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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Devi, T. Ali, Hassan Mohamed Alsalami, Zaid Kumar, S. Senthil Sangeetha, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798331504960

Concrete structures are made from cement, water, and admixtures, which are used in buildings, water management systems, and foundations. There are many face pressing issues such as corrosion, cracking, and chemical attacks, requiring innovative solutions for sustainable infrastructure. Various methods have been proposed previously to recognize the defects of concrete structures. To overcome these problems, a Deep Learning (DL) model of You Only Look Once version 5 with Alex-Krizhevsky Network (YOLOv5-AlexNet) is proposed for detecting the defects of concrete structures. Primarily, a Surface Crack Detection (SCD) dataset is considered as an input for recognizing the defects of concrete structures. For preprocessing, image resizing and normalization, data augmentation, contrast enhancement, image filtering, and image binarization are considered for efficient processing, enhancing robustness, improving crack visibility, edge detection, and to simplify crack detection. Then for feature extraction, U-shaped Network (U-Net) is employed to reduce noise sensitivity, and to fine-tune the model. A YOLOv5-AlexNet is considered for classification to improve performance and generalizability, and random search for hyperparameter tuning. The proposed YOLOv5-AlexNet model attained higher performance metrics such as accuracy, precision, recall, and f1 score of 99.57%, 98.23%, 98.65%, and 99.86%, when compared to other existing methods like Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Concrete buildings

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Analyzing Code Injection Attacks on Applications of Android Devices and Emulator 9

Analyzing Code Injection Attacks on Applications of Android ...

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

作者： Erfina, Adhitia Hidayat, Rahmat Rizaldi, Rafli Radya Puput Maulana, Riko Sembiring, Falentino Nusa Putra University Faculty of Engineering Computing and Design Departement of Infomation System Sukabumi Indonesia Budi Luhur University Master of Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350370126

The Android operating system released by Google in 2007 has experienced very significant development. In 2019, the number of Android application users reached 2.5 billion. One of the contributing factors is the nature of the android application, which is very flexible and easy to install. Users can download APK files on the internet. Generally, APK files are stored in archive format, so they are pretty easy to manage. However, APK files may be used to spread malware behind this convenience. This study aims to provide an overview of the process of planting malware/backdoor on Android applications and how to prevent it. This study initiated the injection of malicious code on five android applications, namely WhatsApp, Telegram, DroidCam, Fifa Mobile, and Tokopedia. According to the results, four applications were successfully injected, while one application failed. The failure most likely occurred as a result of the application's source code being obfuscated. Obfuscation is therefore strongly advised for Android applications to prevent code injection that could potentially lead to malware insertion. © 2023 IEEE.

关键词： Android (operating system)

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A Novel Genetic Algorithm Based Timetable Generator for Optimized University Timetable Solution

A Novel Genetic Algorithm Based Timetable Generator for Opti...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Ali Hasan Khan Talha Imtiaz Faculty of Computer Science & Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

In educational institutes, efficient timetable generation is critical for optimizing resource utilization and fostering a conducive learning environment. Timetable challenges, such as clashes among faculty schedules and lack of real-time updates, underscore the need for innovative solutions in the field. The problem lies in addressing clashes, transparency issues, and inflexibility in manual scheduling systems, leading to disruptions and impacting both students and faculty. We employed a genetic algorithm, combining one-point crossover and mutation operations, to iteratively evolve and adapt timetables to enhances genetic diversity, addresses scheduling conflicts, and promotes the creation of optimal timetables. The results exhibit dynamic and evolving timetables that prioritize efficiency, clash prevention, and overall optimization. The significance lies in fostering an environment conducive to effective teaching and learning, emphasizing fairness, adaptability, and overall well-being. Our work introduces a genetic algorithm-based methodology that not only addresses existing challenges in educational timetable generation but also significantly contributes to fostering a more efficient, transparent, and adaptable scheduling system.

关键词： Schedules Processor scheduling Diversity reception Manuals Scheduling Resource management Task analysis

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Analysis of Harmonic Distortion in Low-voltage Distribution Network Caused by Photovoltaic Power Plants and Electric Vehicle Chargers

Analysis of Harmonic Distortion in Low-voltage Distribution ...

