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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Meenakshi, K. Ravinder, Bathini Pasha, Mohammed Rehaman Thinakaran, K. Jayanayudu, Dabbara Maniraj, S.P. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Vadapalani India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Siddartha Institute of Science and Technology Department of Electronics and Communication Engineering Andhra Pradesh Puttur India SRM Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer's patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand. © 2024 IEEE.

关键词： Alzheimer's disease Cloud computing Decision trees Early detection Healthcare

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An Effective Cervical Cancer Detection Approaches - A Comparative Analysis 6

An Effective Cervical Cancer Detection Approaches - A Compar...

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

作者： Maheswari, S. Marimuthu, C.N. Deepa, B. Geetha, S. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350394702

The chance to preserve a life is provided by early identification of cervical cancer, which is the fourth most common malignancy among women globally. Early diagnosis can lower its frequency. However, due to a number of variables, manual screening techniques are ineffective in detecting cervical cancer. However, this leads to incorrect diagnoses and excessive therapy. Therefore, researchers suggested cervical identification using both conventional and deep learning (DL) methods. This study examines future approaches for automated cervical cancer diagnosis and reviews prior work, focusing on the DL area. This is thought to ensure accurate diagnosis and may possibly lower the incidence of cervical cancer. The comparison chart is provided among various techniques in which EL-SVM model shows higher accuracy. © 2023 IEEE.

关键词： Diagnosis

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A novel end-to-end approach for epileptic seizure classification from scalp EEG data using deep learning technique

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4223-4231页

作者： Kumar, Puranam Revanth Shilpa, B. Jha, Rajesh Kumar Mohanty, Sachi Nandan Department of Electronics and Communication Engineering Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education Hyderabad India School of Computer Science and Engineering (SCOPE) VIT-AP University Andhra Pradesh Amaravathi India

Early detection and proper treatment of epilepsy seizure is essential and meaningful to those who suffer from this disease. Symptoms of seizures are confusion, abnormal gazing, and obsessive hand movements. Neurological exams, blood testing, cognitive tests, and neuroimaging modalities are some of the techniques used to detect epileptic seizure activity in patients. Among them, neuroimaging techniques have attracted a lot of interest from doctors in the field. The use of computer-aided diagnostic systems (CADS) based on deep learning (DL) and neuroimaging modalities can help quickly and accurately diagnose epileptic seizures. This research introduces a novel seizure detection approach based on a bidirectional long short-term memory (Bi-LSTM) network. As a result, Bi-LSTM preserves the nonstationary nature of electroencephalogram (EEG) data while decreasing processing costs by employing local mean decomposition (LMD) and statistical feature extraction algorithms. Two distinct LSTM networks with opposing propagation directions are combined in the deep architecture. So, the deep model can use information from both before and after the current time of analysis to decide the output state. On a long-term scalp EEG database, an average accuracy of 97%, sensitivity of 95.70%, specificity of 93.90%, and G-Mean of 94.80% were achieved, with an anticipation time of 10 min. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Automatic seizure detection Bi-LSTM Deep learning EEG Data Epilepsy Local Mean Decomposition (LMD)

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Development of a Neural Network System for Predicting Topsoil Moisture Using Remote Sensing and Rainfall Forecast Data 4

Development of a Neural Network System for Predicting Topsoi...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Martinez, Miriam Zambudio Da Silveira, Larissa Haringer Martins Marin-Perez, Rafael Gomez, Antonio Fernando Skarmeta University of Murcia Faculty of Computer Science Murcia Spain Odin Solutions Sl Murcia Spain University of Murcia Dept. of Information and Communication Engineering Murcia Spain

ISBN: (纸本)9798331519872

With only 1% of the planet's water resources accessible in rivers and lakes, and with droughts on the rise, the scarcity of fresh water is becoming an increasingly serious problem for the world's population. Of this 1%, 70% is used by the agricultural sector, which tends to consume more of this scarce resource due to population growth and the resulting pressure to increase agricultural production. One of the fundamental variables in this context is soil surface moisture, which is particularly important for the management of water resources and has a direct impact on the interaction between the soil and the atmosphere. Therefore, this work discusses the current advances made in the measurement of this variable, which is fundamental for precision agriculture, and creates a model capable of predicting topsoil moisture in a study basin located in the southeast of the Iberian Peninsula, characterised by its predominantly agricultural. To develop the model, we used topsoil moisture data from the Sentinel-1 satellite, probes distributed over the study area, precipitation measurements from meteorological stations and ensemble forecasts from meteorological models. The final model predicted surface soil moisture (SSM) with an average Root Mean Squared Error (RMSE) of 0.15 and a maximum difference between observed and predicted data of 1.37%, demonstrating the ability to predict from one to ten days in advance. © 2024 IEEE.

