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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of Engineering & technology 2023.

关键词： Support vector machines

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Spearphishing Attack Detection Model Using Machine Learning Approach Based On Network Traffic Analysis

Spearphishing Attack Detection Model Using Machine Learning ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Yohanes Priyo Atmojo Dandy Pramana Hostiadi I Made Darma Susila Gede Angga Pradipta Made Liandana Putu Desiana Wulaning Ayu Department of Information Technology Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Magister Information Systems Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Computer System Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

In the digital era, people use e-mail as the primary means of communication, as well as credentials and private communication in their work. Sending fake e-mails is usually illegal and aims to steal data or implant malware. Fake e-mails must be prevented and detected as early as possible. This paper proposes a new model for detecting fake e-mail-sending activities using a machine learning classifier approach: NaÏve Bayes, k-NN and Decision Tree. The aim is to see the performance of several classification methods and analyze the best detection performance from using classification methods. This paper contributed to introducing a new extracting features technique and modelling it as a detection model using a machine learning approach. The experiment shows that the decision tree model can detect optimally with an accuracy value of 98.11%, precision of 94.04%, and recall of 99.93%. This model can help system administrators handle appropriate procedures.

关键词： Training Analytical models Phishing Machine learning Telecommunication traffic Malware Electronic mail

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Battle Royale Optimization with Fuzzy Deep Learning for Arabic Sentiment Classification

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computer systems Science & Engineering 2023年第8期46卷 2619-2635页

作者： Manar Ahmed Hamza Hala J.Alshahrani Jaber S.Alzahrani Heba Mohsen Mohamed I.Eldesouki Mohammed Rizwanullah Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkah 24211Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent *** refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided *** of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as *** literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and *** sources were used for processing and analyzing the Arabic language content like ***,an important challenge in this domain is the unavailability of sufficient and reliable *** this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)*** aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic *** the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful ***,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of ***,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM *** scenario establishes the novelty of current *** performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.

关键词： Arabic corpus aspect based sentiment analysis arabic language deep learning battle royale optimization natural language processing

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Modified ChannelNet for Estimating SISO-OFDM Channels

Modified ChannelNet for Estimating SISO-OFDM Channels

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Sunita Khichar Sanika Wijayasekara Wiroonsak Santipach Lunchakorn Wuttisittikulkij Sushank Chaudhary Imran A. Khoso Ejaz Ul Haq Pisit Vanichchanunt Isara Josiah Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Computer System Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Electrical Engineering Fculty of Engineering Kasetsart University Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

We propose to modify ChannelNet, which is a convolutional neural network (CNN) used to estimate fast-fading channels in orthogonal frequency division multiplexing (OFDM). By replacing linear interpolation with zero padding and removing the denoising CNN, we significantly reduce the computational complexity of the estimation with minimal performance degradation. The numerical results confirm that the proposed modifications can achieve a comparable mean square error (MSE) performance close to the linear minimum MSE estimation.

关键词： Degradation Interpolation OFDM Simulation Channel estimation Estimation Telecommunications

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Restoring Lesions in Low-Dose Computed Tomography Images of COVID-19 Using Deep Learning 6th

Restoring Lesions in Low-Dose Computed Tomography Images of ...

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6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021

作者： Kulathilake, K. A. Saneera Hemantha Abdullah, Nor Aniza Lachyan, Abhishek Shivanand Bandara, A. M. Randitha Ravimal Patel, Dhrumil Deveshkumar Lai, Khin Wee Faculty of Computer Science and Information Technology Department of Computer System and Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Medicine Department of Social and Preventive Medicine Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Applied Sciences Department of Computer Science University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Radiology Seth GS Medical College King Edward Memorial Hospital Acharya Donde Marg Parel East Maharastra Parel Mumbai400012 India Faculty of Engineering Department of Biomedical Engineering Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9783030907235

The use of Low-dose Computed Tomography (LDCT) in clinical medicine for diagnosis and treatment planning is widespread due to the minimal exposure of patients to radiation. Also, recent studies have confirmed that LDCT is a feasible medical imaging modality for diagnosing COVID-19 cases. In general, X-ray tube current is being reduced to acquire the LDCT images. Reduction of the X-ray flux introduces the Quantum noise into the generated LDCT images and, as a result, it produces visually low-quality CT images. Therefore, it is challenging to differentiate the lesions in the diagnosis of COVID-19 patients using the LDCT images due to low contrast and failure to preserve the subtle structures. Therefore, in this study, we proposed a Deep Learning (DL) model based on the Generative Adversarial Network (GAN) for post-processing the LDCT images to enhance their visual quality. In this proposed model, the generator network is designed as a U-net to generate the restored CT images by filter out the noise. Also, the discriminator network follows a patch-GAN model to discriminate the real and generated images while preserving the texture details. The quantitative and qualitative results demonstrated the effectiveness of noise suppression and structure preservation of the proposed DL method. Hence, it provides an acceptable quality improvement for LDCT images to discriminate the lesions for diagnosing the COVID-19 positive cases. © 2022, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

arXiv

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arXiv 2024年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 2024, CC BY.

