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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Mohammad, Fares Al-Haran, Bader Doush, Iyad Abu Kandil, Marwa College of Engineering and Applied Science American University of Kuwait Kuwait Kuwait Computer Science Department Yarmouk University Irbid Jordan

ISBN: (纸本)9798331533137

Chances of survival can be significantly improved through early detection of breast cancer. Traditional breast cancer detection techniques require experts to identify the physical features of breast tumors from medical tests. However, the limited number of medical experts will leave many women at risk of developing higher stages of cancer. Large amount of data can be efficiently analysed using Machine Learning (ML) algorithms to recognise abnormal patterns;thus providing an early detection tool for breast cancer. This paper examines the outcome of six distinct ML algorithms in classifying breast tumors to either benign tumor (B), or malignant tumor (M). The models of ML utilized Wisconsin Breast cancer dataset that contains 569 rows of data and 32 features for training. The models were evaluated by the ROC curve, the confusion matrix, precision, recall, accuracy, and F1 score values. The experimental results proved that SVM and KNN models achieved the highest accuracy of 98.24%, and F1 score of 98% for SVM and 99% for KNN. This indicates that SVM and KNN models handled the data’s complexity effectively and performed accurate prediction of breast tumor type. ©2024 IEEE.

关键词： Lung cancer

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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A Nonlinear Cryptosystem Based on Structure Phase Masks, QZ Modulation, Using Gyrator Transform Domain

A Nonlinear Cryptosystem Based on Structure Phase Masks, QZ ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Sharma, Bhavana Singh, Hukum Khurana, Mehak The NorthCap University Department of Computer Science and Engineering Gurugram122017 India The NorthCap Univesity Department of Applied Sciences Gurugram122017 India

ISBN: (纸本)9798350375237

This communication delves into an encryption methodology centered around the gyrator transform and the utilization of diverse structured phase masks (SPMs). Unlike conventional random phase masks (RPMs), this approach substitutes them with structured phase masks (SPMs) derived from Devil's Vortex Fresnel Lens (DVFL) and Fresnel Toroidal Lens (FTL). The primary objective behind incorporating these phase keys is to bolster algorithmic security by expanding the key-space. Structured phase masks (SPMs) boast the capability of housing multiple storing keys within a single-phase mask, owing to their nature as diffractive optical elements (DOEs), making them arduous to replace. Consequently, they introduce additional security parameters that enhance the system's robustness and reliability. The efficacy of the proposed schemes has been validated through computer simulations, with assessments conducted via the computation of mean-squarederror (MSE) and peak signal-to-noise ratio (PSNR) between the original and decrypted images. Furthermore, these schemes have undergone scrutiny regarding their sensitivity to various encryption parameters and their resilience against noise attacks. © 2024 IEEE.

关键词： Vortex flow

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Vulnerabilities and Challenges in the Development of PUF-Based Authentication Protocols on FPGAs: A Brief Review 7

Vulnerabilities and Challenges in the Development of PUF-Bas...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Yavuz, Sinan Naroska, Edwin Daniel, Kai University of Siegen Department of Electrical Engineering and Computer Science Siegen Germany Niederrhein University of Applied Sciences Department of Electrical Engineering and Computer Science Krefeld Germany Institute of Measurement and Sensor Technology University of Applied Sciences Ruhr West Mülheim Germany

ISBN: (纸本)9798331540289

The security of IoT (Internet of Things) devices and the protection of sensitive information processed by these devices such as personal data, sensor values, process-related information is an important and difficult challenge. A major task in IoT communication is secure identification of devices. Unfortunately, traditional cryptographic methods are often not suitable for IoT devices due to their limited hardware resources. However, typical methods are computationally intensive, require a large amount of memory, and have a high-power consumption. On the other hand, Physical Unclonable Functions (PUFs) are low-cost and lightweight hardware-based primitives that can be used as a security component to protect data against third parties and thus increase security of a device. In this paper, the challenges and vulnerabilities in the development of PUF-based authentication protocols are presented. To this end, a security analysis of different approaches known from literature are discussed. Furthermore, possible attack vectors and prevention techniques are also considered. © 2024 IEEE.

