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Toward Human-centered XAIin Practice:A survey

Machine Intelligence Research 2024年第4期21卷 740-770页

作者： Xiangwei Kong Shujie Liu Luhao Zhu Department of Data Science and Management Engineering School of ManagementZhejiang UniversityHangzhou 310058China

Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI *** AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human ***,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI *** first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of *** addition,a taxonomy of XAI methods is developed for visual computing with analysis of method *** clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in *** we check the availability of open-source XAI tools for stakeholders'*** survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.

关键词： Artificial intelligence(AI)application explainable AI(XAI) human-centered design visual computing medical diagnosis.

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A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

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Journal of Safety science and Resilience 2024年第2期5卷 167-178页

作者： Gangqiao Wang Han Xing Yongqiang Chen Yi Liu Institute of Big Data Inteligent Management and Decision-making College of ManagementShenzhen UniversityShenzhen 518060China School of Safety Science Tsinghua UniversityBejing 100084China Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBejing 100871China

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep *** to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of *** this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep *** the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future ***-time data is then incorporated into diverse forecasts to dynamically adjust the simulation *** ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually *** approach has been applied to a human-involved car-following system simulation under complex traffic *** results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

关键词： Disaster modeling and analysis Deepuncertainty Hybrid modeling Adaptivesimulation

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Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis 3

Plant Fettle Detector: Unifying Fruit Quality Tracking and P...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nair, Sruthi Bajpai, Khushi Fadnavis, Sakshi Jain, Shruti Sheikh, Zeenat Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering - Data Science Nagpur44013 India

ISBN: (纸本)9798350381931

India being an agricultural country, food quality tracking is a major challenge faced by common farmers across the country. This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture - fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses. The research explores architectural nuances, development methodologies, and deployment on Google Cloud Platform. Testing shows high accuracy rates of 98.5% for fruit quality and 98.83% for plant disease detection. The system's transformative potential in agriculture is highlighted, bridging the gap between technology and sustainable food production systems. © 2024 IEEE.

关键词： Image analysis

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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Cardiac Health Assessment System Using Machine Learning Algorithm 4

Cardiac Health Assessment System Using Machine Learning Algo...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Jadhav, Rahul Jamdar, Anuj Thakur, Sonal Biradar, Santosh G H Raisoni College of Engineering and Management Department of Data Science Pune India

ISBN: (纸本)9798331518981

The Cardiac Health Assessment System leverages machine learning algorithms to enhance the early detection and management of cardiovascular diseases (CVDs). By analyzing patient information including medical background, lifestyle factors, and results from diagnostic tests the system can accurately predict the likelihood of cardiac events. Classification models like calculated relapse, choice trees, and Credulous Bayes are used to categorize patients into different risk groups, aiding healthcare providers in making well-informed decisions. The system also offers personalized health recommendations to mitigate identified risks, thereby improving patient outcomes and reducing the burden on healthcare resources. Continuous learning is embedded within the model, enabling it to refine predictions based on new data. This proactive approach aims to shift CVD management from reactive treatment to preventative care, reducing CVD-related mortality and enhancing overall heart health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Smart Content management System with Subscription Services 4

Smart Content Management System with Subscription Services

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Borse, Saurabh Jamdade, Anuj Joshi, Yash Vani, Sunita G H Raisoni College of Engineering and Management Department of Data Science Pune India

ISBN: (纸本)9798331518981

The "Smart Content management System with Subscription Services"project focuses on creating a sophisticated web-based platform for sharing and accessing various types of digital content, including text, images, and videos. The platform supports two main user roles: User A (content consumers) and User B (content creators). While User A has free access to standard content, premium content produced by User B requires a subscription, offering a revenue stream for creators and added value for subscribers. Built using modern web technologies like HTML, CSS, Python, JavaScript, Django, and Bootstrap, with PostgreSQL for reliable data management, the platform is designed for scalability, security, and efficiency. Cloud-based deployment ensures high reliability and scalability, addressing the growing needs of an expanding user base [1], [3]. The platform aims to transform content sharing by providing a seamless experience for both creators and consumers. It promotes high-quality content creation through monetization while offering users a broad range of valuable resources. The integration of multiple content formats increases the platform's versatility, catering to diverse user preferences and enhancing engagement. © 2024 IEEE.

