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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

computer systems Science & engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems Science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Multi-Scale Network for Thoracic Organs Segmentation

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computers, Materials & Continua 2022年第2期70卷 3251-3265页

作者： Muhammad Ibrahim Khalil Samabia Tehsin Mamoona Humayun N.Z Jhanjhi Mohammed A.AlZain Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information systems College of Computer and Information SciencesJouf UniversityKSA School of Computer Science and Engineering(SCE) Taylor’s UniversityMalaysia Center for Smart Society 5.0(CSS5) Faculty of Innovation and Technology(FIT)Taylor’s UniversityMalaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Medical Imaging Segmentation is an essential technique for modern medical *** is the foundation of many aspects of clinical diagnosis,oncology,and computer-integrated surgical *** significant successes have been achieved in the segmentation of medical images,DL(deep learning)*** delineation of OARs(organs at risk)is vastly dominant but it is prone to errors given the complex irregularities in shape,low texture diversity between tissues and adjacent blood area,patientwide location of organisms,and weak soft tissue contrast across adjacent organs in CT *** now several models have been implemented onmulti organs segmentation but not caters to the problemof imbalanced classes some organs have relatively small pixels as compared to *** segment OARs in thoracic CT images,we proposed the model based on the encoder-decoder approach using transfer learning with the efficientnetB7 DL *** have built a fully connected CNN(Convolutional Neural network)having 5 layers of encoding and 5 layers of decoding with efficientnetB7 specifically to tackle imbalance class pixels in an accurate way for the segmentation of *** methodology achieves 0.93405 IOU score,0.95138 F1 score and class-wise dice score for esophagus 0.92466,trachea 0.94257,heart 0.95038,aorta 0.9351 and background *** results showed that our proposed framework can be segmented organs accurately.

关键词： Deep learning convolutional neural network computed tomography organs at risk computer-aided diagnostic

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Early Detection of Childhood Malnutrition Using Survey Data and Machine Learning Approaches 4

Early Detection of Childhood Malnutrition Using Survey Data ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Zumma, Thoufiq Rahaman, Anikur Islam Prova, Nuzhat Noor Haque, Tasriful Joy Bose, Julias Caeser Youki, Ravina Akter Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Washington University Science and Technology Department of Computer Science New York United States Pace University Department of Seidenberg School of Csis New York United States Victorian Institute of Technology Master of Information Technology and Systems Australia University of Wollongong Faculty of Artificial Intelligence and Science Australia University of Technology Sydney Faculty of Engineering and It Australia

ISBN: (纸本)9798331523923

Children's physical, mental and emotional development depends heavily on sleep, with age-specific sleep needs fluctuating. Malnutrition may result from eating too little, absorbing nutrients poorly, being unwell, living in poverty or not knowing enough about nutrition. Food allergies and iron insufficiency are two examples of illnesses whose onsets might differ depending on environmental exposures and genetics. By providing proper diet, getting medical attention when necessary and encouraging healthy behaviors, parents play a crucial role in supporting their children's health. For children to grow and develop properly, they need meals high in fruits, vegetables, milk, protein, calcium, iron and fiber. Key advantages of a balanced diet include aiding bone formation, improving heart health and encouraging regular bowel motions and digestion. In general, children's general health and wellness depend on caregivers being aware of their dietary requirements and giving them the proper attention. The findings of the research imply that machine learning algorithms may be useful in understanding and predicting the prevalence of malnutrition in children. Furthermore, these algorithms could provide perceptive data that might help politicians create effective policies. In this research, we make use of five different machine learning algorithms. With a decision tree, the accuracy is 0.92%, followed by svm which provides 0.91%, KNN, which provides 0.84%, GBM classifier, which provides 0.79% and random forest, which provides 0.65%. © 2025 IEEE.

关键词： Adversarial machine learning

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A new method for human activity identification using convolutional neural networks

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International Journal of Cloud Computing 2023年第2-4期12卷 191-200页

作者： Prakash, P.S. Balakrishnan, S. Venkatachalam, K. Balasubramanian, Saravana Balaji Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tiruchengode637205 India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Applied Cybernetics Faculty of Science University of Hradec Králové Hradec Králové50003 Czech Republic Department of Information Technology Lebanese French University Erbil Iraq

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

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2024 IEEE International Conference on Emerging Technologies in computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Rani, Rudra Yamini Prasad, Mudarakola Lakshmi Reddy, Induru Sunnihith Tayubi, Iftikhar Aslam Sultan, Ghazala Reddy, Pundru Chandra Shaker VNR Vignana Jyothi Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Telangana Dundigal India Computer Science and Business Systems RMK Engineering College Tamilnadu Tiruvallur India King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Aligarh Muslim University Department of Computer Science Uttar Pradesh Aligarh India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350372502

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease;these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision. © 2024 IEEE.

关键词： Hospitals

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ChatGPT: Applications, Opportunities, and Threats

ChatGPT: Applications, Opportunities, and Threats

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2023 systems and information engineering Design Symposium, SIEDS 2023

作者： Bahrini, Aram Khamoshifar, Mohammadsadra Abbasimehr, Hossein Riggs, Robert J. Esmaeili, Maryam Majdabadkohne, Rastin Mastali Pasehvar, Morteza University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States Iran University of Science and Technology School of Computer Engineering Tehran Iran Azarbaijan Shahid Madani University Faculty of Information Technology and Computer Engineering Tabriz Iran Alzahra University Faculty of Engineering Department of Industrial Engineering Tehran Iran The American University Faculty of Medical Sciences Department of Medicine Managua Nicaragua Islamic Azad University Faculty of Engineering Department of Industrial Engineering Tehran Iran

ISBN: (纸本)9798350300642

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations. © 2023 IEEE.

关键词： Deep learning

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Observation points classifier ensemble for high-dimensional imbalanced classification

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CAAI Transactions on Intelligence technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer Science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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