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Building an Affordable Portable Storage Area Network (SAN) with Raspberry Pi: Design, Implementation, and Performance Evaluation 4

Building an Affordable Portable Storage Area Network (SAN) w...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Solihin Bin Ahmad Nasser, Muhammad Attarbashi, Zainab S. Aman, Azana Hafizah Mohd Abuzaraida, Mustafa Ali International Islamic University Malaysia Faculty of Information and Communication Technology Selangor Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science & Technology Bangi43600 Malaysia Misurata University Faculty of Information Technology Computer Science Department Libya

ISBN: (纸本)9798350372632

In a digital landscape demanding cost-effective yet robust data storage solutions, Portable Storage Area Network (SAN) became an affordable alternative for data storage systems. A portable SAN system using the Raspberry Pi solution offers personal users the ability to easily expand their storage capacity, improve data organization and management, protect their data, and have a flexible and mobile solution. Users can share any sort of data. This project aims to address the increasing demand for efficient storage solutions by constructing a portable SAN system using the Raspberry Pi's compact size and budget-friendly hardware. The proposed SAN system enhances data organization, expands storage capacity, and offers secure, flexible data access. Targeting individual users and small to medium-sized enterprises, the project covers hardware specifications, intricate design elements, and the seamless integration of OpenMedia Vault, Raspberry Pi OS, and the iSCSI protocol. Performance, stability, and practicality of the SAN solution are visually presented through extensive testing, to prove its potential benefits for diverse storage needs. © 2024 IEEE.

关键词： Budget control

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Brain Tumour Detection Using Stacked Deep Learning Approaches

Brain Tumour Detection Using Stacked Deep Learning Approache...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Sheneamer, Abdullah Jazan University Faculty of Computer Science & Information Technology Computer Science Department Jazan45142 Saudi Arabia

ISBN: (纸本)9798350327816

A brain tumour is a mass or uncontrolled growth of abnormal cells in the brain. tumours have different types and characteristics and have different treatments. Some tumours are noncancerous (benign tumours), some tumours are cancerous (malignant), and brain tumours can be of any type or size. Given the seriousness of this disease, it must be detected early in order to increase the chance of recovery for the patient. Early detection of the tumour plays a major role in the treatment of the patient, as early detection of the tumour would increase the chances of treatment for the patient. In this paper, we used a stacked scratch-built and transfer learning convolutional neural network model to detect brain tumour with high accuracy. The model was designed and implemented on two datasets. These datasets contain a set of images for brain tumour detection. For testing, we used our models, VGG16, VGG19, AlexNet, MobileNetV2 and InceptionResNetV2 individually and further improved the accuracy of the results by integrating all these models into one model and ultimately got better results than the previous models. Experimental results are excellent when compared with contemporary brain tumour detectors. Interestingly, we achieve between 99.5% to 100% success in detecting brain tumour based on recall, precision, and F1-Score. © 2023 IEEE.

关键词： component formatting insert style styling

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Deep Learning-Based Kidney Disease Classification Using ResNet50: Enhancing Diagnostic Accuracy 2

Deep Learning-Based Kidney Disease Classification Using ResN...

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2nd Jordanian International Biomedical Engineering Conference, JIBEC 2024

作者： Owida, Hamza Abu Al-Nabulsi, Jamal Al Hawamdeh, Nour Abuowaida, Suhaila Salah, Zaher Elsoud, Esraa Abu Al-Ahliyya Amman University Faculty of Engineering Department of Medical Engineering Amman Jordan Al-Albayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan The Hashemite University Faculty of Prince Al-Hussein Bin Abdullah II for Information Technology Department of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331539542

Kidney disease is a significant global health issue affecting millions, with various abnormalities, such as stones and cysts, that are often preventable or curable. However, these conditions can progress to more severe forms, including kidney tumors or chronic kidney disease (CKD). Early detection of CKD is crucial to prevent disease progression, thereby reducing mortality rates and treatment costs. This study aims to develop a deep learning model capable of accurately detecting and classifying multiple kidney abnormalities in computed tomography (CT) scans. Using the ResNet-50 architecture, the model was trained to distinguish between healthy kidneys and three specific abnormalities - cysts, tumors, and stones - with a high classification accuracy of 99.6%. The results demonstrate the model's potential as a valuable tool in early kidney disease diagnosis, enhancing clinical decision-making and patient outcomes © 2024 IEEE.

