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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Isikoglu, Bora Kubatova, Meerim Cetin, Aydin Computer Engineering Department Gazi University Graduate School of Natural and Applied Sciences Ankara Turkey Computer Engineering Department Gazi University Faculty of Technology Ankara Turkey

ISBN: (纸本)9798350379433

We present a novel approach to optimize artificial intelligence (AI) models using unsupervised learning, specifically utilizing the k-means clustering algorithm with varying cluster parameters. The proposed method aims to enhance the performance of the initial model by iteratively evaluating it after adding new features to the original dataset. The novelty is the use of k-means model outputs as a new feature. By tracking the k-means model outputs we find the best tuning parameters to make the best testing evaluation results and feed the outputs back into the original feature set. This iterative process continues until the accuracy of the meta-model reaches a convergence point. The methodology is designed to uncover latent patterns within the data through the exploration of different cluster configurations, fostering a dynamic feature extraction process. We demonstrate the effectiveness of our approach on the Diabetes Dataset, and Wisconsin Breast Cancer Detection Dataset showcasing improvements in model accuracy as a result of the iterative integration of evaluation data. Our experimental results highlight the adaptability and robustness of the proposed method. Additionally, we discuss the implications of our findings for various applications and provide insights into potential avenues for future research in the realm of unsupervised learning for post-training model optimization. © 2024 IEEE.

关键词： Contrastive Learning

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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Ensemble Model for Detection and Classification of Brain Tumors 4

Ensemble Model for Detection and Classification of Brain Tum...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Ramapuram India Srm Institute of Science and Technology Department of Information Technology Tamilnadu Ramapuram India

ISBN: (纸本)9798331529635

Brain tumors, characterized by abnormal cell growth within the brain, present significant challenges for early detection and accurate classification due to their complex and heterogeneous nature. Manual evaluation of MRI scans, though critical, is often laborintensive and prone to errors, highlighting the need for advanced, automated diagnostic methods. Convolutional Neural Networks (CNNs) have demonstrated strong potential in this domain by effectively learning hierarchical features from raw medical images to detect and classify brain tumors. However, single CNN models often encounter difficulties in handling the variability in tumor appearance, such as differences in shape, size, and intensity. To address these challenges, this research proposes an ensemble model that integrates pretrained CNN architectures-ResNet50, Inception, and Xception. The ensemble technique strengthens the adaptability and accuracy of brain tumor identification and categorization by incorporating the advantages of these distinct models. The proposed model was trained and evaluated using a large dataset of 5,316 MRI images, representing a range of brain tumor types, including gliomas, pituitary, meningiomas, and cases no tumor. With a high classification accuracy of 99.39%, our ensemble model proved to be useful in enhancing the reliability of diagnoses and assisting surgeons in making decisions on the treatment of brain tumors. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving a Deep Neural Network Accelerator using FPGA for Diagnosing Malaria Diseased Blood Cells 14

Improving a Deep Neural Network Accelerator using FPGA for D...

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14th International Conference on Information and Knowledge technology, IKT 2023

作者： Rezaeikarjani, Hadi Valinataj, Mojtaba Babol Noshirvani University of Technology Faculty of Electrical and Computer Engineering Babol Iran

ISBN: (纸本)9798350349412

The increasing computational demands of deep neural networks across various applications have driven the adoption of hardware accelerators. These specialized hardware devices are tailor-made for specific computational tasks, offering enhanced efficiency compared to conventional computer systems. In medical diagnosis applications, particularly the detection of malaria-infected blood cells, hardware accelerators play a pivotal role. This paper explores the augmentation and acceleration of malaria-infected blood cell detection by leveraging FPGA-based hardware accelerators with deep neural networks. The significance of this research is twofold. Firstly, rapid and precise processing of medical images is imperative in diagnosing malaria. FPGA-based hardware accelerators excel in parallel processing and high efficiency, significantly expediting disease detection, a crucial advantage during outbreaks. Secondly, the intricate architectures and numerous parameters of deep neural networks demand efficient implementation. Hardware accelerators, notably FPGA-based ones, facilitate precise and efficient model execution, enhancing diagnosis accuracy, a paramount factor in disease detection. This study adopts an Artificial Neural Network (ANN) with a Multilayer Perceptron (MLP) architecture and implements various hardware units, resulting in substantially faster malaria-infected cell detection. The simulation results demonstrate an acceptable accuracy increase from 94.8% to 98.3% and a significant reduction in latency from 5.93 ns to 0.4 ns in the hardware implementation. Moreover, the output representation has been improved, transitioning from a matrix display to a visually interpretable format with distinct colors, enabling real-time disease detection. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Identification and Classification of Crowd Activities

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computers, Materials & Continua 2022年第7期72卷 815-832页

作者： Manar Elshahawy Ahmed O.Aseeri Shaker El-Sappagh Hassan Soliman Mohammed Elmogy Mervat Abu-Elkheir Department of Information Technology Faculty of Computers and InformationMansoura UniversityEgypt Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityEgypt Faculty of Computer Science and Engineering Galala University435611SuezEgypt Department of Computer Science Faculty of Media Engineering and TechnologyGerman UniversityEgypt

The identification and classification of collective people’s activities are gaining momentum as significant themes in machine learning,with many potential applications *** need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd *** paper investigates the capability of deep neural network(DNN)algorithms to achieve our carefully engineered pipeline for crowd *** includes three principal stages that cover crowd analysis ***,individual’s detection is represented using the You Only Look Once(YOLO)model for human detection and Kalman filter for multiple human tracking;Second,the density map and crowd counting of a certain location are generated using bounding boxes from a human detector;and Finally,in order to classify normal or abnormal crowds,individual activities are identified with pose *** proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities *** results onMOT20 and SDHA datasets demonstrate that the proposed system is robust and *** framework achieves an improved performance of recognition and detection peoplewith a mean average precision of 99.0%,a real-time speed of 0.6ms non-maximumsuppression(NMS)per image for the SDHAdataset,and 95.3%mean average precision for MOT20 with 1.5ms NMS per image.