关键词： Authentication Protocol

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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How Large Language Models are Transforming Teachers' Assessment of Student Competency: A Case Study on LLM-Based Report Writing

How Large Language Models are Transforming Teachers' Assessm...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Dongyub Kim, Daejung Loeser, Martin Seo, Kyoungwon Seoul National University of Science and Technology Department of Applied Artificial Intelligence Seoul Korea Republic of Seoul National University of Science and Technology Department of Environmental Engineering Seoul Korea Republic of Electrical Engineering and Mechatronics ZHAW Zurich University of Applied Sciences Department of Computer Science Winterthur Switzerland

ISBN: (纸本)9798331510756

Evaluating student competencies is critical to fostering academic success, but the traditional process of assessment and report generation is time-consuming and labor-intensive for teachers. The emergence of Large Language Models (LLMs) presents an opportunity to automate this process, potentially reducing the workload for educators. However, concerns such as hallucinations in LLM outputs and the lack of usability studies involving teachers raise questions about their reliability and practical application. In response, we developed an LLM-based report writing system specifically designed for real-time competency evaluation. To assess its effectiveness, we conducted a case study involving five educational experts, each with over 15 years of experience. These experts used the system to generate student competency reports and found them to be both sensible and specific. While they expressed concerns about issues like insufficient detail, they recognized the system's significant time-saving benefits. Our findings demonstrate that LLMs can positively impact teachers' competency assessments by streamlining the reporting process, offering valuable support for educators. © 2025 IEEE.

关键词： Students

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences

A Utility-Mining-Driven Active Learning Approach for Analyzi...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Wang, Danny Y. C. Jordanger, Lars Arne Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

In the rapidly evolving e-commerce industry, the ability to select high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining based active learning strategy, to tackle this challenge. We found that the parameter settings for positive and negative SHAP values affect the mining results of the model, introducing a key consideration into the active learning framework. Unlike traditional SHAP, which evaluates individual elements, HUSPM-SHAP utilizes SHAP values in combination with HUSPM to identify the valuable of high-utility sequential patterns for improving prediction models. In experiments to predict behaviors that actually lead to purchases, the developed HUSPM-SHAP model shows its superiority in different scenarios. The model's ability to reduce labeling requirements while maintaining high predictive performance is highlighted. Our results show that the model is able to refine the processing of e-commerce data and lead to optimized, cost-efficient prediction modeling. © 2024 IEEE.

关键词： Active learning

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Automatic classification of normal/AD brain MRI slices using whale-algorithm optimized hybrid image features

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14237-14248页

作者： Kadry, Seifedine Jessy, V. Elizabeth Rajinikanth, Venkatesan Crespo, Rubén González Department of Applied Data Science Noroff University College Kristiansand4612 Norway College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Innovation Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India Department of Computer Science and Technology Universidad Internacional de La Rioja La Rioja Logroño Spain

In recent years, the prevalence of Age-Related Illnesses (ARL) has been increasing among older individuals, and early recognition and treatment will result in better living conditions. It is well known that Alzheimer's Disease (AD) is among the ARD, and severe cases may result in dementia as well. It is the purpose of this study to propose a technique for distinguishing normal/AD brain MRI slices with improved accuracy utilizing the T2-modality. This scheme consists following phases: (i) Brain MRI collection and preprocessing, (ii) Deep feature extraction with the chosen scheme, (iii) Handcrafted feature extraction, (iv) Whale Algorithm (WA) based feature reduction and serial integration, and (v) binary classification using five-fold cross-validation. A total of 2000 MRI slices (1000 normal and 1000 AD class) are examined during this task using images collected from Alzheimer’s Disease Neuroimaging Initiative (ADNI). This study confirms that the proposed scheme provides a classification accuracy of > 98% when applied with the K-Nearest Classifier. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks

SSRepL-ADHD: Adaptive Complex Representation Learning Framew...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rehman, Abdul Heldal, Ilona Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350362480

Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performance on also downstream different types of Neurodevelopmental disorder (NDD) detection. In this paper, a novel SSRepL and Transfer Learning (TL)-based framework that incorporates a Long Short-Term Memory (LSTM) and a Gated Recurrent Units (GRU) model is proposed to detect children with potential symptoms of ADHD. This model uses Electroencephalogram (EEG) signals extracted during visual attention tasks to accurately detect ADHD by preprocessing EEG signal quality through normalization, filtering, and data balancing. For the experimental analysis, we use three different models: 1) SSRepL and TL-based LSTM-GRU model named as SSRepL-ADHD, which integrates LSTM and GRU layers to capture temporal dependencies in the data, 2) lightweight SSRepL-based DNN model (LSSRepL-DNN), and 3) Random Forest (RF). In the study, these models are thoroughly evaluated using well-known performance metrics (i.e., accuracy, precision, recall, and F1-score). The results show that the proposed SSRepL-ADHD model achieves the maximum accuracy of 81.11% while admitting the difficulties associated with dataset imbalance and feature selection. © 2024 IEEE.

关键词： Self-supervised learning

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