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Artificial intelligence in the detection and classification of dental caries

Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz university (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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Smart Flood Detection and Communication in Hazardous Zones via MANET-Based IoT and Parallely Distributed Slimmable Neural Networks

Smart Flood Detection and Communication in Hazardous Zones v...

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Sustainable Expert systems (ICSES), International Conference on

作者： V. Sathya Shuchi Bhadula R. Srinivasan Bhargav H K Bhavani Sankar Panda Amit Barve Department of Computer Science Periyar Maniammai Institute of Science and Technology (Deemed to be University) Vallam Thanjavur Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tiruvallur Tamil Nadu India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) and Associate Professor Shridevi Institute of Engineering and Technology Tumakuru Karnataka India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Limda Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET.

关键词： Temperature sensors Temperature measurement Accuracy Disasters Neural networks Disaster management Prediction algorithms Transformers Mobile ad hoc networks Load modeling

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Development of Autonomous Robots for Industrial and Domestic Applications

Development of Autonomous Robots for Industrial and Domestic...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： P. Ramadevi VijayaBhaskar Sadu Veeresh Fuskele Siva Nagaiah Bolleddu T. Ch. Anil Kumar M. Kalyan Chakravarthi Department of Computer Science Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Jawaharlal Nehru Technological University Kakinada Andhra Pradesh India Department of Mechanical Engineering BabulalTarabai Institute of Research and Technology Sironja Madhya Pradesh India Associate Professor of English and Coordinator KL-CDOE Koneru Lakshmaiah Education Foundation - Deemed to be University Guntur Andhra Pradesh India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi Andhra Pradesh India

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.

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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

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computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

关键词： 2D-LICM hyper-chaotic map with Walrus Optimization (2D-LICMHy-CM_WO) Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) Double Deep Q-Learning Network (DoubleDQN) Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE)

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Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four Switch Induction Motor Drive

Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four ...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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Dynamic fingerprint construction and tree generation approach for biometric template protection

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AIP Conference Proceedings 2023年第1期2917卷

作者： Ashoka Rajan Swaminathan Annadurai Abishi Chowdhury Ernest Johnson Assistant Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai India Associate Professor Paul J. Hill School of Business Faculty of Business Administration University of Regina Regina Saskatchewan Canada

This paper proposes a novel security method for protecting biometric fingerprint templates and storing them safely by creating a combined fingerprint template from different fingerprints, thereby creating a new virtual identity during enrolment. However, the second fingerprint image combined would be an image that was dynamically created by merging parts of several different images based on a merging algorithm used. We further extract the minutiae features from the two fingerprints. A combined fingerprint template is produced using the extracted information. Finally, the biometric values from the template are stored in a user-defined tree created. A new virtual identity is thus created to protect biometric templates from hackers and crackers. Even though the hacker has access to the database, they will not be able to match the biometric template with a person’s identity. Thus, when the database is stolen, the method avoids compromising complete minutiae features belonging to a single fingerprint. The proposed method achieves 0.3% FRR and 0.1% FAR. Hence, it balances both the recognition and security of the system.

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Real-time energy-efficient framework for multi-source harvesting and adaptive communication IIoT networks

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Sustainable Computing: Informatics and systems 2025年 47卷

作者： Kumari Priyanka Sinha Hosam Alden Riyadh Y.Mohana Roopa Saiyed Faiayaz waris Hatem S.A. Hamatta L. Bhagyalakshmi Shadab Alam Ali Algahtani Dept. of CSE Nalanda College of Engineering Chandi Bihar Engineering University Patna India Accounting and Finance department Faculty of Business Curtin University Malaysia Miri 98009 Malaysia Professor of Computer science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Department of Computer Science and Engineering Koneru Lakshmiah Education Foundation Vaddeswaram Andhra pradesh India Department of Applied Sciences Aqaba University College Al Balqa Applied University Aqaba Jordan Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Professor and Head Dept. of ECE Rajalakshmi Engineering College Chennai TN India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering King Khalid University Abha Asir 61421 Saudi Arabia Research Center for Advanced Materials Science (RCAMS) King Khalid University Guraiger Abha Asir 61413 Saudi Arabia

