, ResNet50, and EfficientNetB0. A comprehensive dataset is employed, containing images of healthy and infected leaves, which aids in model training and evaluation. The results show that MobileNetV2 achieves the highest accuracy of 91% in identifying leaf diseases.

关键词： Deep learning Accuracy Plants (biology) Transfer learning Crops Livelihood Convolutional neural networks Artificial intelligence Diseases Residual neural networks

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Secure and Efficient Face Recognition via Supervised Federated Learning 2

Secure and Efficient Face Recognition via Supervised Federat...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, aiBThings 2024

作者： Solomon, Enoch Woubie, Abraham Emiru, Eyael Solomon Abdelzaher, Ahmed F Virginia State University Department of Computer Science RichmondVA United States Silo Ai Helsinki Finland University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798331529529

Facial recognition systems frequently rely on training with large image dataset acquired from several sources on a single device. These databases often store sensitive data, leading to significant privacy concerns. To mitigate this, we propose employing federated learning for supervised face recognition. Federated learning enables model training while maintaining privacy by leveraging decentralized edge devices. In our method, each edge device trains a model individually, which is subsequently sent to a secure aggregator. We use GAN to introduce different data without sending actual data. The aggregator combines various models into a global one, which is subsequently disseminated to the edge devices. Experiments with CelebA datasets show that federated learning effectively protects privacy while maintaining performance levels. © 2024 IEEE.

关键词： Federated learning

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ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation 41

ACM-MILP: Adaptive Constraint Modification via Grouping and ...

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41st International Conference on Machine Learning, ICml 2024

作者： Guo, Ziao Li, Yang Liu, Chang Ouyang, Wenli Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China AI Lab Lenovo Research Beijing China

Data plays a pivotal role in the development of both classic and learning-based methods for Mixed-Integer Linear Programming (MILP). However, the scarcity of data in real-world applications underscores the necessity for MILP instance generation methods. Currently, these methods primarily rely on iterating random single-constraint modifications, disregarding the underlying problem structure with constraint interrelations, thereby leading to compromised quality and solvability. In this paper, we propose ACM-MILP, a framework for MILP instance generation, to achieve adaptive constraint modification and constraint interrelation modeling. It employs an adaptive constraint selection mechanism based on probability estimation within the latent space to preserve instance characteristics. Meanwhile, it detects and groups strongly related constraints through community detection, enabling collective modifications that account for constraint dependencies. Experimental results show significant improvements in problem-solving hardness similarity under our framework. Additionally, in the downstream task, we showcase the efficacy of our generated instances for hyperparameter tuning. Source code is available: https://***/Thinklab-SJTU/ACM-MILP. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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