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MA-DV2F: A Multi-Agent Navigation Framework Using Dynamic Velocity Vector Field

IEEE Robotics and Automation Letters 2025年第6期10卷 5823-5830页

作者： Ma, Yining Khan, Qadeer Cremers, Daniel Technical University of Munich Computer Vision & Artificial Intelligence School of Computation Information and Technology Germany Munich Center for Machine Learning Germany

In this paper, we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of reference orientation and speed that a vehicle must attain at any point on the navigation grid such that it safely reaches its target. The field is dynamically updated depending on the speed and proximity of the ego-vehicle to other agents. This dynamic adaptation of the velocity vector field allows prevention of imminent collisions. Experimental results show that MA-DV2F outperforms concurrent methods in terms of safety, computational efficiency and accuracy in reaching the target when scaling to a large number of vehicles. © 2016 IEEE.

关键词： Multi agent systems

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SightAssist: A Multi-facility machine learning Approach for Empowering the Visually Impaired 2nd

SightAssist: A Multi-facility Machine Learning Approach for...

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2nd International Conference on Data Science and Network engineering, ICDSNE 2024

作者： Bandopadhyay, Saikat Dutta, Jhalak Das, Smita Datta, Soumyajit Ghosh, Debaditya Dey, Rohit Kumar Nandigrami, Jeet Department of Artificial Intelligence and Machine Learning Netaji Subhash Engineering College Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Agartala India Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India

ISBN: (纸本)9789819783359

Blindness or visual impairment ranks among the top ten disabilities affecting both men and women, impacting over 35 million individuals of all ages in India. Access to visual information is crucial for enhancing the independence and safety of blind and visually impaired individuals. There exists a compelling imperative to develop assistive technologies that can significantly improve their quality of life. This paper presents SightAssist, a multilingual software solution designed to address the challenges faced by the visually impaired community in India. SightAssist utilizes a camera-based approach to serve as a digital prosthetic eye and virtual assistant, capable of currency detection, facial recognition, and providing various functionalities to assist with daily activities. Initially, a custom-labeled dataset has been prepared using a pre-trained YOLO model. Gradually, dataset pre-processing, normalization, and semantic segmentation are employed to optimize model performance. Additionally, voice-based accessibility features have been integrated using the Web Speech API, enabling users to interact with the application through voice commands. The experimental results show the successful development of SightAssist and its potential to significantly improve the lives of visually impaired people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Assistive technology

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Smart Agriculture: A Comprehensive Approach to Crop Disease Diagnosis using Offline Multilingual Technologies 6

Smart Agriculture: A Comprehensive Approach to Crop Disease ...

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

作者： Ashok, K. Mattew, Jimsha K. Chandana, K.S. Maharathi, G.N. Abhilash, G.M. Somani, Nithish Computer Science and Engineering New Horizon College of Engineering Bengaluru India Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331522667

This research study presents a comprehensive multilingual approach to crop disease diagnosis aimed at transforming agricultural practices. The proposed system leverages advanced deep learning models such as ResNet, EfficientNet, and MobileNet to automate plant phenotyping using high-resolution images. Beyond disease identification, it provides intelligent plant parenting recommendations to enhance crop breeding strategies. The system is equipped with multilingual support and offline capabilities, ensuring global accessibility, especially in areas with limited internet access. Additionally, the model delivers outputs by detecting diseases and offering diagnosis in the user’s native language, facilitating better decision-making for farmers. Experimental results from real-world scenarios demonstrate the system’s high accuracy and potential to drive sustainable agriculture, accelerate genetic progress, and improve crop prediction. © 2025 IEEE.

关键词： Smart agriculture

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GNSTAM: Integrating Graph Networks With Spatial and Temporal Signature Analysis for Enhanced Android Malware Detection

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IEEE Access 2025年 13卷 81326-81346页

作者： Sharma, Yogesh Kumar Tomar, Deepak Singh Pateriya, R.K. Solanki, Surendra Maulana Azad National Institute of Technology Department of Computer Science and Engineering Madhya Pradesh Bhopal462003 India Manipal University Jaipur Department of Artificial Intelligence and Machine Learning Rajasthan Jaipur303007 India

The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited adaptability. This paper introduces a hybrid methodology leveraging advanced machine learning techniques to enhance accuracy and adaptability in Android malware detection. It begins with collecting and preprocessing a comprehensive dataset of benign and malicious applications. An efficient Generative Adversarial Network (GAN) is employed to generate synthetic malware samples, effectively augmenting the dataset and enhancing the diversity of the malware samples under study process. To model the intricate relationships between applications, an efficient Graph Neural Network (GNN) process is utilized. Incorporating transformers, sequences of system and API calls are analyzed, harnessing this ability to discern patterns indicative of malicious activities. Additionally, a one-shot learning model tailored for the detection of new malware variants with minimal examples is introduced, enabling rapid adaptation to emerging threats. Federated learning preserves user privacy by training the model across a distributed network. A reinforcement learning model initiates proactive defenses, identifying optimal actions against malware threats. This methodology advances Android malware detection, showing over 5.9% improvement in detection accuracy, 4.5% reduction in false positives, and enhanced adaptability to new malware variants. It ensures enhanced security for Android users while preserving privacy. Evaluation results highlight its practical applicability in real-time scenarios. © 2013 IEEE.

关键词： Android malware

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 IEEE.