关键词： Crowd analysis individual detection You Only Look Once(YOLO) multiple object tracking kalman filter pose estimation

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Towards Atomic Scale Quantum Dots in Silicon: An Ultra-Efficient and Robust Subtractor Using Proposed P-Shaped Pattern

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IEEE Transactions on Nanotechnology 2024年 23卷 482-489页

作者： Rasmi, Hadi Mosleh, Mohammad Navimipour, Nima Jafari Kheyrandish, Mohammad Dezful Branch Islamic Azad University Department of Computer Engineering Dezful313 Iran Kadir Has University Department of Computer Engineering Faculty of Engineering and Natural Sciences Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Yunlin Douliou64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan

Today, Complementary Metal-Oxide-Semiconductor (CMOS) technology faces critical challenges, such as power consumption and current leakage at the nanoscale. Therefore, Atomic Silicon Dangling Bond (ASDB) technology has been proposed as one of the best candidates to replace CMOS technology;due to its high-speed switching and low power consumption. Among the most important issues in ASDB nanotechnology, output stability and robustness against possible faults may be focused. This paper first introduces a novel P-shaped pattern in ASDB, for designing stable and robust primitive logic gates, including AND, NAND, OR, NOR and XOR. Then, two combinational circuits, half-subtractor and full-subtractor, are proposed by the proposed ASDB gates. The simulation results show high output stability as well as adequate robustness, against various defects obtained by the proposed designs;on average, they have improvements of more than 56% and 62%, against DB omission defects and extra cell deposition defects;respectively. Also, the results of the investigations show that the proposed circuits have been improved by 65%, 21% and 2%, in terms of occupied area, energy and occurrence, respectively;compared to the previous works. © 2002-2012 IEEE.

关键词： Silicon

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Leveraging Graph-RAG for Enhanced Diagnostic and Treatment Strategies in Dentistry 8

Leveraging Graph-RAG for Enhanced Diagnostic and Treatment S...

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8th International Conference on Information technology, InCIT 2024

作者： Sophaken, Chotanansub Vongpanich, Kantapong Intaphan, Wachirawit Utasri, Tharathon Deepho, Chutamas Takhom, Akkharawoot King Mongkut's University of Technology Thonburi Faculty of Engineering Bangkok Thailand National Electronics and Computer Technology Center Pathum Thani Thailand Naresuan University Faculty of Dentistry Phitsanulok Thailand Thammasat University Faculty of Engineering Thammasat School of Engineering Thailand

ISBN: (纸本)9798350366303

This paper presents a method for extracting and interpreting information from diverse, unstructured dental literature using advanced AI techniques. By integrating information extraction, ontologies, and knowledge graphs, the approach enhances the efficiency and accuracy of dental data analysis. Named Entity Recognition (NER) and a Large Language Model (LLM) are employed to extract relevant entities and relationships, which are then structured into triples and integrated with a dental ontology to ensure contextual relevance. This enriched ontology supports Retrieval-Augmented Generation (RAG) applications, enabling advanced querying and analysis. The methodology improves the identification and categorization of dental conditions, treatments, and anatomical terms, providing a structured representation of dental knowledge. Knowledge graphs facilitate the representation and analysis of relationships between entities, fostering insightful interpretations and supporting hypothesis generation, thereby enhancing the accessibility and usability of dental knowledge. Experimental results demonstrate the effectiveness of this approach in managing complex dental information, showcasing the benefits of combining Knowledge Representation (KR) with Machine Learning (ML). This research contributes to dental studies by offering a robust framework for extracting and utilizing knowledge from diverse and extensive datasets. © 2024 IEEE.

关键词： Knowledge graph

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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Intelligent Farm Irrigation and Safety System 25

Intelligent Farm Irrigation and Safety System

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Smadi, Adnan Al-Nusairat, Murar Erjoub, Ammar Freitekh, Laith Khouj, Mohammed Zarqa Univeristy Faculty of Information Technology Department of Computer Science Zarqa Jordan Yarmouk University Engineering Faculty Depatrment of Electronics Engineering Irbid Jordan University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

In recent years, there has been growing interest in utilizing technology to improve agricultural practices and optimize crop yield. One area of focus is the development of intelligent farm irrigation systems that integrate automation, sensing, and control mechanisms to enhance the efficiency and effectiveness of farming operations. An important aspect of automated agriculture is the management of irrigation, as well as ensuring the safety of the farming environment. Plastic houses have become a cornerstone in modern agriculture, enabling controlled cultivation environments and increased yields. However, managing these environments efficiently requires a harmonious integration of various factors. This paper proposes an intelligent for irrigation, temperature, humidity, flame, gas detection, and fire alarm functions in a plastic house. The system presented here uses Arduino Microcontroller based system that integrates various sensors technology to monitor soil conditions accompanied by audible and visual alerts. Fire safety is ensured through flame and gas sensors, triggering immediate alerts. Python language was used to write the code for the Arduino microcontroller. © 2024 IEEE.

关键词： Smart agriculture

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