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  1. Article ; Online: Analysis of Intrusion Detection Approaches for Network Traffic Anomalies with Comparative Analysis on Botnets (2008–2020)

    Sultan Ahmad / Sudan Jha / Afroj Alam / Meshal Alharbi / Jabeen Nazeer

    Security and Communication Networks, Vol

    2022  Volume 2022

    Abstract: Botnets are conglomerations of traded PCs (bots) that are remotely controlled by its originator (botmaster) under a command-and-control (C&C) foundation. Botnets are the making dangers against cutting edge security. They are the key vehicles for several ... ...

    Abstract Botnets are conglomerations of traded PCs (bots) that are remotely controlled by its originator (botmaster) under a command-and-control (C&C) foundation. Botnets are the making dangers against cutting edge security. They are the key vehicles for several Internet assaults, for example, spam, distributed denial-of-service (DDoS) attack, rebate distortion, malware spreading, and phishing. This review paper depicts the botnet examined in three domains: preview of botnets, observation, and analysis of botnets, apart from keeping track of them and protecting against them too. We have also attempted to the various ways to indicate differing countermeasures to the botnet dangers and propose future heading for botnet affirmation look into a consolidated report on the energy investigation and future headings for botnet break down are also been presented in this paper.
    Keywords Technology (General) ; T1-995 ; Science (General) ; Q1-390
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Hindawi-Wiley
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model

    Sudan Jha / Sultan Ahmad / Anoopa Arya / Bader Alouffi / Abdullah Alharbi / Meshal Alharbi / Surender Singh

    Journal of Healthcare Engineering, Vol

    2023  Volume 2023

    Abstract: The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The ... ...

    Abstract The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India. Women aged 45 or above are more vulnerable to this disease. Images are more effective at depicting information as compared to text. With the advancement in technology, several computerized techniques have come up to extract hidden information from the images. The processed images have found their application in several sectors and medical science is one of them. Disease-like breast cancer affects most women universally and it happens due to the existence of breast masses in the breast region for the development of breast cancer in women. Timely breast cancer detection can also increase the rate of effective treatment and the survival of women suffering from breast cancer. This work elaborates the method of performing hybrid segmentation techniques using CLAHE, morphological operations on mammogram images, and classified images using deep learning. Images from the MIAS database have been used to obtain readings for parameters: threshold, accuracy, sensitivity, specificity rate, biopsy rate, or a combination of all the parameters and many others under study.
    Keywords Medicine (General) ; R5-920 ; Medical technology ; R855-855.5
    Subject code 610
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Hindawi Limited
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: AI-Driven livestock identification and insurance management system

    Munir Ahmad / Sagheer Abbas / Areej Fatima / Taher M. Ghazal / Meshal Alharbi / Muhammad Adnan Khan / Nouh Sabri Elmitwally

    Egyptian Informatics Journal, Vol 24, Iss 3, Pp 100390- (2023)

    2023  

    Abstract: Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been ... ...

    Abstract Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been identified by various means such as ear tags, tattoos, rumen implants, and hot brands. Being non-scientific approaches, these controls can be easily circumvented. The emerging technologies of biometric identification are extensively applied for Human recognition via thumb impression, face features, or eye retina patterns. The application of biometric recognition technology has now moved towards animals. Cattle identification with the help of muzzle patterns has shown tremendous results. For precise identification, nature has awarded a unique Muzzle pattern that can be utilized as a primary biometric feature. Muzzle pattern image scanning for biometric identification has now been extensively applied for identification. Animal recognition via Muzzle pattern image for different applications has been proliferating gradually. One of those applications includes the identification of fake insurance claims under livestock insurance. Fraudulent animal owners tend to lodge fake claims against livestock insurance with proxy animals. In this paper, we proposed the solution to avoid and/or discard fraudulent claims of livestock insurance by intelligently identifying the proxy animals. Data collection of animal muzzle patterns remained challenging. Key aspects of the proposed system include: (1) the Animal face will be detected through visual using YOLO v7 object detector. (2) After face detection, the same procedures will apply to detect muzzle point (3) the muzzle pattern is extracted and then stored in the database. The System has a mean average precision of 100% for the face and 99.43% for the nose/muzzle point of the animal. Once the animal is registered in the database, the identification process is initiated by extracting unique nose pattern features with ORB and/or SIFT. ...
    Keywords Machine Learning ; Transfer Learning ; Deep Learning ; Artificial Intelligence ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2023-09-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

    Vinothini Arumugham / Hayder M. A. Ghanimi / Denis A. Pustokhin / Irina V. Pustokhina / Vidya Sagar Ponnam / Meshal Alharbi / Parkavi Krishnamoorthy / Sudhakar Sengan

    Sustainability, Vol 15, Iss 5453, p

    2023  Volume 5453

    Abstract: Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions ... ...

    Abstract Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.
    Keywords renewable energy ; distributed energy resources ; micro-grid system ; deep learning ; demand response programs ; smart grid ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 690
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization

    Ameer Hamza / Muhammad Attique Khan / Shui-Hua Wang / Majed Alhaisoni / Meshal Alharbi / Hany S. Hussein / Hammam Alshazly / Ye Jin Kim / Jaehyuk Cha

    Frontiers in Public Health, Vol

    2022  Volume 10

    Abstract: The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of ... ...

    Abstract The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.
    Keywords corona virus ; multi-filters contrast enhancement ; deep learning ; Bayesian optimization ; hyperparameters ; fusion ; Public aspects of medicine ; RA1-1270
    Subject code 006
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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