关键词： Deep learning

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Deep Optimal VGG16 Based COVID-19 Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 43-58页

作者： M.Buvana K.Muthumayil S.Senthil kumar Jamel Nebhen Sultan S.Alshamrani Ihsan Ali Department of Computer Science and Engineering PSNA College of Engineering and TechnologyDindigul624622India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologyChennai600073India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia

Coronavirus(COVID-19)outbreak was first identified in Wuhan,China in December *** was tagged as a pandemic soon by the WHO being a serious public medical *** spite of the fact that the virus can be diagnosed by qRT-PCR,COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray(CXR)and Computed Tomography(CT)*** this paper,the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various *** features like Speeded-Up Robust Features(SURF),Features from Accelerated Segment Test(FAST)and Scale-Invariant Feature Transform(SIFT)are used in the test images to detect the presence of *** optimal features are extracted from the images utilizing DeVGGCovNet(Deep optimal VGG16)model through optimal learning *** task is accomplished by exceptional mating conduct of Black Widow *** this strategy,cannibalism is *** this phase,fitness outcomes are rejected and are not satisfied by the proposed *** results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing ***16 model identifies the imagewhich has a place with which it is dependent on the distinctions in *** impact of the distinctions on labels during training stage is studied and predicted for test *** proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen,Spec,Accuracy and F1 score were promising.

关键词： COVID 19 multi-feature extraction vgg16 optimal learning rate

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation 21

Smart Glove with Mobile Application to Detect Static Arabic ...

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21st International Learning and technology Conference, L and T 2024

作者： Elshareif, Mohamed Elgilani Eltahir Ahmad Alias, Nurul Aswani Jomhari, Nazean Sofian, Hazrina University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350393569

The use of assistive technology in the field of education is now a common practice in today's tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise. © 2024 IEEE.

关键词： Assistive technology

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Neighborhood Search Based Improved Bat Algorithm for Web Service Composition

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computer systems science & Engineering 2023年第5期45卷 1343-1356页

作者： Fadl Dahan Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen

Web services are provided as reusable software components in the services-oriented *** complicated composite services can be combined from these components to satisfy the user requirements represented as a workflow with specified Quality of Service(QoS)*** workflow consists of tasks where many services can be considered for each *** for optimal services combination and optimizing the overall QoS limitations is a Non-deterministic Polynomial(NP)-hard *** work focuses on the Web Service Composition(WSC)problem and proposes a new service composition algorithm based on the micro-bats behavior while hunting the *** proposed algorithm determines the optimal combination of the web services to satisfy the complex user *** also addresses the Bat Algorithm(BA)shortcomings,such as the tradeoff among exploration and exploitation searching mechanisms,local optima,and convergence *** proposed enhancement includes a developed cooperative and adaptive population initialization *** elitist mechanism is utilized to address the BA convergence *** tradeoff between exploration and exploitation is handled through a neighborhood search *** benchmark datasets are selected to evaluate the proposed bat algorithm’s *** simulation results are estimated using the average fitness value,the standard deviation of the fitness value,and an average of the execution time and compared with four bat-inspired *** is observed from the simulation results that introduced enhancement obtains significant results.

关键词： Cloud computing web service composition bat algorithm serviceoriented architecture

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Covert Channel Detection and Generation Techniques: A Survey 3

Covert Channel Detection and Generation Techniques: A Survey

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Al-Khulaidi, Nesmah A. Zahary, Ammar T. Hazaa, Muneer A.S Nasser, Adel A. Yemen Academy for Graduate Studies Department of Computer Networks Sana'a Yemen University of Science and Technology Faculty of Computing and It Department of Computer Science Sana'a Yemen Thamar University Faculty of Computer and Information System Department of Computer Science Thamar Yemen Sa'adah University Faculty of Sciences Department of Information Systems and Computer Science Sa'adah Yemen

ISBN: (纸本)9798350305333

The use of covert communications has become more widespread recently as a solution to the information security issue. information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment. © 2023 IEEE.

关键词： Intrusion detection

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

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Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File system (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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