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Efficient computation of Hash Hirschberg protein alignment utilizing hyper threading multi-core sharing technology

CAAI Transactions on Intelligence Technology 2022年第2期7卷 278-291页

作者： Muhannad Abu-Hashem Adnan Gutub Department of Geomatics Faculty of Architecture and PlanningKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Engineering College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Due to current technology enhancement,molecular databases have exponentially grown requesting faster efficient methods that can handle these amounts of huge ***-fore,Multi-processing CPUs technology can be used including physical and logical processors(Hyper Threading)to significantly increase the performance of ***,sequence comparison and pairwise alignment were both found contributing significantly in calculating the resemblance between sequences for constructing optimal *** research used the Hash Table-NGram-Hirschberg(HT-NGH)algo-rithm to represent this pairwise alignment utilizing hashing *** authors propose using parallel shared memory architecture via Hyper Threading to improve the performance of molecular dataset protein pairwise *** proposed parallel hyper threading method targeted the transformation of the HT-NGH on the datasets decomposition for sequence level efficient utilization within the processing units,that is,reducing idle processing unit *** authors combined hyper threading within the multicore architecture processing on shared memory utilization remarking perfor-mance of 24.8%average speed up to 34.4%as the highest boosting *** benefit of this work improvement is shown preserving acceptable accuracy,that is,reaching 2.08,2.88,and 3.87 boost-up as well as the efficiency of 1.04,0.96,and 0.97,using 2,3,and 4 cores,respectively,as attractive remarkable results.

关键词： computational biology high-performance computing Hyper Threading pairwise sequence alignment parallel design sequence alignment shared-memory

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A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

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Improving Automated Dental Diagnostics Utilizing YOLO9- Based Framework

Improving Automated Dental Diagnostics Utilizing YOLO9- Base...

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International Conference on computer and Applications (ICCA)

作者： Samar M. Nour Aya Ali Alnozahy Reem Salah Shehab Computer and Systems Engineering Faculty of Engineering Badr University Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The dental diagnostics sector has advanced significantly due to the formation of artificial intelligence (AI) in recent years. YOLO9's key breakthroughs, such as improved feature extraction, deeper convolution layers, and increased anchor box processes, are thoroughly described. These enhancements enable the diagnosis of it is not visible and more delicate dental defects, which were difficult to detect in prior editions. This paper harnesses the capabilities of the YOLO9-based framework to provide a novel way to improve automated dentistry diagnostics. Through the use of intraoral and dental X-ray images, the proposed system in this paper seeks to increase the precision and efficacy of the detection and diagnosis of dental disorders, such as caries, periodontal disease, and other abnormalities of the mouth. The proposed system uses the YOLO9 architecture, which is known for its outstanding detection speed and precision, to scan X-ray dental pictures, offering reliable diagnostic results in real-time. In addition, the paper investigates how YOLO9 might be used with current treatment approaches and post-processing algorithms to improve diagnostic accuracy. The experimental results reveal that the YOLO9-based framework is effective in a variety of diagnostic circumstances, with considerable improvements in sensitivity and specificity when compared to traditional approaches and previous versions of YOLO. Where the proposed system provides on average 22% improvement in accuracy. The system's robustness is demonstrated by rigorous testing on an assortment of datasets, including panoramic radiographs and X-rays.

关键词： YOLO Accuracy Sensitivity and specificity Real-time systems Robustness Dentistry Artificial intelligence X-ray imaging Testing Diseases

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A Framework Using Federated Learning for IoT-Based Forest Fire Prediction

A Framework Using Federated Learning for IoT-Based Forest Fi...

