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2023 International Conference on Recent Advances in science and Engineering Technology, ICRASET 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Abosaq, Hamad A. Olayah, Fekry A. Al Yami, Mohammed Abdelrahman, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science India Najran University Computer Science Dep. Saudi Arabia Najran University Kingdom of Saudi Arabia Department of Information System Saudi Arabia Najran University Faculty of Computer Science and Information Systems Department of Information Systems Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information System Saudi Arabia

ISBN: (纸本)9798350306927

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model. © 2023 IEEE.

关键词： Real time systems

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Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

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computers, Materials & Continua 2023年第5期75卷 3677-3693页

作者： Mohammed Jasim Mohammed Jasim Bzar Khidir Hussan Subhi R.M.Zeebaree Zainab Salih Ageed Engineering College Al-Kitab UniversityKirkukIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various *** specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer *** is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss *** automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical ***,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative *** manuscript devises a new Northern Goshawk Optimization with Transfer Learning Model for Colonic Polyp Detection and Classification(NGOTL-CPDC)*** NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp *** accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing ***,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO ***,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC *** comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

关键词： Biomedical imaging artificial intelligence colonic polyp classification medical image classification computer-aided diagnosis

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Classification of Sign language Using VGG16 and VGG19 7

Classification of Sign language Using VGG16 and VGG19

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Dabwan, Basel A. Olayah, Fekry A. Halawani, Hanan T Mashraqi, Aisha M Abdelrahman, Yahya A. Shamsan, Ahlam F. AlBaydha University Department of Information System AlBaydha Yemen Najran University Department of Information System Saudi Arabia Najran University Department of Computer Science College of Computer Science and Information Systems Saudi Arabia Najran University Department of Information System Applied College Najran Saudi Arabia

ISBN: (纸本)9798350383065

The Translation system (TS) serves as a communication bridge for individuals with disabilities to interact with others in society. As computers play a vital role in our daily lives, advancements in human-computer interaction (HCI) have particularly benefited the deaf and mute community. Our proposed system aims to develop an intelligent translator that facilitates seamless communication between individuals with speech impairments and the general population. This system seeks to establish an effective and efficient means of communication for those with speaking deficiencies and regular individuals. The proposed system aims to convert sign language into written text using pre-trained models of VGG16 and VGG19, we used The Sign Language dataset consists of 27,455 training samples and 7,172 test samples, This model gave 97.5% with VGG16 and 96% with VGG19. © 2023 IEEE.

关键词： Human computer interaction

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Power Control for Collaborative Sensors in Internet of Things Environments Using K-means Approach

Power Control for Collaborative Sensors in Internet of Thing...

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International Conference on Applied Soft Computing and Communication networks, ACN 2023

作者： Hasan, Mohammed Zaki Alabady, Salah Abdulghani Salleh, Mohd Fadzli Mohd Computer Networks Department College of Computer Science and Mathematics University of Mosul Mosul Iraq Computer Engineering Department College of Engineering University of Mosul Mosul Iraq School of Electrical and Electronic Engineering Universiti Sains Malaysia Seri Ampangan Pulau Pinang Nibong Tebal14300 Malaysia

ISBN: (纸本)9789819720033

Energy efficiency in sensor networks is a critical concern, as sensors are often resource-constrained devices with limited battery power. Maximizing energy efficiency is essential to prolonging the network’s lifetime, reducing maintenance costs, and enabling long-term monitoring and data collection in various Internet of Things (IoT) applications. Collaborative beamforming involves multiple sensors that coordinate their signal transmission to be focused on a specific direction. However, sensors may have various energy budget limitations to achieve beamformed signal transmission in a specific direction of the sink. In this paper, we developed a power control approach of beamforming transmission to prolong the lifetime of sensors by organizing the collaborative sensors into clusters. The proposed algorithm is based on the K-means clustering algorithm, which groups these collaborative sensors into clusters based on their residual energy information (REI). REI is changed among collaborative sensors to communicate with each other within a cluster. Therefore, each of these collaborative sensors adjusts its transmission power level to achieve balance in making decisions and facilitate collaboration between sensors in the inner cluster. Simulation results demonstrate that the proposed algorithm requires significantly low overhead and computational complexity in terms of prolonging the lifetime of the collaborative sensor cluster as compared to alternative power control algorithm that balances the energy consumption of the entire network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Detecting Gesture Language for Deaf and Mute People Usingon Ultra-Low-Power TinyML Model 2

Detecting Gesture Language for Deaf and Mute People Usingon ...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Dabwan, Basel A. Jadhav, Mukti E. Ismil, Omar A. Hassan, Eman A. Farah, Enaam A. Mohammad, Ashraf A. Ali, Yahya A. Albaydha University Department of Information System Albaydha Yemen Shri Shivaji Science & Art College Department of Computer Science Buldhana Chikhali India Department of Information Systems Preparatory in Najran University Saudi Arabia Najran University Department of Computer Science Saudi Arabia Najran University Department of Information System Saudi Arabia

ISBN: (纸本)9798350373110

Sign language serves as a communication method for deaf individuals, utilizing gestures and body movements to convey messages. It includes components such as hand shapes, movements, orientations, and facial expressions, each gesture not only representing a word but also conveying nuanced tones. Many deaf individuals face challenges with verbal communication, impacting their literacy skills. Therefore, developing a sign language translation system, specifically a sign language recognition (The SLR (Sign Language Recognition) system holds great significance in their lives as it plays a vital role in connecting deaf and hearing individuals by bridging the communication gap., making it a pivotal area of study in human-computer interaction research. To address this, we utilized the TinyML Model, enabling local data processing without reliance on cloud services. This technology supports the inference of machine learning models, involving deep learning models, on microcontrollers Under constrained resources and power. Our approach involved training the TinyML Model using a dataset representing the ASL Alphabet, comprising 24 classes of English sign language letters (excluding J and Z, which involve motion). The training dataset included 27,455 instances, with a test dataset of 7,172 instances. We assigned 80% of the dataset for training purposes and set aside the remaining 20% for testing. The results were highly promising, achieving an impressive accuracy rate of 98%. This success underscores the potential of our model in automatically recognizing American Sign Language gestures, thereby improving communication and accessibility for individuals with hearing impairments. © 2024 IEEE.