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International Conference on Smart Systems and Technologies (SST)

作者： Heidi Adrić Zvonimir Klaić Mario Primorac Krešimir Fekete Department of Power Engineering Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Modern technologies have changed the behaviour of power systems, and pose new challenges to power quality in low-voltage distribution (LV) networks. The impact of modern technologies, such as photovoltaic (PV) power plants and electrical vehicle charging stations (EVCS), on power quality in the distribution LV network is presented in this paper. Harmonic analysis is conducted for five different scenarios and results are compared based on values of Total Harmonic Distortion (THD V ) on 0.4 kV busbar as a point of common coupling (PCC). Every scenario has different parameters for elements of modelled LV network based on real measurement data. The obtained results emphasize the importance of considering THD V value and keeping it below the recommended limit.

关键词： Photovoltaic systems Low voltage Total harmonic distortion Simulation Power quality Distribution networks Smart systems Data models Power systems Investment

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

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computers, Materials & Continua 2022年第5期71卷 3969-3983页

作者： CSS Anupama T.J.Benedict Jose Heba FEid Nojood O Aljehane Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Faculty of Science AL-Azhar UniversityCairo11651Egypt Faculty of Computers and Information Technology University of Tabuk47512Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid University62529Saudi Arabia&Faculty of Computer and ITSana’a University31220Yemen Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman University11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharj16278Saudi Arabia

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or *** image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,*** the same time,cervical cancer becomes a major reason for increased women’s mortality *** cervical cancer is an identified at an earlier stage using regular pap smear *** this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)*** BPSIC-CDF technique enables the IoT devices for pap smear image *** addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)***,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear ***,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear *** order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev *** experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

关键词： Biomedical imaging pap smear images internet of things deep learning cervical cancer disease diagnosis

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Intelligent Biomedical Electrocardiogram Signal Processing for Cardiovascular Disease Diagnosis

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computers, Materials & Continua 2022年第4期71卷 255-268页

作者： R.Krishnaswamy B.Sivakumar B.Viswanathan Fahd N.Al-Wesabi Marwa Obayya Anwer Mustafa Hilal Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India School of Computing SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Factually of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Biomedical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Automatic biomedical signal recognition is an important processfor several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patternsof the ECG signals. In order to raise the diagnostic accuracy and reduce thediagnostic time, automated computer aided diagnosis model is necessary. Withthe advancements of artificial intelligence (AI) techniques, large quantity ofbiomedical datasets can be easily examined for decision making. In this aspect,this paper presents an intelligent biomedical ECG signal processing (IBECGSP) technique for CVD diagnosis. The proposed IBECG-SP technique examines the ECG signals for decision making. In addition, gated recurrent unit(GRU) model is used for the feature extraction of the ECG signals. Moreover,earthworm optimization (EWO) algorithm is utilized to optimally tune thehyperparameters of the GRU model. Lastly, softmax classifier is employedto allot appropriate class labels to the applied ECG signals. For examiningthe enhanced outcomes of the proposed IBECG-SP technique, an extensivesimulation analysis take place on the PTB-XL database. The experimentalresults portrayed the supremacy of the IBECG-SP technique over the recentstate of art techniques.

关键词： Biomedical signals ecg disease diagnosis artificial intelligence parameter tuning gru model

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Enhanced Hemorrhage Detection Based on Deep Reinforcement Hemorrhage Candidate Filtering (DR-HCF) for Diabetic Retinopathy Treatment