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2023 IEEE International Conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Purcell, Richard Naik, Kshirasagar Lung, Chung-Horng Zaman, Marzia Sampalli, Srinivas Mutakabbir, Abdul Dalhousie University Faculty of Computer Science HalifaxNS Canada Electrical and Computer Engineering University of Waterloo WaterlooON Canada Systems and Computer Engineering Carleton University OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350319040

Forest fires are a growing threat to human commu-nities. The Canadian Wildland Fire Information System gives realtime information to fire management agencies and the public. However, machine learning use for forest fire ignition classification prediction within the platform and ones like it, is yet to be fully realized. We propose a novel framework that uses federated machine learning combined with Internet of Things technologies, for forest fire ignition classification prediction. The framework incorporates distributed IoT weather stations deployed in an area prone to forest fires. We find comparable prediction accuracy between a federated machine learning system and a central server machine learning system. Our federated system, trained on an imbalanced dataset comprising 5,008,365 non-ignition cases and 45,411 ignition instances, has shown encouraging outcomes. It attained an Accuracy of around 0.76 and a ROC-AUC of about 0.80. The performance is on par with other systems, indicating that our approach is effective in classifying forest fire ignitions with a spatial resolution markedly superior to that of centralized systems. © 2023 IEEE.

关键词： Deforestation

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Data Integration Framework with Multi-Source Big Data for Enhanced Forest Fire Prediction

A Data Integration Framework with Multi-Source Big Data for ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kaur, Parveen Naik, Kshirasagar Purcell, Richard Sampalli, Srinivas Lung, Chung-Horng Zaman, Marzia Mutakabbir, Abdul University of Waterloo Electrical and Computer Engineering WaterlooON Canada Dalhousie University Faculty of Computer Science HalifaxNS Canada Carleton University Systems and Computer Engineering OttawaON Canada Research and Development Cistel Technology OttawaON Canada

ISBN: (纸本)9798350324457

Forest fires pose imminent threats to ecosystems and human lives, necessitating precise prediction for effective mitigation. The challenges include managing extensive big data and addressing data imbalance. This study introduces a data integration framework that integrates data from remote sensing satellites, ground-based weather stations, and other sources to create a comprehensive weather database spanning 18 years in Alberta, Canada. Machine learning methods, including Random Forest, eXtreme Gradient Boosting, and Multi-Layer Perceptron are employed to evaluate forest fire prediction performance, overcoming the challenge of data imbalance through changes in spatial resolution, spatio-subsamping, and downsampling techniques. XGBoost exhibits results with an ROC-AUC score of 87.2% and a sensitivity of 75%.Using meteorological data and fire history improves prediction, demonstrating big data and machine learning's role in addressing forest fire challenges. © 2023 IEEE.

关键词： Downsampling Forest Fire Imbalanced data Machine Learning Random Forest XGBoost

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Efficient Strategy for Building Colorectal Cancer Classification CAD System Using Weakly-Annotated Whole Slide Images

Efficient Strategy for Building Colorectal Cancer Classifica...

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Artificial Intelligence, Robotics and Control (AIRC), International Conference on

作者： Ahmed Saeed Nagia M. Ghanem Mohamed A. Ismail Computer and Systems Engineering Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9798350385076

ISBN: (纸本)9798350385083

Colorectal cancer (CRC) is one of the most deadly diseases nowadays. With the advancement of deep learning, it has become feasible to build CAD (computer-aided diagnosis) systems that help doctors in the process of disease diagnosis and prognosis. In this research, we study the CRC classification problem using weakly-annotated whole slide images (WSIs) based on the multiple instance learning (MIL) approach. We concentrate on applying efficient preprocessing steps that output a computational power-efficient representation of the data; and performing a set of experiments to determine the best model structures and hyperparameters suiting the problem being studied, which serves as a preliminary step toward improving the learning algorithm. Our research proves that the correct choice of these factors can potentially improve the learning process and the model performance. The results we achieved demonstrate significant superiority over the baseline work that leveraged the weakly-annotated slides, where we achieved an 89.58% accuracy compared to 84.17%. Our results even outperformed the original work evaluation results obtained by applying a pre-training step using a set of strongly-annotated slides.

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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