关键词： Soil moisture

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Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

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computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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An Efficient Attack Detection Framework in Software-Defined Networking using Intelligent Techniques 3

An Efficient Attack Detection Framework in Software-Defined ...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Hassan, Heba A. El-Din Hemdan, Ezz Shokair, Mona El-Samie, Fathi E. Abd El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf32952 Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Menouf32952 Egypt Prince Sultan University Security Engineering Laboratory Department of Computer Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Recently, Software-Defined Networking (SDN) architecture has offered great benefits due to the separation between the control and network elements such as routers and switches. Unfortunately, the enormous growth of attacks hinders the wide adoption of SDN. Intrusion Detection Systems (IDSs) are used as significant tools to detect and mitigate network attacks of anomalous nature. Several Deep Learning (DL) models, such as Convolutional Neural Networks (CNNs), have been utilized for building IDSs in cyber security in recent years, because they achieve the desired detection results. Nonetheless, little work has been introduced on IDSs in SDN systems. Hence, this paper presents an efficient framework based on intelligent techniques for predicting attacks in SDN systems. This work focuses on the early detection of abnormal attacks existing in an SDN environment. When malicious traffic is identified in an SDN topology, the Artificial Intelligence (AI) module employs Machine Learning (ML) or DL models to identify and stop the attack source. The architecture presented in this research allows for comparing several ML and DL classification techniques that can be used to identify different sorts of network attacks. The proposed framework is tested on the InSDN dataset using several learning models like Random Forest (RF), AdaBoost (AB), K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Logistic Regression (LR) classifiers. In addition, DL models such as Deep CNN and Long Short Term Memory (LSTM) are considered. The results demonstrate that the proposed Deep CNN model for multi-class attack data classification achieves the highest accuracy of 99.85% compared to RF, AB, KNN, NB, DT, LR, and LSTM classifiers with accuracy levels of 95%, 88 %, 97%, 93 %, 90 %, 98%, and 88.31 %, respectively. © 2023 IEEE.

关键词： Software defined networking

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The Analysis of Device-to-Device (D2D) communication System Operating in 5G Underlaying Cellular Networks on Multiple Bands over Nakagami-m Fading Channels

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SN computer science 2025年第5期6卷 1-15页

作者： Shatnawi, Razan A. Khodeir, Mahmoud A. Al-Mistarihi, Mamoun F. Darabkh, Khalid A. Al-Zuraiqi, Eyad T. Qasaymeh, Mahmoud M. Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid 22110 Jordan Computer Engineering Department School of Engineering The University of Jordan Amman 11942 Jordan Department of Telecommunications Engineering Hijjawi Faculty for Engineering Technology Yarmouk University P.O. Box 566 Irbid 21163 Jordan Computer Engineering and Communication Department Tafila Technical University Tafila 66110 Jordan

In contrast to traditional cellular connection, device-to-device (D2D) communication is a direct connection amidst adjacent mobile users that does not pass through the base station (BS) and does not rely on network infrastructure to meet low power requirements and high data rate. Cellular networks and D2D connections were combined in underlay mode to address power usage concerns. In underlay D2D communication, licensed frequency bands are utilized by both D2D and cellular connections;due to resource sharing, these bands may overlap, which lowers service quality but boosts spectral efficiency. Most existing research exclusively discuss energy efficiency (EE) in the context of single cells. This article has taken into account the entire cellular network with D2D connections on various bands, and stochastic geometric theory has been used in order to obtain closed form expressions for the probability of successful transmission (STP), the average sum rate (ASR), and EE for D2D connection underlaid cellular networks over Nakagami-m fading channels. Results of simulations are used to validate analytical conclusions. Simulation results demonstrate that higher STP, higher ASR, and lower EE all result in enhanced system performance. In addition, as the shape parameter’s value m (Nakagami-m order) increases, the EE, ASR, and STP of D2D users improve, which enhances system performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Device-to-device communication Energy efficiency Nakagami-m fading channel Stochastic geometry

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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Brain Chips in the Rough: Computational Modeling of Brain Stimulation by an Intercept and Replace Machine Learning Model

Brain Chips in the Rough: Computational Modeling of Brain St...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Harika Korkusuz Jiang Liu Faculty of Science and Engineering Computer Science and Communication Engineering Waseda University Tokyo Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

Inthis study, we proposed a closed-loop transformer neural network decoder-based signal generation method to record, decode, and stimulate the brain to intercept a previously functional brain area. The dataset consists of the auditory cortex output of computational modeling of the human auditory system, following voice input. The system is comprised of three phases. In Phase I artificial brain signal was generated based on a generative sequence-to-sequence machine learning model. In Phase II the cortical area downstream was stimulated with the generated brain signal, effectively replacing the upstream cortical area on which the machine learning model is based. Finally, in Phase III, incremental learning is applied to fine-tune the model to unlock differences between what is being recorded by our electrodes and the hidden neural patterns of the brain. As an evaluation, we computed RMSE and similarity index between the generated stimulation output and inputted original computational model output without any interception. The generative regression model using our proposed method shows an average RMSE = 0.009845, and similarity index (SI) = 0.9416 across the models without an word embedding layer, and SI = 0.855283 and RMSE = 0.033068 for the model with a word embedding layer. This could be evidence to support that the Transformer-Decoder model has extracted distinguishable features during heard speech.

关键词： Electrodes Incremental learning Computational modeling Auditory system Brain modeling Transformers Feature extraction Indexes Brain stimulation Biological neural networks

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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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