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25th International Multi Topic Conference, INMIC 2023

作者： Abbas, Ahsan Alzahrani, Abdulkareem Imran, Azhar Almuhaimeed, Abdullah Khan, Ali Haider Air University Department of Creative Technologies Islamabad Pakistan Al-Baha University Faculty of Computer Science & It Saudi Arabia Digital Health Institute King Abdulaziz for Science and Technology Riyadh Saudi Arabia Lahore Garrison University Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350317701

Among the most prominent causes for visual impairment, especially among older adults, is cataract. As per the World Health Organisation (WHO), there are 2.2 billion individuals globally are estimated to be blind or possess an eyesight problem. One of the most identified and important causes of this is cataracts. Cataracts should be identified and treated as soon as possible to avoid blindness. Ophthalmologists use an expensive slit lamp to diagnose cataracts in regions with few medical facilities. Consequently, the issue is that a lack of skilled ophthalmologists may delay the identification of cataracts, for which medical treatment is unavoidable. Medical image analysis based on artificial intelligence provides a rapid and precise diagnosis in modern healthcare. We utilized deep learning models based on transfer learning, namely VGG19, and ResNet-50, to diagnose cataracts using fundus images and enhance classification accuracy. The performance metric for the model is accuracy;the highest achieved accuracy was 98%. © 2023 IEEE.

关键词： Diagnosis

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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A Survey of Edge Computing Privacy and Security Threats and Their Countermeasures

A Survey of Edge Computing Privacy and Security Threats and ...

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IEEE computer Society Annual Symposium on VLSI

作者： Ahmed Shafee Tasneem A. Awaad Ahmed Moro Department of Engineering and Computer Science Adams State University Alamosa CO USA Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Lifecycle Collaboration Software Segment Cairo Egypt

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Recent advancements in Artificial Intelligence (AI) and the proliferation of the Internet of Things (loT) and mobile devices have generated enormous data volumes, necessitating advanced data processing closer to data sources through edge computing to enhance efficiency and reduce latency. Nonetheless, privacy concerns in cloud storage and data transmission, alongside the inadequacy of traditional privacy protections, have highlighted the vulnerability to sophisticated data breaches. This paper offers a survey on edge computing, emphasizing privacy and security challenges, evaluating threats, and exploring solutions to promote the development of secure, robust and privacy-preserving edge computing frameworks.

关键词： Surveys Data privacy Cloud computing Privacy Very large scale integration Data processing Security

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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Ha-Bsn: Hardware Acceleration of Bio-Sfa and Bio-Nica, Biological Neural Networks, on Fpga

SSRN

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SSRN 2023年

作者： Ebrahimi, Amirhosein Vafaeisefat, Hoda Asghari, Mohsen Rahmati, Dara School of Computer Science Institute for Research in Fundamental Sciences Farmanieh Campus Tehran Iran Faculty of Computer Science and Software Engineering Concordia University Montreal Canada Faculty of Computer Science and engineering Shahid Beheshti University Tehran Iran

Nowadays, with the ever-growing advancement in neural networks, many of today's needs have been resolved. However, the processing speed of neural network applications might be challenging and take too long to execute. This is especially true for real-time and embedded systems, which must guarantee a response within limited time constraints. It is relatively straightforward to observe the blurred line between the real-time system and biologically inspired applications. For instance, the bio-SFA algorithm learns slow-varying features by inspiration from the human brain, and the bio-NICA algorithm, a biologically plausible neural network for non-negative independent component analysis, separates mixtures of unknown signals into their distinct sources by understanding how the brain solves blind source separation. These algorithms stem from the human brain's functionality;therefore, the processing speed should be reasonable. In this work, we will leverage the high capability of hardware performance by using field-programmable gate arrays (FPGAs) to accelerate both the bio-SFA and bio-NICA algorithms. We have compared our approach with a software implementation running on two popular embedded boards' processors, ARM Cortex-A53 SoC and ARM Cortex-A72. We have run the standard algorithm on both boards and calculated the number of cycles it takes. Generally, the designed accelerator performs around 38x speedup for Bio-SFA and 4x speedup for Bio-NICA over Cortex-A53. Similarly, compared to Cortex-A72, it achieves 9x speedup for Bio-SFA and 2x for Bio-NICA. Moreover, the accelerator consumes only 0.34 Watts for Bio-SFA and 0.85 Watts for Bio-NICA of power. © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption 14

Design and FPGA Implementation of the Henon Chaotic Map for ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 IEEE.

关键词： Genetic algorithms

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Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system 10

Development of an modified Ant Colony Optimization algorithm...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Sharan, Sudeep Dominguez-Jimenez, Juan Jose Nauth, Peter Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz Ucase Software Engineering Group School of Engineering Avda. de la Universidad de Cádiz 10 Puerto Real Cádiz11519 Spain

ISBN: (纸本)9781665490993

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants. © 2023 IEEE.

关键词： Motion planning

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