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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Rahal, Hakima Rym Slatnia, Sihem Kazar, Okba Harous, Saad University of Mohammed Khider Linfi Laboratory Computer Science Department Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331542573

Breast cancer is a condition brought on by the uncontrollable growth and multiplication of aberrant cells in the breast tissue. The misdiagnosis of breast cancer can result in needless or delayed therapies, mental and physical health, increased anxiety and stress, and decreased survival rates for patients. Also, the exchange of healthcare data is difficult to secure due to strict privacy laws, online threats, and the possibility of human error. Blockchain technology can enhance the security and privacy of patient data, give patients greater control over their health data, and facilitate safe data exchange between healthcare providers. In this study, we propose a Blockchain-based Ensemble Deep Learning Model method that can classify Breast Cancer either Malignant or Benign. This method is built on combining different breast cancer classification ANN models and sharing them through blockchain to use their weights to train and optimize the performance of a final model. This model achieved 98% accuracy, and showed a promising in protecting patients from fatal diagnosis mistakes. © 2024 IEEE.

关键词： Differential privacy

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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Cervical Cancer Prediction Empowered with Federated Machine Learning

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computers, Materials & Continua 2024年第4期79卷 963-981页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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A Mutual Information-Based Assessment of Reverse Engineering on Rewards of Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1089-1100页

作者： Chen, Tong Liu, Jiqiang Baker, Thar Wu, Yalun Xiang, Yingxiao Li, Yike Niu, Wenjia Tong, Endong Zomaya, Albert Y. Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah27272 United Arab Emirates The University of Sydney School of Computer Science Sydney2006 Australia

Rewards are critical hyperparameters in reinforcement learning (RL), since in most cases different reward values will lead to greatly different performance. Due to their commercial value, RL rewards become the target of reverse engineering by the inverse reinforcement learning (IRL) algorithm family. Existing efforts typically utilize two metrics to measure the IRL performance: the expected value difference (EVD) and the mean reward loss (MRL). Unfortunately, in some cases, EVD and MRL can give completely opposite results, due to MRL focusing on whole state-space rewards, while EVD only considering partly sampled rewards. Such situation naturally rises to one fundamental question: whether current metrics and assessment are sufficient and accurate for more general use. Thus, in this article, based on the metric called normalized mutual information of reward clusters (C-NMI), we propose a novel IRL assessment;we aim to fill this research gap by considering a middle-granularity state space between the entire state space and the specific sampling space. We utilize the agglomerative nesting algorithm (AGNES) to control dynamical C-NMI computing via a fourth-order tensor model with injected manipulated trajectories. With such a model, we can uniformly capture different-dimension values of MRL, EVD, and C-NMI, and perform more comprehensive and accurate assessment and analyses. Extensive experiments on several mainstream IRLs are experimented in object world, hence revealing that the assessing accuracy of our method increases 110.13% and 116.59%, respectively, when compared with the EVD and MRL. Meanwhile, C-NMI is more robust than EVD and MRL under different demonstrations. © 2020 IEEE.

关键词： Reverse engineering

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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An efficient swarm intelligence algorithm for Multi-Objective Task Scheduling Optimization in the Context of Cloud computing

An efficient swarm intelligence algorithm for Multi-Objectiv...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Yadav, Dharmveer Abraham Amal Raj, B. St. Xavier's College Department of Computer Science Jaipur India Xavier Institute of Management and Informatics Department of Computer Science Jaipur India

