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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Zade, Nikita Langote, Meher Verma, Prateek Faculty of Engineering & Technology Department of Artificial Intelligence & Data Science Maharashtra Sawangi442001 India Faculty of Engineering & Technology Department of Artificial Intelligence & Machine Learning Maharashtra Sawangi442001 India

ISBN: (纸本)9798331509675

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare. © 2025 IEEE.

关键词： Adversarial machine learning

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Real-Time Potholes Detection and Prevention Using Deep learning Techniques 8th

Real-Time Potholes Detection and Prevention Using Deep Learn...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Shabai, Iqbal Mahalle, Parikshit N. Shinde, Gitanjali Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Machine Learning Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789819766802

Road infrastructure safety and maintenance have received more attention recently due to the significant influence that it has on traffic flow and road user safety. Potholes are one common kind of road defect that seriously endangers drivers. Manual checks take a lot of time and may delay necessary repairs. In response to these challenges, a real-time pothole recognition system utilizing state-of-the-art computer vision and deep learning techniques was developed. The system leverages the Faster R-CNN model for precise pothole identification, enabling fast detection and timely repairs. The automated detection and instantaneous notification of authorities by the system enhances road safety and maintenance efficiency. This ingenious tactic improves the standard of the transportation network and creates roads safer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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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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Convergence Analysis of Probability Flow ODE for Score-Based Generative Models

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IEEE Transactions on Information Theory 2025年第6期71卷 4581-4601页

作者： Huang, Daniel Zhengyu Huang, Jiaoyang Lin, Zhengjiang Peking University Beijing International Center for Mathematical Research Center for Machine Learning Research Beijing100871 China University of Pennsylvania Department of Statistics and Data Science PhiladelphiaPA19104 United States Massachusetts Institute of Technology Department of Mathematics CambridgeMA02139 United States

Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical perspectives. Assuming access to L2 -accurate estimates of the score function, we prove the total variation between the target and the generated data distributions can be bounded above by Q(d3/4 δ1/2) in the continuous time level, where d denotes the data dimension and δ represents the L2 -score matching error. For practical implementations using a p-th order Runge-Kutta integrator with step size h, we establish error bounds of Q(d3/4 δ1/2 + d·(dhp) athe discrete level. Finally, we present numerical studies on problems up to 128 dimensions to verify our theory. © 1963-2012 IEEE.

关键词： Continuous time systems

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Toward a Race-Conscious Entrepreneurship Education

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Entrepreneurship Education and Pedagogy 2024年第2期7卷 161-189页

作者： Monroe-White, Thema McGee, Ebony Department of Technology Entrepreneurship and Data Analytics Campbell School of Business Berry College Mount Berry GA United States Department of Teaching and Learning Peabody College of Vanderbilt University Nashville TN United States

This article advocates for race-conscious science, technology, engineering/computer science, and mathematics (STEM) innovation and entrepreneurship (I&E) education. Results from a national survey of the academic plans and career trajectories of underrepresented and minoritized (URM) STEM students and alumni suggest that URM STEM students need robust opportunities to apply their scientific and engineering insights to societal problems, while empowering them to be entrepreneurs. Specifically, we find evidence of a relationship between racialized stress and racial activism for nascent URM STEM entrepreneurs among master’s and doctoral STEM students the majority of whom identify as African American/Black and/or Latinx/Hispanic/Chicano. Furthermore, entrepreneurial intent among Black STEMers is greatest among those who have experienced social suffering coupled with a desire for racial justice, and this relationship is amplified for students and alumni of minority serving institutions (MSIs). This study makes the case for advancing a critical and socio-historical scholarship of STEM I&E education to promote URM STEM entrepreneurship. © The Author(s) 2023.

关键词： Black and Latinx graduate students entrepreneurial intent racial activism racial justice equity ethic STEM entrepreneurship

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of Artificial Intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Predicting Agricultural Crop Damage Caused by Unexpected Rainfall Using Deep learning

Predicting Agricultural Crop Damage Caused by Unexpected Rai...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Fulkar, Bhushan Patil, Pawan Srivastav, Gaurav Mahale, Promod Maharashtra Wardha India Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798350375237

Efficient allocation of resources and timely agricultural interventions depend on the precise identification of crop loss at the field parcel level. Using recent data from 2018 to 2023, this study investigates the integration of deep learning techniques with real-time field photography to classify agricultural field parcels into those experiencing crop loss and those not. We build and assess deep learning models specifically for crop loss classification using a dataset from the Finnish Food Authority (FFA) that combines on-the-ground photos taken during field visits with comprehensive field parcel information. In this work, we use convolutional neural networks (CNNs) to examine the visual characteristics that we have extracted from field photos in order to identify the subtleties that may indicate crop health or possible loss. Our goal is to improve the model's generalization capabilities over a variety of crop types and agricultural landscapes by training it on a combination of field parcel data and corresponding photographs. © 2024 IEEE.

关键词： Image segmentation

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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

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Soft Computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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A Review on the Role of Artificial Intelligence in Personalized learning

A Review on the Role of Artificial Intelligence in Personali...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Qureshi, Rukaiyya Hajare, Shital Verma, Prateek Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798350361537

This article explores the revolutionary role of Artificial Intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles. © 2024 IEEE.

关键词： Federated learning

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Membership Functions, Set-Theoretic Operations, Distance Measurement Methods Based on Ambiguous Set Theory: A Solution to a Decision-Making Problem in Selecting the Appropriate Colleges

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International Journal of Fuzzy Systems 2023年第4期25卷 1311-1326页

作者： Singh, Pritpal Huang, Yo-Ping Deep Learning Lab Department of Data Science and Analytics Central University of Rajasthan Rajasthan Ajmer305817 India Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan

To deal with uncertainty in a very precise way, the ambiguous set theory has been proposed. This theory supports the representation of ambiguities in terms of four membership degrees, namely "true," "false," "true-ambiguous," and "false-ambiguous." These membership degrees are linearly dependent on each other, because they are defined over the same event. However, these four membership degrees have the difficulty of unclear margins that prevent them to be separated from each other in the ambiguities representation of an event. To address this problem of unclear margins in the ambiguous set, the membership degrees are defined using four different functions, and called ambiguous membership functions (AMFs). These membership degrees are defined in such a way that their sum should be less than or equal to 2, and the individual membership degree should be in the range [0, 1]. To visually represent the uncertain margins for the membership degrees, the concept of an ambiguous region (AR) is introduced. In this study, various set-theoretic operations, their properties, and methods for measuring distances in support of the ambiguous sets are discussed. Finally, we discuss a real-time application of the ambiguous set theory with respect to solving the decision-making problem of choosing the appropriate colleges for students. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Membership functions

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