关键词： Cloud platforms

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Deep ensemble optimal classifier-based software defect prediction for early detection and quality assurance

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Journal of Umm Al-Qura University for engineering and Architecture 2025年 1-12页

作者： Srinivas, Kalyanapu Janapati, Krishna Chaithanya Sai Kiran, Mungalched Sudheer Reddy, Davu Vinay, Thota Vamshi Yashwanth, Choula Department of Computer Science and Engineering Dhanekula Institute of Engineering and Technology Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering (Data Science) Malla Reddy Engineering College and Management Sciences Medchal Telangana Kistapur India

Software Defect Prediction (SDP) is critical in identifying fault-prone modules during the software development life cycle, enhancing software quality, and reducing maintenance costs. However, existing SDP models face significant challenges, including high false positive rates and severe class imbalance between defect-prone and non-defect modules, which hinder practical model training and accurate defect detection. To overcome these challenges, this study proposes a robust and efficient SDP architecture that integrates several advanced techniques to enhance predictive accuracy and model reliability. The methodology begins with comprehensive data preprocessing, incorporating normalization to standardize feature scales and outlier detection to eliminate noise and improve data quality. To address the inherent class imbalance in the dataset, the minority oversampling by synthetic data (MOSD) technique is employed to generate synthetic samples for the minority class, thus achieving a more balanced distribution. Following this, an Adaptive sequential K-best (ASKB) feature selection strategy is implemented to identify and retain the most relevant features, effectively reducing dimensionality while preserving critical information. Finally, the weighted random forest (WRF) classifier is utilized, which assigns appropriate weights to training instances, enabling improved classification performance, especially for minority class instances. The proposed methodology achieved an accuracy of 99.119%, a precision of 99.431%, a recall of 99.122%, and an F1-score of 99.333%. © The Author(s) 2025.

关键词： Adaptive sequential K-best Class imbalance data preprocessing Feature selection Minority oversampling Predictive modeling Software defect prediction Synthetic data Weighted random forest

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Enhanced Crop Yield Forecasting Using Deep Reinforcement Learning and Multi-source Remote Sensing data

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 426-442页

作者： Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Malhan, Anu Department of Computer Engineering New Horizon Institute of Technology and Management Thane India Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Thane India

Accurate crop yield predictions are essential for agricultural planning and food security. Traditional methods relying on historical data are enhanced by modern techniques using satellite imagery, weather data, and soil conditions. This study presents an advanced crop yield forecasting model integrating deep reinforcement learning and multi-source remote sensing data. The Hybrid Reinforcement-Supervised Learning (HRSL) model combines reinforcement learning and supervised learning for enhanced classification and decision-making. Machine learning and artificial intelligence techniques analyze historical yield data and current season variables, improving prediction accuracy. Preprocessing steps like imputation, normalization, and atmospheric correction ensure robust datasets. Feature extraction with cascaded artificial neural networks (CANN) automates the learning of complex data relationships. Implemented using Python, this methodology achieves an accuracy of 97.8%. The proposed model demonstrates superior performance in yield prediction, evaluated through rigorous accuracy, precision, and training efficiency metrics. Integrating multi-source data with advanced machine learning models offers significant benefits for precision agriculture and food security. Future work will expand these techniques to broader applications and continuously improve predictive models with evolving data and computational capabilities. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Crop yield forecasting Deep reinforcement learning Food security Machine learning Multi-source data integration Precision agriculture Remote sensing

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A Survey on: Design, Implementation, and Evaluation of a Secure and Anonymous Communication Platform Utilizing the TOR Network for Enhanced Privacy and data Protection 4

A Survey on: Design, Implementation, and Evaluation of a Sec...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Biradar, Santoshkumar Parsewar, Parth Suresh Mishra, Nilamadhab Galakatu, Ankita Atul Gandewar, Sarthak Santosh G H Raisoni College of Engineering & Management Pune Department Of Cyber Security & Data Science Pune India

ISBN: (纸本)9798331518981

This study presents an advanced anonymous messaging platform that integrates Tor network, VPN, and dynamic IP rotation to provide strong privacy protection for users. Tor network onion routing masks user identities with multilayer encryption between distributed nodes, while a VPN adds an encrypted tunnel, improving security by hiding the user's real IP address. The main innovation is the automatic rotation of the IP address of both the sender and the receiver every 10 seconds, which prevents backtracking and significantly complicates traffic analysis. This dynamic mechanism ensures that even if a user's communication is intercepted, it becomes impractical to trace it back to its original source or destination. By addressing weaknesses such as Tor network's potential exit node tracking and risks of VPN One Content Failure, the platform effectively reduces timing attacks and traffic correlation. The design balances latency and performance, ensuring minimal disruption while maintaining high scalability and resilience against adversaries. The combination of these technologies provides a state of the art solution for secure and private anonymous communication. Keywords: anonymous communication, Tor network, VPN, dynamic IP rotation, onion routing © 2024 IEEE.

关键词： Anonymity

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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