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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur B M Nalini Zabiha Khan S Nayana Prasad N Achyutha G Manjula Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE BGS College of Engineering and Technology Bangalore India

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work.

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Introduction to the Minitrack on Learning Analytics 2023

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2023年 2023-January卷 1269-1270页

作者： Willermark, Sara Islind, Anna Sigridur Deeva, Galina Óskarsdóttir, Mari University West School of Business Economics and IT Sweden Reykjavik University Department of Computer Science Iceland KU Leuven Research Centre for Information Systems Engineering Belgium

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HeteroSys: Heterogeneous and Collaborative Sensing in the Wild

HeteroSys: Heterogeneous and Collaborative Sensing in the Wi...

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International Conference on Smart Computing (SMARTCOMP)

作者： Indrajeet Ghosh Adam Goldstein Avijoy Chakma Jade Freeman Timothy Gregory Niranjan Suri Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Department of Information Systems University of Maryland Baltimore County United States Department of Electrical Engineering and Computer Science UC Berkeley United States DEVCOM Army Research Lab Department of Computer Science Bowie State University United States

Advances in Internet-of-Things, artificial intelligence, and ubiquitous computing technologies have contributed to building the next generation of context-aware heterogeneous systems with robust interoperability to control and monitor the environmental variables of smart environments. Motivated by this, we propose HeteroSys, an end-to-end multi-functional smart IoT-based system prototype for heterogeneous and collaborative sensing in a smart IoT-based environment. A unique characteristic of HeteroSys is that it relies on Home Assistant (HA) to collate heterogeneous sensors (e.g., passive infrared sensors (PIR), reed (door) switches, object tags, wearable wrist-mounted, water leak sensors, and internet protocol cameras), and uses a variety of networking protocols such as Zigbee open standard for mesh networking, WiFi, and Bluetooth Low Energy (BLE) for communication. The reliance on HA (and its broad community support) makes HeteroSys ideal for various applications such as object detection, human activity recognition and behavior patterns. We articulated the development phase, integration, testing challenges and evaluation of the HeteroSys. We conducted an extensive 24-hour longitudinal data collection from 5 participants performing 6 activities by deploying in an indoor home environment. Our assessment of the acquired dataset reveals that the representations learned using deep learning architecture aid in improving the detection of activities to 83.1% accuracy.

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An Enhanced Diagnostic System using Deep Learning for Early Prediction of Pancreatic Cancer

An Enhanced Diagnostic System using Deep Learning for Early ...

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International Conference on Electrical Energy systems (ICEES)

作者： Kumbham Bhargavi M Lakshmi Prasad Arun G Pundru Chandra Shaker Reddy S Yuvalatha M Naga Triveni Department of CSE (AI & ML) Keshav Memorial Institute of Technology Hyderabad Telangana India Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and Business Systems Anna University Bannari Amman Institute of Technology Tamil Nadu India Department of CSE (AI&ML) St. Martin's Engineering College Hyderabad Telangana India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

With a dismal 5-year survival-rate of approximately 7%, pancreatic-cancer(PC) remains one of the world's most lethal tumors. In order to improve patient survival rates, early detection of PC is essential. computerized-tomography(CT), MRI combined with MRCP, or a biopsy is necessary for the diagnosis of PC. The steps involved in the suggested CAD design process are as follows: picture preprocessing, segmentation, feature-extraction & classificatiion. Color conversion and the isotropic diffusion filter method are utilized for preprocessing. The next step is the segmentation processes' usage of the suggested Fuzzy K-NN Equality code. A classification tool that makes use of Deep Learning is feature extraction. Using the characteristics gathered from the pancreatic sample, tumor cells are categorized. The image classification criterion includes both the train values and the testing datasets. To identify pancreatic cancer, an algorithm called DCNN_DBN is employed, which combines Deep-Convolutional Neural-Networks with Deep-Belief-Networks. The results of the experiments show that the present CAD system has great promise and is safe for the automated diagnosis of benign & malignant tumors, with an accuracy rate of 99.8 percent. The use of this classifier greatly reduces the computational complexity. More anomalies in pancreatic cancer cells could be detected with an improved version of the proposed method.

