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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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Machine Learning Empowered Electricity Consumption Prediction

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computers, Materials & Continua 2022年第7期72卷 1427-1444页

作者： Maissa A.Al Metrik Dhiaa A.Musleh Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

Electricity,being the most efficient secondary energy,contributes for a larger proportion of overall energy *** to a lack of storage for energy resources,over supply will result in energy dissipation and substantial investment *** electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as:smart distributed grids,assessing the degree of socioeconomic growth,distributed system design,tariff plans,demand-side management,power generation planning,and providing electricity supply stability by balancing the amount of electricity produced and *** paper proposes amedium-termprediction model that can predict electricity consumption for a given location in Saudi ***,this study implemented a standalone ArtificialNeuralNetwork(ANN)model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi *** dataset used in this research is gathered exclusively from the Saudi Electric *** pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine *** number of neurons and learning rate of the standalone model were optimized using hyperparameter ***,the standalone model was tested against the bagging ensemble using the optimized *** bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN *** results for the proposed model produced 0.9116 Correlation Coefficient(CC),0.2836 Mean Absolute Percentage Error(MAPE),0.4578,Root Mean Squared Percentage Error(RMSPE),0.0298 MAE,and 0.069 Root Mean Squared Error(RMSE),respectively.

关键词： Electricity consumption prediction artificial neural network machine learning

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Optimizing Data-Driven Federated Learning in UAV Networks 30

Optimizing Data-Driven Federated Learning in UAV Networks

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Li, Datian Xiao, Mingjun Xu, Yin Wu, Jie University of Science and Technology of China School of Computer Science and Technology & Suzhou Institute for Advanced Research China Temple University Department of Computer and Information Sciences United States

ISBN: (纸本)9798331515966

Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative three-layer architecture. In this architecture, UAVs, inherently lacking data, serve as mobile clients and are tasked with periodically collecting desired data from a group of Points of Interest (PoIs) for FL training. Meanwhile, a Base Station (BS) coordinates these UAVs to efficiently train a high-quality global model. Our objective is to determine a data collection strategy for each UAV to minimize the time required to meet the global model's loss requirement within the constraint of energy consumption. Through theoretical analysis, we establish a bound for the convergence speed of DDFL and quantify the impact of collecting data from different PoIs on the global model's loss function. Leveraging these analyses, we formulate the PoI selection problem as a novel two-stage Combinatorial Multi-Armed Bandit (CMAB) problem with multiple constraints. We then propose an Adaptive Two-stage CMAB-based algorithm, named FedATC, to jointly optimize the data collection route and UAV velocity. Extensive simulations demonstrate that FedATC significantly reduces the time required to achieve desired model quality compared to state-of-the-art algorithms. © 2024 IEEE.

关键词： Federated learning

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Towards Dynamic University Course Timetabling Problem: An Automated Approach Augmented via Reinforcement Learning 24

Towards Dynamic University Course Timetabling Problem: An Au...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Xiao, Yanan Li, XiangLin Jiang, Lu Wang, Pengfei Wang, Kaidi Luo, Na School of Computer Science and Information Technology Northeast Normal University China Dalian Maritime University Department of Information Science and Technology China Computer Network Information Center Chinese Academy of Sciences China School of Business Macau University of Science and Technology China

ISBN: (纸本)9798331506681

University Course Timetabling Problem (UCTP) is a significant resource allocation challenge with NP-hard characteristics. As problem sizes increase, finding an optimal solution becomes increasingly complex. To address this, we propose an automated planning method using Reinforcement Learning (RL), which treats UCTP as a series of dynamic decision-making tasks. The RL agent acts as an automated planner, operating in a simulated environment that reflects the complexity and constraints of a university course. It adapts to changes in the timetable by evaluating the outcomes of its actions, continuously refining its strategy to identify and implement the most effective actions. During the explore phase, the agent tests new strategies using a priority-driven reward system and an experience replay mechanism. This approach replays high-value actions, enhancing solution diversity and reducing the search space. In the exploit phase, the agent leverages its accumulated knowledge to apply proven effective strategies. It also incorporates unbiased external evaluations to avoid local optima and ensure global optimization of its strategy. Extensive testing on multiple real-world datasets shows that our automated planning framework consistently outperforms existing methods in various complex scheduling scenarios. This confirms its effectiveness and practicality in meeting diverse scheduling needs. © 2024 IEEE.

关键词： Reinforcement learning

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Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications

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Journal of Mobile Multimedia 2023年第2期19卷 451-476页

作者： Ranjith, C. Prasanna Hardas, Bhalchandra M. Khaja Mohideen, M. Syed Nijil Raj, N. Robert, Nismon Rio Mohan, Prakash Faculty in Information Technology Department University of Technology and Applied Sciences Shinas Oman Electronics Engineirng Dept Shri Ramdeobaba College of Enginering and Management Nagpur India Department of Information Technology University of Technology and Applied Science Salalah Oman Department of Computer Science and Engineering Younus College of Engineering and Technology Kollam India Department of Computer Science Christ University Bangalore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. © 2022 River Publishers.