Enhanced Hemorrhage Detection Based on Deep Reinforcement He...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Alubady, Raaid Abdul Kadeem, Sahar R. Taha, Ahmed Ali, Sarah Al-Chilibi, Abdulkareem Haider Sadiq, Sadiq Nabeel Technical Engineering College Al-Ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al- Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Diabetic retinopathy (DR) is a retinal condition caused by diabetes that leads to blindness due to an eye infection. The DR is caused by diabetes mellitus, which affects over 80% of the world's population. Hemorrhage happens when DR is misdiagnosed at an early stage when retinal bleeding has already begun. Early detection of retinal bleeding and infection is crucial for avoiding a potentially blinding scenario. As a result, the clinical analysis of DR uses sophisticated computing approaches to boost the total detection rate. Current study methods need additional time investment to make a correct bleeding diagnosis in retinal analysis. So, in this work, introduce the deep reinforcement hemorrhage candidate filtering (DR-HCF) approach is applied to improve the hemorrhage detection accuracy. Hemorrhage candidate characteristics are derived from retinal pictures using a color and shape analysis and extraction technique. Diabetic retinopathy therapy is administered based on the derived characteristics, reducing the risk of permanent visual loss. Several datasets are used to measure the DR-HCF system's computation time of 24.5%, sensitivity ratio of 98.3%, specificity ratio of 97.6%, and accuracy efficacy ratio of 99.45%. © 2023 IEEE.

关键词： Reinforcement

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A Hybrid Model Integrating Multi-Omic and Topological Information of PPI Network for Drug Synergism Prediction

A Hybrid Model Integrating Multi-Omic and Topological Inform...

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Nguyen, Giang T.T. Phuong, Kien T. Nguyen-Trong, Khanh Le, Duc-Hau Hanoi University of Science and Technology Network Information Center Hanoi Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam Institute of Technology Faculty of Information Technology Telecommunications Hanoi Viet Nam Thuyloi University Faculty of Computer Science and Engineering Hanoi Viet Nam

ISBN: (纸本)9798350315844

Combination therapies in precision medicine promise to enhance treatment efficacy and combat drug resistance. Current drug combination prediction methods struggle to extract valuable insights from multi-omics cell line data and integrate them with protein-protein interaction (PPI) network information. To address these challenges, we introduce AE-XGBSynergy, a novel approach. AE-XGBSynergy integrates multi-omics cell line data and drug-cell line features derived from the PPI network's topological characteristics to predict drug synergy combinations. It employs the struc2vec algorithm to extract features for drugs and cell lines based on the PPI network's topological characteristics. Additionally, a pre-trained encoder captures abstract representations of high-dimensional cell line data. These representations of drug pairs and cell lines serve as input for an extreme gradient-boosting algorithm to predict drug combinations for cancer cell lines. We assessed our model's performance using two datasets: the O'Neil and DrugCombDB datasets, which provide extensive drug combination information. Our experimental results show that AE-XGBSynergy excels at integrating multi-omics data to predict drug synergies, with genomic data playing a particularly significant role. Notably, when compared to NEXGB, a hybrid model that includes drug and cell line features using a PPI network, our approach outperforms in terms of accuracy (ACC), AVC-ROC, AUC-PR, Precision, and F1-score. © 2023 IEEE.

关键词： Forecasting

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Non-Invasive Skin Cancer Detection: A High-Resolution Millimeter-Wave Time Reversal Approach 19

Non-Invasive Skin Cancer Detection: A High-Resolution Millim...

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19th European Conference on Antennas and Propagation, EuCAP 2025

作者： Ravanbakhsh, Fatemeh Madannejad, Alireza Ebrahimizadeh, Javad School of Medical Science and Technologies Islamic Azad University Tehran Iran School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden Katholieke Universiteit Leuven Department of Electrical Engineering Leuven Belgium

ISBN: (纸本)9788831299107

This paper introduces a novel non-invasive skin cancer detection system using a high-resolution millimeter-wave time reversal imaging approach. Operating in the 2-30 GHz frequency range, the system utilizes two moving Vivaldi antennas to scan the imaging area, significantly reducing hardware complexity and cost compared to conventional systems. The system features a skin-removal technique that eliminates surface reflections, improving subsurface imaging accuracy. Through time-reversal methods, particularly the DORT algorithm, the system achieves high-resolution imaging capable of detecting both single and multiple tumors. In tests with phantoms, the system successfully identified single tumors with diameters of 5 mm and 8 mm and multiple tumors of 8 mm in close proximity. The worst-case positioning error for single tumor detection was 4 mm, and for multiple targets, 14 mm. The system also exhibited a high signal-to-clutter ratio (SCR) of up to 10.8 dB, demonstrating its effectiveness in distinguishing between benign and malignant tissues. © 2025 European Association on Antennas and Propagation.

关键词： antennas electromagnetics measurements propagation

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