关键词： Deep learning Image segmentation Accuracy Sensitivity Image color analysis Databases Roads Pancreatic cancer Classification algorithms Testing

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Implementation of a Physically Unclonable Function using LEDs and LDRs 12

Implementation of a Physically Unclonable Function using LED...

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12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Hristov, Emil Picos, Rodrigo De Benito, Carol Stavrinides, Stavros G. Arul, Tolga Anagnostopoulos, Nikolaos A. Moner Al Chawa, M. Universitat de les Illes Balears Industrial Engineering Dept. Palma Spain International Hellenic University Physics Department Kavala Greece University of Passau Faculty of Computer Science and Mathematics Passau Germany Technical University Dresden Institute of Circuits and Systems Dresden Germany

ISBN: (纸本)9798350321074

This work introduces a novel Physically Unclonable Function (PUF) based on the characteristics of Light-Emitting Diodes (LEDs) and Light-Detecting Resistors (LDRs) as long as these are combined. Specifically, we profit from the variations in the optical output of an LED caused by process variations, as well as from the variations in LDRs. In this way, our work allows for providing a PUF based on optical components, whose electrical response is a function of the used components. Notice that the electrical response refers not only to the static but also to the dynamical behavior. This way, a unique, double-component system can be easily constructed. © 2023 IEEE.

关键词： Light emitting diodes

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DEEP MEMORY SEARCH: A METAHEURISTIC APPROACH FOR OPTIMIZING HEURISTIC SEARCH

arXiv

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arXiv 2024年

作者： Hedar, Abdel-Rahman Abdel-Hakim, Alaa E. Deabes, Wael Alotaibi, Youseef Bouazza, Kheir Eddine Department of Computer Science Faculty of Computers & Information Assiut University Assiut71526 Egypt Computer Science Department in Jamoum Umm Al-Qura University Electrical Engineering Department Assiut University Assiut71516 Egypt Texas A&M University-San Antonio One University Way Hall 334 San AntonioTX78224 United States Department of Computer Science College of Computer and Information Systems Umm Al-Qura University Makkah24381 Saudi Arabia Computer and Information Science Division Higher Colleges of Technology Abu Dhabi United Arab Emirates

Metaheuristic search methods have proven to be essential tools for tackling complex optimization challenges, but their full potential is often constrained by conventional algorithmic frameworks. In this paper, we introduce a novel approach called Deep Heuristic Search (DHS), which models metaheuristic search as a memory-driven process. DHS employs multiple search layers and memory-based exploration-exploitation mechanisms to navigate large, dynamic search spaces. By utilizing model-free memory representations, DHS enhances the ability to traverse temporal trajectories without relying on probabilistic transition models. The proposed method demonstrates significant improvements in search efficiency and performance across a range of heuristic optimization *** Codes I.2.6 © 2024, CC BY-NC-ND.

关键词： Constrained optimization

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases 24

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Al Mamlook, Rabia Emhamed Nasayreh, Ahmad Gharaibeh, Hasan Aljaidi, Mohammad Al-Na'amneh, Qais Alzu'bi, Dalia Bzizi, Hanin Abdeltawab, Abdelrahman Trine University Dep. of Business Administration Indiana United States University Zawia Department of Industrial Engineering Tripoli Libya Yarmouk University Dep. Computer Science Irbid Jordan Zarqa University Dep. of Computer Science Zarqa Jordan Applied Science Private University Dep. of Cyber Security Amman Jordan Concordia University Dep. Information and Systems Engineering Montreal Canada Western Michigan University Dep of Biomedical Science KalamazooMI49008 United States Bani Sweif University Faculty of Pharmacy Bani Suwayf Egypt

ISBN: (纸本)9798350384307

This paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. Through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. The outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology. © 2023 IEEE.

