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Informatica (Slovenia) 2023年第9期47卷 133-144页

作者： Abdulsaed, Entesar H. Alabbas, Maytham Khudeyer, Raidah S. Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq

Convolutional neural networks (CNNs) have exceptionally performed across various computer vision tasks. However, their effectiveness depends heavily on the careful selection of hyperparameters. Optimizing these hyperparameters can be challenging and time-consuming, especially when working with large datasets and complex network architectures. In response, we propose a novel approach for hyperparameter optimization in CNNs using the Salp Swarm Algorithm (SSA). Based on the natural behavior of mollusks, SSA mimics the collective intelligence that governs feeding and navigation. Taking advantage of SSA's unique properties, our research thoroughly explores the hyperparameter space. This exploration aims to identify the algorithm that maximizes CNNs performance. This paper presents the architecture of the SSA-based framework for hyperparameter optimization and compares it to other established optimization techniques, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). We also present experimental results using the MNIST and fashion MNIST datasets, achieving an impressive classification accuracy of 99.46% for MNIST and 94.53% for fashion-MNIST. This case study not only contributes to the fields of deep learning and hyperparameter optimization by demonstrating the effectiveness of SSA in optimizing CNNs, but it also provides benefits to researchers and practitioners who are looking for optimal hyperparameter configurations for CNNs in a variety of computer vision applications. We also evaluate the scalability and robustness of our proposed method in the context of different CNNs structures. The insights we gained highlight SSA's potential for addressing challenges related to hyperparameter optimization. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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An Efficient Steganographic Approach for Securing Text Transmission Using S-Box in Cloud 2

An Efficient Steganographic Approach for Securing Text Trans...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ashraf, Humaira Malik, Javeria Ihsan, Uswa Al-Quayed, Fatima Humayun, Mamoona Jhanjhi, N.Z. International Islamic University Department of Computer Science and Software Engineering Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Taylor's University Malaysia Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

In era of modern world and technology where everything is digitalized. Cloud computing emerged as one of the fastest growing parts of digital industry. With the Succor of cloud computing models, business accomplishes their objectives with least exertion when contrasted with traditional processing environment. In spite of these compensations, yet security of the information in cloud database is the significant fear in endorsement of cloud. Although several techniques have been proposed such as cryptographic and steganographic approaches, but all of these have some loopholes. Therefore, in this paper new data protection technique based on steganographic encryption is proposed which has less computational time and secure against known plain text and Man-In-Middle Attack. © 2023 IEEE.

关键词： Cryptography

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Analysis of Bio-Inspired approaches and Social Media Analytics in Twitter datasets for Misinformation Detection 1

Analysis of Bio-Inspired approaches and Social Media Analyti...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Nicholas, Neomi Nelin Nirmalrani, V. Sathyabama Institute of Science and Technology Faculty of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350342796

Bio-inspired algorithms, such as those with regard to collective intelligence and evolutionary algorithms have been increasingly used in social media analytics in recent years. These algorithms have been shown to be effective in many areas, such as sentiment analysis, community identification, and misinformation detection. This paper explains the underlying principles and features of the most recent bio-inspired techniques. The effectiveness of bio-inspired algorithms for the sentiment analysis categorization on Twitter datasets is studied. In this paper, several social media spam detection techniques that are currently in use are tabulated, and detailed study on the use of social media analytics and bio-inspired algorithms to detect misinformation in social media datasets is performed. The findings of the proposed analysis throws light into the efficiency of these algorithms and gaps for more research, which is inevitable to further improve their performance and robustness for misinformation detection. © 2023 IEEE.