关键词： Human computer interaction

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Blockchain Interoperability for A Reputation-Based Drug Supply Chain Management 6

Blockchain Interoperability for A Reputation-Based Drug Supp...

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6th International Conference on Information systems and computer networks, ISCON 2023

作者： Tanvir, Deluwar Hussen Amin, Ruhul Islam, Ashraful Islam, Mohammad Shamsul Rashid, Muhammed Mamunur Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh System Analyst Investment Corporation of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350346961

The pharmaceutical drug industry faces considerable challenges in supply chain management, particularly in regard to tracking and tracing medications. Counterfeit drugs pose a significant global threat, and existing supply chain management practices often prove insufficient to prevent adulteration. Blockchain technology offers a potential solution to these issues by establishing a decentralized and distributed ecosystem where stakeholders can foster a trustworthy relationship. To address these limitations, we propose a blockchain-based system for tracking drugs in the supply chain, incorporating a reputation mechanism to ensure transparency and accountability among entities. To mitigate the interoperability challenges between blockchain networks, we implement inter-blockchain communication using the burn-to-claim protocol. Hyperledger Fabric is used as the primary blockchain because it ensures confidentiality between multiple organizations, and Quorum is the other blockchain. Our solution prioritizes trust, traceability, transparency, and accountability to deliver safe, authentic medication to consumers.. © 2023 IEEE.

关键词： Blockchain

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Detection of Pancreatic Cancer Cells at Early Stage and Recommend the Treatment Process 6

Detection of Pancreatic Cancer Cells at Early Stage and Reco...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Margret Sharmila, F. Shree Harini, R. Vityarubbasiri, M. Gopi Krishna, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore641008 India

ISBN: (纸本)9798350394702

Early identification and diagnosis of cancerous cells will focus on the symptomatic patients, to give the best way of successful treatment. When the detection of cancer is delayed, then there is a lower chance of survival, high problems with treatment and higher cost estimates. Considering Pancreas Cancer growth, a type of cancer that is difficult to identify because there are less or no symptoms found at the early stage. So, we introduce Artificial Intelligence (AI) technology to identify pancreatic cancer cells. This is the first insight that is provided to detect cancerous cells at the early stage. With the use of technologies, anything is possible, so we use the process of medical imaging analysis, data processing and Machine Learning (ML) for the recognition. Data is collected through MRI and CT scan reports, biomarkers tests, liquid biopsies, endoscopic techniques, early symptom monitoring, and genetic testing which is then categorized into train and test sets. Every training data set is analyzed to provide a standard image to extract the features with the use of OpenCV. Building an AI model with the use of the Convolutional Neural Network (CNN) algorithm with an efficient set of filters. Training the model with the training data set helps to improve the accuracy of predicting results. When it is ready, the testing data set is used to check the accuracy of predictions. Finally, by assessing the model based on the performance metrics from both training and testing datasets, accuracy is achieved. After achieving a certain level of accuracy, and leveraging ML algorithms such as Hybrid models, the system can provide treatment recommendations consistent with a separate training dataset. This training dataset comprises predicted treatments corresponding to the achieved accuracy. After, the system is prepared for deployment. The envisioned process can track the patient's status for future use. © 2023 IEEE.

关键词： Forecasting

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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EEG Data reduction with Epileptic Seizure Detection based machine learning in IoMT networks 14

EEG Data reduction with Epileptic Seizure Detection based ma...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Alwash, Riyam Faisal Kadhum Idrees, Ali Al-Obaidi, Salah University of Babylon College of Science for Women Department of Computer Science Babylon Iraq Technology University of Babylon College of Information Department of Information Networks Babylon Iraq

ISBN: (纸本)9798350335095

There is a greater demand to enhance intelligent healthcare systems in order to keep track of the medical condition of patients. This demand leads to increase the interest in remote healthcare systems that make use of biosensors. These biosensors generate a significant amount of vital sensed data, which is then sent to the edge of the Internet of Medical Things (IoMT) for processing before being sent on to the data centers of the cloud. In this paper, we propose an EEG data reduction and epileptic seizures detection in IoMT networks. The suggested method operates on 2 levels within the network: edge and fog levels. At the edge gateway, The EEG data is transformed into time-frequency representations using Short Time Fourier Transformation (STFT). Then, a set of statistical texture feature based on the gray level co-occurrence matrix (GLCM) is extracted. These EEG based features are then compressed before being sent to the fog gateway using a binary-encoded representative method. In Fog Gateway, the received encoded features are decoded and passed into a classifier to determine the patient's situation. A set of classifiers are used to evaluate the proposed method using the Bonn University dataset. The proposed technique demonstrates the outperformance of the proposed method by achieving an accuracy of (100%) in multiple cases and an acceptable accuracy for other cases while considering fewer features compared to existing work. © 2023 IEEE.

关键词： Classification (of information)

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