关键词： Qubits

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Enabling Quadruple-D Compensation Coil Integration for Efficient Power Transfer to Receiver in Wireless Power Transfer systems for EV Charging

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Erel, Mehmet Zahid Imtiaz, Tabish Elsanabary, Ahmed Mubin, Marizan Soon, Tey Kok Mekhilef, Saad Ankara Yildirim Beyazit University Department of Energy Systems Engineering Ankara Turkey Universiti Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Port Said University Faculty of Engineering Department of Electrical Engineering Port Said Egypt Universiti Malaya Department of Computer System and Technology Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Swinburne University of Technology School of Science Computing and Engineering Technologies Hawthorn Australia Presidency University Department of Electrical and Electronics Engineering Karnataka Bengaluru India

Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil structures in wireless charging systems has addressed various operational challenges, enhanced power transfer, and provided a scalable solution for EVs. However, cross-coupling issues and space limitation problems arise when multiple coils are introduced to the WPT system. In this paper, a novel integrated compact coupler is proposed, including two naturally decoupled coils on the transmitter side, while both are coupled with the receiver coil to improve the power transfer capability of the system. ANSYS Maxwell is employed to simulate the proposed coupler and evaluate its performance under misalignment operations. The proposed wireless charging system inherently detects misalignment direction based on the variation of output voltage with respect to x-misalignment. As a result, the experimental prototype of the designed wireless WPT system presents a peak efficiency of 97.08% at 1.147-kW output power for an air gap of 120 mm under fully-aligned condition. Additionally, the designed compensation coil can transfer power of 243.76-W under fully-aligned condition and increases reliability in power transfer during misalignment operations. © 2013 IEEE.

关键词： Inductive power transmission

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MELex: The Construction of Malay-English Sentiment Lexicon

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computers, Materials & Continua 2022年第4期71卷 1789-1805页

作者： Nurul Husna Mahadzir Mohd Faizal Omar Mohd Nasrun Mohd Nawi Anas ASalameh Kasmaruddin Che Hussin Abid Sohail Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA(UiTM)Kedah08400MerbokKedahMalaysia Department of Decision Sciences School of Quantitative SciencesUniversiti Utara Malaysia06010KedahMalaysia Disaster Management of Institute School of Technology Management and LogisticUniversiti Utara Malaysia06010KedahMalaysia Department of Management Information System College of Business AdministrationPrince Sattam Bin Abdulaziz University165Al-KharjSaudi Arabia Faculty of Entrepreneurship and Business Universiti Malaysia KelantanMalaysia Department of Computer Science COMSATS University IslamabadLahorePakistan

Currently,the sentiment analysis research in the Malaysian context lacks in terms of the availability of the sentiment ***,this issue is addressed in this paper in order to enhance the accuracy of sentiment *** this study,a new lexicon for sentiment analysis is constructed.A detailed review of existing approaches has been conducted,and a new bilingual sentiment lexicon known as MELex(Malay-English Lexicon)has been *** MELex involves three activities:seed words selection,polarity assignment,and synonym *** approach differs from previous works in that MELex can analyze text for the two most widely used languages in Malaysia,Malay,and English,with the accuracy achieved,is 90%.It is evaluated based on the experimentation and case study approaches where the affordable housing projects in Malaysia are selected as case *** finding has given an implication on the ability of MELex to analyze public sentiments in the Malaysian *** novel aspects of this paper are ***,it introduces the new technique in assigning the polarity score,and second,it improves the performance over the classification of mixed language content.

关键词： Machine learning data sciences artificial intelligence opinion mining sentiment analysis sentiment lexicon lexicon-based bilingual lexicon

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Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

Mudahnya BM: A Context-Aware Mobile Cloud Learning Applicati...

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7th International Conference on Advances in Visual Informatics, IVIC 2021

作者： Muhammad, Sufri Admodisastro, Novia Osman, Hafeez Mohd Ali, Norhayati Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Seri Kembangan Selangor43400 Malaysia

ISBN: (纸本)9783030902346

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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SCALABLE LINK PREDICTION IN TWITTER USING SELF-CONFIGURED FRAMEWORK

arXiv

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arXiv 2022年

作者： Daud, Nur Nasuha Hamid, Siti Hafizah Ab Seri, Chempaka Saadoon, Muntadher Anuar, Nor Badrul Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Link prediction analysis becomes vital to acquire a deeper understanding of events underlying social networks interactions and connections especially in current evolving and large-scale social networks. Traditional link prediction approaches underperformed for most large-scale social networks in terms of its scalability and efficiency. Spark is a distributed open-source framework that facilitate scalable link prediction efficiency in large-scale social networks. The framework provides numerous tunable properties for users to manually configure the parameters for the applications. However, manual configurations open to performance issue when the applications start scaling tremendously, which is hard to set up and expose to human errors. This paper introduced a novel Self-Configured Framework (SCF) to provide an autonomous feature in Spark that predicts and sets the best configuration instantly before the application execution using XGBoost classifier. SCF is evaluated on the Twitter social network using three link prediction applications: Graph Clustering (GC), Overlapping Community Detection (OCD), and Redundant Graph Clustering (RGD) to assess the impact of shifting data sizes on different applications in Twitter. The result demonstrates a 40% reduction in prediction time as well as a balanced resource consumption that makes full use of resources, especially for limited number and size of clusters. © 2022, CC BY.

关键词： Efficiency

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