关键词： bio inspired algorithms misinformation detection optimization social media analytics twitter datasets

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CFedKT: Clustered Federated Learning with Knowledge Transfer 7

CFedKT: Clustered Federated Learning with Knowledge Transfer

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhang, Long Zhu, Zhiqin Zhou, Haitao Li, Yuanyuan Qi, Guanqiu Chongqing University of Posts And Telecommunications College of Computer Science And Technology Chongqing China Chongqing University of Posts And Telecommunications College of Computer Science And Technology Sichuan China State University of New York At Buffalo State Computer Information Systems Department Buffalo United States

ISBN: (纸本)9798350359145

Traditional federated learning (FL) aggregates the models from individual clients to obtain a globally more generalizable model. In practical scenarios, the effectiveness of aggregating a global model is compromised due to data heterogeneity. Clustered federated learning (CFL) partitions clients into clusters based on the correlations of their held data to mitigate the impact of data heterogeneity. However, the lack of global knowledge sharing limits the generalization ability of the cluster-based models. Therefore, we propose a novel cluster federated learning algorithm with knowledge transfer: CFedKT. The method uses a spectral clustering algorithm to cluster clients according to the weight update vectors of each client model and aggregates multiple cluster models to update the corresponding client model. We introduce a global model to facilitate knowledge transfer among models from different clusters, aiming to strike a balance between the performance of the global model and cluster-specific personalized features. In the experiments, CFedKT demonstrated significant improvements in accuracy and communication efficiency across different tasks and diverse data heterogeneity scenarios. Additionally, CFedKT showed a certain level of robustness and effectiveness under varying degrees of data heterogeneity. © 2023 IEEE.

关键词： Aggregates

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Revolutionalize Your Learning Experience with EQU ACCESS 4

Revolutionalize Your Learning Experience with EQU ACCESS

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Raveenthiran, Gawsan Sivarajah, Kaanushan Mohamed Riyal, Akash Ahamed Kugathasan, Vethurja Chandrasiri, Sanjeevi Rajendran, Karthiga Sri Lanka Institute of Information Technology Department of Computer Science & Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350395914

This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners. © 2024 IEEE.

关键词： Contrastive Learning

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Model view controller (MVC) architecture and client-to-client file transfer protocol with binarized spiking neural network for building a smart home appliances control in IoT

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International Journal of information technology (Singapore) 2023年第6期15卷 3189-3200页

作者： Yadawad, Ranganath Kulkarni, Umakant P. Department of Computer Science and Engineering SDM College of Engineering and Technology Karnataka Dharwad 580002 India

In the time Internet of Things (IoT) the term “Smart” is mostly used because of its application in various entities such as smart cities, smart homes, and smart cars. These are done by integrating Machine Learning (ML) algorithms along with IoT devices. In the context of the smart home, many researchers use IoT designs with Deep Learning (DL) techniques, although the majority of them are limited because of hardware issues of the end devices. So, the transport of detected messages from IoT devices to the application layer takes longer when object detection techniques are used in real-time systems, which lowers the DL’s accuracy. To overcome these issues a model view controller (MVC) architecture and client-to-client file transfer protocol (C2CFTP) with binarized spiking neural network (BS4NN) for building a smart home appliances control in IoT is proposed in this manuscript. In this smart home automation system, users can remotely monitor their houses by employing an object detection algorithm built on the client-to-client model view controller (C2CMVC) architecture. C2CMVC architecture consists of three layers such as bottom Layer, Repository layer, and application layer. This, IoT-based sensing device, collects data from a laptop or personal computer (PC) that has a camera attached using the bottom layer. For object detection, the BS4NN is employed, and when an object is discovered, a message is delivered to the C2CFTP. The middle layer, also known as the repository layer, in this, the C2CFTP broker used. C2CFTP is a novel protocol which is efficiently transfers messages to the application layer. Users receive notifications by short message service (SMS) and email, and the system's application layer sends a command message. The performance of the proposed method attains 32.0%, 1.0%, 15.0%, and 15.0% lower delay is likened to the existing methods. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management

关键词： Binarized Spiking Neural Network Client-to-client file transfer protocol Internet of Things Model view controller architecture Object detection

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Brain Cancer Detection With MRI and ML 2

Brain Cancer Detection With MRI and ML

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Priyesh Srivastava, Shashank Gupta, Riya Kumar, Sanjay Galgotia College of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Greator noida India