关键词： Deep Learning Eye Disease Fine Tuning Transfer Learning

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Bilateral-Head Region-Based Convolutional Neural Networks: A Unified Approach for Incremental Few-Shot Object Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4376-4390页

作者： Li, Yiting Zhu, Haiyue Tian, Sichao Ma, Jun Xiang, Cheng Vadakkepat, Prahlad National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research 138634 Singapore Peking Union Medical College Institute of Medicinal Plant Development Chinese Academy of Medical Sciences Beijing100193 China Robotics and Autonomous Systems Thrust Guangzhou511453 China The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong SAR Hong Kong Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen Futian518045 China

Practical object detection systems are highly desired to be open-ended for learning on frequently evolved datasets. Moreover, learning with little supervision further adds flexibility for real-world applications such as autonomous driving and robotics, where large-scale datasets could be prohibitive or expensive to obtain. However, continual adaption with small training examples often results in catastrophic forgetting and dramatic overfitting. To address such issues, a compositional learning system is proposed to enable effective incremental object detection from nonstationary and few-shot data streams. First of all, a novel bilateral-head framework is proposed to decouple the representation learning of base (pretrained) and novel (few-shot) classes into separate embedding spaces, which takes care of novel concept integration and base knowledge retention simultaneously. Moreover, to enhance learning stability, a robust parameter updating rule, i.e., recall and progress mechanism, is carried out to constrain the optimization trajectory of sequential model adaption. Beyond that, to enforce intertask class discrimination with little memory burden, we present a between-class regularization method that expands the decision space of few-shot classes for constructing unbiased feature representation. Final, we deeply investigate the incomplete annotation issue considering the realistic scenario of incremental few-shot object detection (iFSOD) and propose a semisupervised object labeling mechanism to accurately recover the missing annotations for previously encountered classes, which further enhances the robustness of the target detector to counteract catastrophic forgetting. Extensive experiments conducted on both Pascal visual object classes dataset (VOC) and microsoft common objects in context dataset (MS-COCO) datasets demonstrate the effectiveness of our method. © 2020 IEEE.

关键词： Feature extraction

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Exploring Equilibrium Strategies in Network Games with Generative AI

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu The College of Communications Engineering Army Engineering University PLA Nanjing210000 China The School of Computer Science and Engineering NTU Singapore The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 2024, CC BY.

关键词： Game theory

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A Cross-subject Transfer Learning Method for CSI-based Wireless Sensing

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IEEE Internet of Things Journal 2025年

作者： He, Zhengran Bouazizi, Mondher Gui, Guan Ohtsuki, Tomoaki Keio University School of Science for Open and Environmental Systems Kanagawa 223-0061 Japan Keio University Department of Information and Computer Science Yokohama Japan Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China

WiFi-based passive non-contact sensing is widely regarded as a leading technology in wireless sensing, owing to its extensive application scope and favorable growth outlook. Nevertheless, although current WiFi-based sensing techniques attain remarkable accuracy in identifying activities within particular scenarios, they need stronger generalization capabilities across different targets and environments, hindering further commercial development. To address this issue, this paper uses CNN, BLSTM, and Attention layers to propose a cross-subject transfer learning method based on the CNN-ABLSTM algorithm model. This method combines widely used transfer learning methods with deep neural network algorithms in cross-domain sensing. Specifically, this method leverages the performance advantages of the CNN-ABLSTM algorithm model in processing time-series data like channel state information (CSI) and utilizes transfer learning to fine-tune the pre-trained model from the source domain for application in the target domain with different subjects. This enables faster and more accurate achievement of cross-subject tasks. The simulated results show that the proposed new approach achieves higher recognition accuracy and shorter training times than traditional transfer learning methods for cross-subject tasks. In testing with the dataset used, it achieves up to around 85% performance of activity recognition accuracy in cross-subject tasks. © 2014 IEEE.

关键词： Contrastive Learning

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