关键词： Differential privacy

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Identification of Infectious Disease Like COVID-19 Gene Biomarkers Using a Clear artificial intelligence Approach

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SN computer Science 2025年第4期6卷 1-13页

作者： Dubey, Shivendra Verma, Dinesh Kumar Kumar, Mahesh Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Jaypee University of Engineering and Technology MP Guna India

This study offers an approach that uses SARS-COV-2 mNGS (meta-genomic next-generation sequencing) samples to apply XAI (explainable artificial intelligence) methodologies derived from the use of machine learning methods. The research's data set contains 15,979 expressions of genes from 234 infected persons, of whom 39.68% (93) had SARS-COV-2 that was positive and 60.29% (141) had SARS-COV-2 that was negatives. The SARS-COV-2 related gene was chosen using the LASSO (least absolute shrinkage and selection operator) technique. The class imbalanced issue has been resolved using the SVM-SMOTE (Support Vector machine - Synthetic Minority Oversampling Technique) approach. To identify SARS-COV-2 related biomarkers possibilities and increase the comprehensibility of the final approach, an explainable strategy that utilized SHAP (SHAPley Additive exPlanations) as well as LIME (local interpretable model-agnostic explanations) approaches were used. The XGBoost (92.98% accuracy) approach performs better for the identification of infectious diseases like COVID-19 than the LR (91.17% accuracy), SVM (87.68% accuracy), and RF (91.18% accuracy) models. The three most significant genes connected to COVID-19 by the SHAP method were FAM83A, LGR6, and IFI27. The LIME findings demonstrated that the likelihood of a positive class was particularly influenced by the elevated degree of IFI27 expression of genes. Infectious diseases like COVID-19 could be accurately predicted using the suggested framework (XGBoost). The findings demonstrate that SHAP and LIME, along with the exploit of ML methods, it could be clarify the biological marker prognosis for infectious disease and give health center a clear knowledge about how potential risk factors affect the forecasts made by the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomarker Gene LIME SARS SHAP

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 12th

Hybrid Design for Privacy Preserved Image Representation in ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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A Novel AI-Assisted e-Consult Platform Integrating Deep learning for Enhanced Healthcare Access and Diagnostic Precision

A Novel AI-Assisted e-Consult Platform Integrating Deep Lear...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Vijayasekaran, G. Rajasree, R.S. Alam, Parvez Suryavanshi, Abhishek Ranjanagi, Vinayak Mulla, Umar Farooq New Horizon College of Engineering Dept of Computer Science and Engineering Bengaluru India Learning New Horizon College of Engineering Dept of Artificial Intelligence and Machine Bengaluru India

ISBN: (纸本)9798331509828

This study introduces the 'AI-Assisted E-Consult' initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult platform, serving as a digital interface that links patients, healthcare provider, and medical facilities. Utilizing state-of-the-art technologies such as AI and machine learning, the platform streamlines predictive health insights and appointment scheduling, thereby improving access to quality medical care. The platform employs Natural Language Processing (NLP) via the NLTK library and uses a Recurrent Neural Network (RNN) architecture trained on medical conversation datasets to power an AI chatbot. This chatbot analyzes symptoms and predicts potential health conditions, offering users preliminary insights into their health status. Furthermore, the system incorporates Haversine distance calculations to measure the geographical proximity between patients and doctors, enhancing logistical planning for consultations. To evaluate the platform's accuracy and performance, metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) will be used, ensuring precise predictions and operational effectiveness. The AI- Powered E-Consult platform is designed to eliminate healthcare barriers and maximize resource efficiency. Offering scalable, adaptable solutions tailored to the needs of contemporary healthcare, this platform represents a significant advancement toward a more accessible, efficient, and time-saving healthcare model, particularly for underserved populations. © 2025 IEEE.

关键词： Recurrent neural networks

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Next-Gen Dermatology: Revolutionizing Dermatological Diagnostics with AI and Emergency Care Solutions 3

Next-Gen Dermatology: Revolutionizing Dermatological Diagnos...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Yashas, D. Kashyap, M. Shivani Charan, P.R. Hemanth Kumar, G.P. Abhishek, B.K. Deekshith, C. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Dept of Information Science and Engineering Bengaluru India Bnm Institute of Technology Dept of Artificial Intelligence and Machine Learning Bengaluru India Siddaganga Institute of Technology Dept of Artificial Intelligence and Data Science Tumkur India Vidyavardhaka College of Engineering Dept of Computer Science and Engineering Mysore India

ISBN: (纸本)9798331515911

Skin health conditions should be diagnosed and treated on time so as to prevent complications and promote better health results. This paper presents an AI-based system that makes use of Convolutional Neural Networks with the InceptionV3 Architecture for predicting skin diseases at higher levels. The model, which is based on the large DermaNet dataset, classifies skin disorders fast and effectively. The system is coupled with a simple Telegram based application for the users to get a complete solution. A user can upload a photo of the affected skin area and get a diagnosis within seconds, an explanation of the disease and how severe it is in percentage. The bot also has a location and contact sharing feature that assists users in finding nearby doctors and clinics that can provide further assistance. It further provides on the spot appropriate cures for the particular condition diagnosed. Moreover, this makes it possible for the customer to fill the healthcare void in particular the neglected ones by making it easy for them to access real time high quality services. By equipping users with relevant information and appropriate actions required at the right time, this project has showcased how AI has the ability to transform the dermatological field. © 2025 IEEE.

关键词： Convolutional neural networks

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A Survey on Graph Neural Networks and its Applications in Various Domains

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SN computer Science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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