ISBN: (纸本)9798350371055

Brain based cancer is a devastating and potentially fatal disease that demands efficient & timely diagnosis. [MRI] magnetic resonance-imaging has come as a very important utility for early detection and evaluation of tumors located inside the brain. In past few years, machine learning techniques (MLT) has played pivotal role in enhancing the diagnostic accuracy of brain cancer detection from MRI scans. This paper presents a deep review of many papers focused on integration of MRI and machine learning for brain cancer detection. Our primary objective is to evaluate the advancements and identify gaps within these studies to determine the most effective ML algorithm for building an optimal brain cancer detection model. Through a systematic analysis of the selected research papers, we assess the methodologies, datasets, and outcomes of various ML approaches, while considering their strengths and limitations. By comparing and contrasting the methodologies and results of the surveyed papers, we aim to make informed recommendations on the choice of an ML algorithm for brain cancer detection. Our review seeks to contribute to the ongoing efforts to improve early-stage detection and management of brain tumors, emphasizing pivotal role of machine learning in enhancing the medical techniques for imaging. © 2024 IEEE.

关键词： convolutional neural networks datasets Deep-learning methods diagnostic machine-learning methodologies MRI scans

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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Multi Face Detection Based Attendance System 23th

Multi Face Detection Based Attendance System

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Prasanna Kumar, K.R. Madhumitha, S. Madhushree, S. Vijiishwarya, M. Logeswaran, K. Devendran, K. Department of Information Technology Kongu Engineering College Perundurai India Department of Artificial Intelligence Kongu Engineering College Perundurai India Department of Computer Science and Engineering Kongu Engineering College Perundurai India

ISBN: (纸本)9783031646492

Face recognition-based data driven solutions are used to solve many real-time problems in day-to-day life. At present some of the known methods like physical presence verification, Id card scanning system and single face system are used in attendance marking purpose. The existing system often focuses on single face detection which consumes more time. Moreover, they do not address the challenges like bulk entry of individuals at a point of time. This limitation hinders their effectiveness in scenarios where multiple faces need to be detected simultaneously in crowded entry and exits like hostels, library, and apartment and so on. This proposed system is designed to detect and track multiple faces simultaneously from the live video streams. Additionally, it includes a time stamping mechanism that records the exact time when each face is captured. Also, it records the unknown faces with entry and exit time details which helpful to trace the people at later point of time. The proposed solution is applied to track the movements in entry and exit points of the Hostel also supports to monitor the unauthorized movements inside the hostel. The unauthorized person movement records help to enhance their security. So, it eliminates the manual attendance marking and speed up the overall process. The proposed real-time multiple face detection system fills this gap by introducing advanced algorithms like haarcascade and innovative features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Face recognition

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Accurate and Efficient Brain Tumor Segmentation using transfer learning and residual networks 5

Accurate and Efficient Brain Tumor Segmentation using transf...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Sanjay Agarwal, Vivek Sharma, Vishnu Garg, Saksham Sharma, Manish Kumar Gupta, Riya Prashant, Shubham Galgotias College Of Engineering & Technology Computer Science Engineering Department Greater Noida India Galgotias College Of Engineering & Technology Information Technology Department Greater Noida India

ISBN: (纸本)9798350330861

Accurate and specific segmentation of brain tumour is essential in medical imaging for accurate diagnosis and planning of treatments. In this research paper, we suggest the use of a Densenet121 model for tumor recognition and a ResUnet model for segmentation. We fine-tune the DenseNet121 model using transfer learning techniques on a new dataset, with pre-trained weights obtained from a large-scale dataset (ImageNet). By leveraging the strengths of transfer learning with Densenet121's and ResUnet's robust feature extraction capabilities, our model effectively detects and segments tumors from Brain MRI Images. This approach can speed up the exercise procedure and improve the model's accuracy, especially for small datasets. We validated our approach on 'The Cancer Genome Atlas (TCGA)' dataset from The Cancer Imaging Archive (TCIA) and compared it with several classification and segmentation deep learning models. The experimental results on the testing data demonstrate that our approach is more efficient and accurate than other deep learning networks. This approach can substantially accelerate the training process and enhance the accuracy of the model, particularly in scenarios where the new dataset is small. We have validated our proposed approach on 'The Cancer Genome Atlas (TCGA)' dataset from The Cancer Imaging Archive (TCIA). We have compared our approach with various classification and segmentation deep learning models, and the experimental results on the testing data demonstrate that our method performs better in terms of precision and effectiveness than other deep learning networks. © 2023 IEEE.

关键词： Magnetic resonance imaging

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