ISBN: (纸本)9798350382723

In the modern era cloud computing is a fastest growing field. It provides many facilities to the user to store, manipulate and retrieve the data anytime and anywhere with help of web or internet. Time to completion of tasks and load balancing of virtual machines are two significant issues that need fixing in cloud computing settings. This study suggests a Multi-objective Optimisation technique to overcome these issues. GA, PSO and ACO Algorithm are utilized in the study to schedule tasks with multiple objectives. Makespan, a critical parameter measuring the overall time necessary to accomplish all jobs, is the primary focus of the study since it assesses the performance of various algorithms in great detail. In our research, PSO produces a makespan of 1.1988, GA produces 1.3025, while ACO remarkably achieves a minimum makespan of 0.8736 after 200 iterations. The comparison's findings demonstrate that ACO is better to PSO and GA, making it the ideal choice for cloud task scheduling algorithm optimization. The significant difference in makespan values demonstrates ACO's superior capacity to explore the solution space, converge effectively, and offer plans that minimize the overall amount of time required to accomplish the task. The findings of this study are crucial because they demonstrate the advantages of ACO for scheduling several objectives efficiently at once. © 2024 IEEE.

关键词： Cloud computing

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A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

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SN computer science 2024年第5期5卷 610页

作者： Aimtongkham, Phet Musikawan, Pakarat Kongsorot, Yanika So-In, Chakchai Applied Network Technology (ANT) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand Advanced Intelligent Interdisciplinary Integration (AIII) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand

In routing protocols for low-power and lossy (RPL)-based Internet of Things (IoT) networks, congestion control is essential for ensuring efficient and dependable communications with energy awareness. As the number of devices and the amount of data traffic increase, congestion typically occurs, resulting in degraded network performance, i.e., increased packet losses and delays with decreased energy efficiency. Many parameters relating to congestion behaviors have been considered and dynamically changed, as have the possible transmission paths either within a single destination-oriented directed acyclic graph (DoDAG) or across different DoDAGs. Thus, this research investigates congestion mitigation methods for RPL-IoT networks based on a fuzzy logic system (FLS) by proposing a novel FLS scheme to determine the selection criteria for conducting path management in three FLS components: (1) inside/outside the DoDAG selection process, (2) inside the path selection process within a single DoDAG (the so-called inside DoDAG) for high or to-be-high loads, and (3) inside the path selection process across the DoDAGs (the so-called outside DoDAG) during severe congestion applying the concept of a multi-DoDAG node (MDN) to determine both a suitable MDN and data traffic for alternate path selection. Simulation results demonstrate that the proposed method effectively controls congestion levels, decreases packet losses and latency, and attains improved energy efficiency in comparison with the state-of-the-art congestion control schemes in RPL-IoT networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Congestion controls Fuzzy logic systems Internet of Things Objective function Path selection Routing protocol for low-power and lossy networks

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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science China(Information sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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Analytical Overview on Transfer Learning in Processing Dental X-rays 22

Analytical Overview on Transfer Learning in Processing Denta...

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5th International Conference on Control and computer Vision, ICCCV 2022

作者： Al Mokdad, Seba Al Houria, Anas Abu Talib, Manar Moufti, Mohammad Bouridane, Ahmed Nasir, Qassim College of Computing and Informatics Department of Computer Science University of Sharjah United Arab Emirates

ISBN: (纸本)9781450397315

Dental x-rays have been a standard piece of dental equipment for many years and are an indispensable diagnostic tool for dentists to detect tooth damage or disease. Recent research has focused on employing computer vision algorithms to automate analysis of dental x-rays. Our study aims to review the work done using transfer learning in dental image processing. AI solutions for dental images have been developed for many purposes, including examining tooth cavities (caries) and restorations and abnormalities in the maxillary sinuses. They have also been used to classify dental implants and determine gender in forensic studies. Transfer Learning is a new approach that is being used to solve a problem that classic deep learning and machine learning techniques could not solve: that of data limitation. Our search has investigated 80 research papers, of which 30 were relevant and analyzed in this paper. The identified studies have discussed a variety of transfer learning models to process different types of x-rays and have reported their efficacy using a variety of metrics. Transfer learning was used to solve various problems depending on the research question. Some papers compared the performance of transfer learning with that of dental experts in analyzing x-ray images, the accuracy of which were surprisingly close to equal. Although the results of the majority of dental applications performed using transfer learning models are encouraging, future research will need to solve the shortcomings highlighted in the present review. © 2022 ACM.

关键词： Diagnosis

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