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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Vivek, V. Tr, Mahesh Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350399264

The computer system that is used to take attendance online is going to be upgraded as part of this project. This attendance tracking system is able to hold the technology known as facial recognition, which is a useful feature. This device helps students take attendance by utilizing both a biometric technology and artificial intelligence algorithms in one convenient package. When pupils enter the classroom, the face recognition software can identify them, making it useful for educational institutions of any kind. The existing system has a number of flaws that make it difficult to accurately record the attendance of both students and teachers. The approach makes it simpler to mark attendance and lowers the number of mistakes that might occur when doing *** students and teachers will gain a significant amount by utilizing these techniques. The ability to recognize people based on their faces is becoming increasingly significant in practically every industry. Face recognition techniques will be used to understand the faces. An attendance record is compared to the record that corresponds to it that was previously saved in the database. A student's photograph is used as a check point for this comparison. © 2023 IEEE.

关键词： Face recognition

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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computer Modeling in engineering & Sciences 2024年第3期138卷 2749-2767页

作者： Shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein Salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient SLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme SLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(SLL) quality of service(QOS)

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Constructing a hybrid activation and parameter-fusion based CNN medical image classifier

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International Journal of Information technology (Singapore) 2024年第5期16卷 3265-3272页

作者： Maree, Mohammed Zanoon, Tareq Dababat, Alya’ Awwad, Mays Department of Information Technology Faculty of Information Technology Arab American University 13 Zababdeh Jenin Palestine Department of Computer System Engineering Faculty of Engineering Arab American University Jenin Palestine

In recent years, numerous deep learning models for medical image classification have emerged, with varying accuracies influenced by factors like image quality, content, and the convoluted low-level features. In this article, we aim to identify key features that enhance the precision of convolutional neural networks (CNNs), which are widely employed in this domain. We empirically demonstrate that parameters like activation functions, image augmentation, convolutional layers, filter sizes, and max pooling significantly impact precision, while others like hidden layers, epochs, and batch sizes affect quality and runtime negatively. To validate our proposal, we use a publicly-available dataset of colored Breast Cancer images which comprises 27,751 images distributed over three sub-datasets as follows: 4162 testing images (2980 no cancer and 1182 with cancer), 19,426 training images (13,911 no cancer and 5515 with cancer), and 4163 validation images (2981 no cancer and 1182 with cancer). Multiple experimental runs were conducted, and the outcomes of each run were recorded, taking into account various evaluation metrics and components of the CNN architecture. Results demonstrate that the highest CNN classifier’s accuracy was achieved when coupling RelU and Sigmoid activation functions using a 3 × 3 filter with a batch of size 32 and 20 epochs and 2 hidden layers. A Similar accuracy rate (83.55%) has also been achieved when coupling ReLU and Sigmoid activation functions using the DensNet-201 pre-trained CNN model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Breast cancer CNN Image classification Multi-level architecture Neural networks Parameter fusion

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A NEW STEPSIZE STRATEGY FOR KORPELEVICH’S ALGORITHM SOLVING VARIATIONAL INEQUALITIES

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2024年第3期6卷 309-317页

作者： Trinh, Ngoc Hai Tran, Trung Duc Faculty of Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam College of engineering and computer science VinUniversity Hanoi Viet Nam

In this paper, we propose two self-adaptive extragradient-like algorithms for solving pseudomonotone variational inequalities. We consider two cases: the mapping is Lipschitz continuous (with unknown modulus) and is not Lipschitz continuous. The step size in our algorithms can either increase or decrease at each step. This feature makes our algorithms more efficient. We also give some numerical examples to compare the performance of our algorithm with the existing one. ©2024 Applied Set-Valued Analysis and Optimization.

关键词： Variational techniques

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on Information technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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Network Bandwidth Optimization Using Software Defined Networking (SDN) 5

Network Bandwidth Optimization Using Software Defined Networ...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Haidar, Gaby Abou Zangana, Ali Noaman Khasro Owayjan, Michel Daou, Roy Abi Zeid Achkar, Roger American University of Science and Technology Faculty of Engineering and Computer Science Beriut Lebanon France La Sagesse University Faculty of Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

This paper provides a detailed comparison of traditional networking architectures and Software Defined Networking (SDN) approaches, with a focus on bandwidth optimization and traffic management. Simulations were conducted under various networking scenarios, utilizing Cisco Packet Tracer for traditional networks and Mininet with the OpenDaylight (ODL) controller for SDN. The evaluated scenarios included single, dual, and triple PC setups, as well as high-Traffic conditions. Traditional networks demonstrated packet loss rates of up to 5%, limited bandwidth utilization (~2.443%), and increasing latency as more devices were added. In contrast, SDN-based setups showed no packet loss and significantly higher bandwidth utilization, reaching up to 94.478%. This study highlights the potential of SDN to enhance network efficiency and scalability, addressing a gap in the literature through scenario-based comparative analysis. Future research directions include exploring large-scale topologies, security enhancements, and advanced optimization techniques. © 2025 IEEE.