Over the past few years, Industrial Internet of Things (IIoT) devices have proliferated across modern manufacturing ecosystems, facilitating real-time monitoring, predictive analytics, and autonomous control. Nevertheless, maintaining these devices in low-resource contexts, especially in situations where the use of wired power is impossible and battery management is not feasible, is a significant challenge. Here, we introduce a new hybrid system that combines multi-source ambient energy harvesting (vibration, heat, and RF) with a lightweight adaptive communication protocol specifically designed for energy-limited industrial settings. Harnessing real-time energy buffer levels and environmental feedback, the architecture utilizes a central middleware engine to adapt transmission behaviors, routing decisions, and node activity states. We design and test detailed models to measure the performance of the framework on major metrics such as energy usage, communication delay, packet delivery ratio, and network sustainability. We utilize a synthetic industrial case scenario with 45 IoT nodes out-deployed across three zones we show the framework's strong benefits over baseline and partial methods, reaching as high as 40 % improvement in node life, 28 % improvement in sustained throughput, and substantial improvements in node availability and energy fairness. This study provides a basis for the installation of self-sustained, resilient IoT systems in realistic industrial environments, connecting the state of the art for energy autonomy design with the state of the art in the field of communication intelligence.

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Blockchain Confidence Protection and Cloud Chain Management Support

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： M Nagalakshmi K Saravanan Mohammad Jabirullah T Rajesh Kumar Associate Professor Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Associate Professor Department of Information Technology R.M.D Engineering College Kavaraipettai Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering CMR Engineering College Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Deemed to be University Guntur Andhra Pradesh India

Verification and completeness are main challenges for today's ever more diverse supply chains. Even with the ability to counter blockchain technologies by offering a trail of manipulation-resistant audit It does not address the confidence issue associated with the source chain activities also information related to a produce life cycle Information itself. Reputation mechanisms are a promising solution for this faith problem. Yet existing structures of credibility are Not ideal for supply chain applications based on blockchain as centred on restricted findings, lack of granularity and their overhead was not discussed and automation. We recommend the system as a three-layer faith in this job. Management platform is using a blockchain consortium tracking relationships between actors in the supply chain and Assign trust and prestige dynamically dependent on these interactions. Its novelty is based on a Model for credibility assessing product quality and the trust of individuals based on many observations funding for credibility qualities in supply chain incidents separating the member in the supply chain from the goods, enables brand credibility to be reserved for smart contracts for straightforward use by the same participant, Effective, secure, and automatic credibility scoring measurement, and the latency and throughput minimum overhead as compared to a straightforward supply chain model based on blockchain.

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Hydroponics Cultivation Using Real Time Iot Measurement system

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Journal of Physics: Conference Series 2020年第1期1712卷

作者： Piyush Mishra Laishram Jimmy Gabriel A. Ogunmola Tran Vang Phu A. Jayanthiladevi T. P. Latchoumi Department of Computer Engineering and Applications GLA University Mathura Uttar Pradesh India Department of Computer Science and Engineering Manipur Technical University Govt. Polytechnic Campus Imphal Manipur India Faculty of Business Sharda University Uzbekistan School of Law Can Tho University Professor-Computer Science & Information Science Srinivas University Karnataka India Department of Computer Science and Engineering Vignan's University Vadlamudi Guntur Andhra Pradesh

The concept hydroponic cultivation is performed in greenhouses or in various plat factories. This sort of cultivation is generally evaluated with pH and electrical conductivity. It may not provide complete information regarding any imbalance that is encountered in cultivation process. This causes poor yield or wastage of resources Thus, to overcome these limitation IoT measurement system has to be implanted in this cultivation process, where sensors and actuators may measure the corresponding reading when need and given to man power. Therefore, imbalance in attaining nutrients is eliminated by constant monitoring of resources towards plant cultivation. This facilitates farmers to handle the nutrients issues that are encountered in cultivation. The performance measurement of the system developed was computed with feasibility of IoT system for automatic measurements. The outcomes of the systems are computed and validated for further processing. Some specific measures are considered where there is no specific relationship among standardized analysis. The sensitive responses have to be examined and analyzed.

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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