关键词： computer vision deep learning image processing object detection Surveillance unmanned aerial vehicles

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Privacy preserving large language models: ChatGPT case study based vision and framework

IET Blockchain

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IET Blockchain 2024年第S1期4卷 706-724页

作者： Ullah, Imdad Hassan, Najm Gill, Sukhpal Singh Suleiman, Basem Ahanger, Tariq Ahamed Shah, Zawar Qadir, Junaid Kanhere, Salil S. School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW Australia Higher Colleges of Technology Abu Dhabi United Arab Emirates School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Management Information Systems Department College of Business Administration Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Technology Sydney International School of Technology and Commerce SydneyNSW Australia Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Sydney Australia

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy-preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre-processing and preserving private context and the private training process for large-scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy;specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and

关键词： Blockchain

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Lightweight Brain Tumor Diagnosis via Knowledge Distillation 7

Lightweight Brain Tumor Diagnosis via Knowledge Distillation

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7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024

作者： Anantathanavit, Rungpilin Raswa, Farchan Hakim Thaipisutikul, Tipajin Wang, Jia-Ching Mahidol University Faculty of Information and Communication Technology Nakhon Pathom Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350368437

Brain tumors pose a significant medical challenge, necessitating precise and rapid diagnosis for effective treatment and improved patient outcomes. This paper introduces knowledge distillation, which has the potential to revolutionize brain tumor diagnosis by enabling early identification from medical imaging data. Using a sophisticated teacher' model to capture intricate patterns, we distill this knowledge into a more efficient "student' model, aiming for comparable accuracy with reduced memory usage and improved inference times. Our method, based on a dataset of 357 MRI scans, demonstrated the potential of knowledge distillation in brain tumor diagnosis, offering a promising avenue for advancing patient care. The proposed model serves as a vital tool for healthcare practitioners, providing accurate and efficient support in detecting brain tumors and contributing to advancements in healthcare technology. The evaluation results indicate the effectiveness of our technique, achieving an impressive accuracy of 98.10 © 2024 IEEE.

关键词： Patient treatment

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IoT-based Heartbeats Monitoring system 9

IoT-based Heartbeats Monitoring System

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Tamron, Mohamad Asyraf Noorza Bin Mohd Attarbashi, Zainab S. Abuzaraida, Mustafa Ali Atitallah, Nesrine Iftikhar, Saman Handayani, Dini Oktarina Dwi Basri, Atikah Balqis Binti International Islamic University Malaysia Kulliyyah of Information & Communication Technology Selangor Malaysia International Islamic University Malaysia Faculty of Information & Communication Technology Department of Computer Science Selangor Malaysia Misurata University Faculty of Information Technology Computer Science Department Libya Arab Open University Faculty of Computer Studies Saudi Arabia International Islamic University Malaysia Faculty of Information and Communication Technology Department of Information Systems Selangor Malaysia

ISBN: (纸本)9798350370126

The advent of the Internet of Things (loT) has opened up a realm of possibilities in developing globally accessible systems and devices. With the current state of internet speeds, these systems offer real-time control, making them more powerful than ever. Simultaneously, the healthcare sector has been witnessing notable advancements, but it also grapples with an increasing number of health issues, with heart problems standing out as a prominent concern in many countries. These problems manifest in various symptoms, some of which can lead to more serious conditions, such as irregular heartbeats. In response to these challenges and leveraging cutting-edge technology, researchers have embarked on endeavors to enhance healthcare devices and render them more readily available to medical professionals. This paper explores the merging of loT technology with healthcare, specifically focusing on the development of a system designed to monitor and record a patient's heartbeat. This is achieved through the deployment of a Heartbeat/SpO2 Sensor, and the data is seamlessly transmitted to a mobile application accessible remotely. The outcomes of this study demonstrate that the heartbeat data captured by Arduino is effectively transmitted to the cloud, meeting the system's predefined objectives. This represents a significant stride in harnessing loT's potential to enhance healthcare practices and patient monitoring. © 2023 IEEE.

关键词： Internet of things

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Shortest Cover After Edit 35

Shortest Cover After Edit

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35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (纸本)9783959773263

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string C is called a cover (as known as a quasi-period) of a string T if each character of T lies within some occurrence of C. By definition, a cover of T must be a border of T;that is, it occurs both as a prefix and as a suffix of T. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size O(n) that computes the longest border and the shortest cover of the string in O(log n) time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string T of length n, where is the length of the string being inserted or substituted. The data structure can be constructed in O(n) time given string T. © Kazuki Mitani, Takuya Mieno, Kazuhisa Seto, and Takashi Horiyama.

关键词： Data structures

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Machine Learning-Based Framework for Cybersecurity of Robotic system 6th

Machine Learning-Based Framework for Cybersecurity of Roboti...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Verma, Narinder Kumar, Neerendra Salotra, Harish Department of Computer Science and Information Technology Central University of Jammu Jammu181143 India

ISBN: (纸本)9789819778614

Robotic systems are increasingly integrated into sectors ranging from manufacturing to health care, presenting significant cybersecurity challenges. Ensuring the security and integrity of these systems is critical to prevent malicious attacks that could compromise functionality and safety. This study introduces a machine learning-based framework designed to enhance the cybersecurity of industrial robotic systems. Leveraging the CICIDS 2017 dataset, which provides a comprehensive collection of network traffic data representing modern cyber threats, the framework demonstrates high efficacy in detecting and mitigating such threats. Experimental results show that the K-Nearest Neighbors and Decision Tree algorithms achieve accuracy rates of 99.01% and 99.83%, respectively, underscoring the potential of these techniques for real-time intrusion detection. These findings highlight the framework's effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. The findings highlight the framework effectiveness in protecting robotic systems against cyber-attacks, thereby fortifying their security in various application domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyber attacks

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