关键词： Packet loss

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A Variational Inference-Based LSTM-enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Ines Raissa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala8580 Cameroon Department of computer engineering University of Buea Faculty of Engineering and Technology Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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Paths, Proofs, and Perfection: Developing a Human-Interpretable Proof System for Constrained Shortest Paths 38

Paths, Proofs, and Perfection: Developing a Human-Interpreta...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Sidorov, Konstantin de Almeida Correia, Gonçalo Homem de Weerdt, Mathijs Demirović, Emir Algorithmics Group Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Faculty of Civil Engineering and Geosciences Delft University of Technology Netherlands

ISBN: (纸本)1577358872

People want to rely on optimization algorithms for complex decisions but verifying the optimality of the solutions can then become a valid concern, particularly for critical decisions taken by non-experts in optimization. One example is the shortest-path problem on a network, occurring in many contexts from transportation to logistics to telecommunications. While the standard shortest-path problem is both solvable in polynomial time and certifiable by duality, introducing side constraints makes solving and certifying the solutions much harder. We propose a proof system for constrained shortest-path problems, which gives a set of logical rules to derive new facts about feasible solutions. The key trait of the proposed proof system is that it specifically includes high-level graph concepts within its reasoning steps (such as connectivity or path structure), in contrast to using linear combinations of model constraints. Using our proof system, we can provide a step-by-step, human-auditable explanation showing that the path given by an external solver cannot be improved. Additionally, to maximize the advantages of this setup, we propose a proof search procedure that specifically aims to find small proofs of this form using a procedure similar to A* search. We evaluate our proof system on constrained shortest path instances generated from real-world road networks and experimentally show that we may indeed derive more interpretable proofs compared to an integer programming approach, in some cases leading to much smaller proofs. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Integer programming

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Camouflage Object Segmentation with Attention-Guided Pix2Pix and Boundary Awareness 14

Camouflage Object Segmentation with Attention-Guided Pix2Pix...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Sany, Erfan Akbarnezhad Naserizadeh, Fatemeh Sinichi, Parsa Hosseini, Seyyed Abed Faculty of Computer Engineering Quchan University of Technology Quchan Iran Faculty of Electrical and Computer Engineering Malek Ashtar University of Technology Tehran Iran Department of Electrical Engineering Mashhad Branch Islamic Azad University Mashhad Iran

ISBN: (纸本)9798331511272

Segmentation of camouflaged objects presents a significant challenge in computer vision due to their inherent blending with the background environment. This study proposes an innovative method for precise camouflage segmentation using an advanced Pix2Pix architecture. The proposed method leverages the VGG16 network as a backbone for extracting robust features. Subsequently, convolutional block attention modules are integrated to refine these features by focusing attention on spatial and channel dimensions. The generator's decoding pathway employs upsampling stages and skips connections to reconstruct high-resolution segmentation maps effectively. Simultaneously, a discriminator network differentiates between ground truth segmentation maps and the generated ones. To optimize the model's performance, a combination of adversarial, L1, and Soft Dice losses was used during training. Here, the Dice score loss function was employed twice. Firstly, it was used to quantify the disparity between the ground truth mask and the mask predicted by the generator. Secondly, it was applied to assess the difference between the boundary of the ground truth mask and the boundary of the predicted region extracted by morphological techniques. Comprehensive evaluations of the CAMO dataset show the effectiveness of the proposed model. This method obtains a pixel accuracy of 85.4054%, intersection over union of 43.9517, F-measure of 59.3806, and mean absolute error of 0.1307. These results highlight advancements in developing robust computer vision systems to identify and understand camouflage strategies in real-world scenarios. ©2024 IEEE.

关键词： Camouflage

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YOLOatt-Med: YOLO-Based Attention Mechanism for Medical Image Classification 14

YOLOatt-Med: YOLO-Based Attention Mechanism for Medical Imag...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Naserizadeh, Fatemeh Sany, Erfan Akbarnezhad Sinichi, Parsa Hosseini, Seyyed Abed Faculty of Electrical and Computer Engineering Malek Ashtar University of Technology Tehran Iran Faculty of Computer Engineering Quchan University of Technology Quchan Iran Department of Electrical Engineering Mashhad Branch Islamic Azad University Mashhad Iran

ISBN: (纸本)9798331511272

Early and accurate diagnosis of respiratory diseases can significantly reduce mortality rates. This study presents a novel approach for classifying respiratory diseases from medical images using computer vision. The approach combines the YOLOv8 object detection model with CBAM to boost feature extraction and enhance classification precision. The effectiveness of the method was tested on two datasets: chest computed tomography scans for lung cancer detection and chest X-rays for pneumonia diagnosis. Results indicate that the CBAMYOLOv8 model surpasses current leading methods, underscoring the method's efficiency in emphasizing essential image features for precise disease classification. ©2024 IEEE.

关键词： Lung cancer

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