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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Jadala, Vijaya Chandra Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jumlesha, Shaik Sesadri, U. School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Green Fields Vaddeswaram Guntur522502 India College of Engineering Andhra University Department of Computer Science and Systems Engineering Visakhapatnam531162 India Assam down town University Faculty of Computer Technology Assam Guwahati India AP Tirupati517501 India Vardhaman College of Engineering Department of Cse Telangana Hyderabad India

ISBN: (纸本)9798350377972

Traditional rehabilitation methods often focus on a single impairment, leading to challenges such as low motivation and inadequate responses to the diverse needs of patients, time taken for appointments, and lack of personal monitoring of health status. To address these issues, The model employs meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, targeting multiple impairments while tailoring interventions to individual patient requirements. A hybrid model proposed would integrate cognitive and machine learning methodologies to enhance personalized rehabilitation. By selectively employing meta-cognitive strategies, dynamic adaptive training, and modified Convolutional Neural Networks (CNN) within an ensemble framework, the proposed system aims to bridge existing gaps in rehabilitation practices. The hybrid system is designed to efficiently manage patient data, reduce the complexity of rehabilitation tasks, and optimize computational resources. The use of stacking as an ensemble method further mitigates biases and uncertainties associated with individual models, thereby improving the reliability of the rehabilitation framework. Preliminary outcomes indicate that this hybrid approach achieves superior accuracy and efficiency compared to traditional methods, ultimately enhancing adaptability to the varied needs and preferences of patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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The Efficient Cluster Head Selection for NarrowBand Internet of Things

The Efficient Cluster Head Selection for NarrowBand Internet...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Shih-Yun Lee, Shou-Yu Wu, Tin-Yu Chang, Yao-Chung Chao, Han-Chieh Tunghai University Department of Computer Science Taichung Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Taiwan National Taitung University Department of Computer Science and Information Engineering Taitung Taiwan National Dong Hwa University Department of Electrical Engineering Hualien Taiwan

ISBN: (纸本)9798350324174

The future of 5G will focus on massive machine-type communication (mMTC), and many applications will be based on this application and gradually mature. These applications include smart cities, e-health, and the Internet of Vehicles (IoV). In addition, the Narrowband Internet of things (NB-IoT) is a critical technology in mMTC. NB-IoT can bring many benefits, such as efficient bandwidth use and long-distance transmission. However, although NB-IoT has many advantages, issues such as throughput, energy consumption, and security still need to be solved. In order to effectively solve these problems, the choice of cluster heads is very important. Considering that the energy consumption of the CH will be more severe than that of other devices, which will also shorten the network;therefore, we will focus on the dynamic cluster head selection. To solve this problem, we introduced the concept of the pseudo-rigid body into this paper and proposed a method similar pseudo-rigid body to determine the CHs;at the same time, we also defined the maximum range of each cluster. The experimental results show that the proposed method can dynamically select the CH and increase the network lifetime. © 2023 IEEE.

关键词： Internet of things

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Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Reception and Signal Decoding 2

Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Recep...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Waseem, Ahsan Kamil, Mohd Azhan Umar Bhardwaj, Abhisehek Razim, Abdul Qadeer, Mohammed Abdul Ghazal, Taher M. Aligarh Muslim University Department Of Electrical Engineering Aligarh India Aligarh Muslim University Department Of Computer Engineering Aligarh India Stony Brook University Department Of Electrical And Computer Engineering New York United States Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configuration for receiving circularly polarized signals, using a Software Defined Radio (SDR) at 137.62 MHz and a V-dipole antenna. The directional characteristics of the V-dipole maximize capture efficiency by improving signal reception during particular satellite orbits. The flexible SDR, in particular the RTL SDR USB dongle, is used to detail signal decoding for Automatic Picture Transmission (APT), allowing for effective processing and tracking. High-quality APT photos are produced by this hardware-software integration, highlighting NOAA's crucial role in international weather forecasting. The study also emphasizes how simple and inexpensive it is to use the V-dipole antenna to receive NOAA weather satellite signals. © 2024 IEEE.

关键词： Weather forecasting

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PResCon: Physiological Response Augmented Continuous Emotion Annotation Tool 17

PResCon: Physiological Response Augmented Continuous Emotion...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Banik, Swarnali Adithya, Akhilesh Mitra, Bivas Sen, Sougata Saha, Snehanshu Ghosh, Surjya Amazon India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India BITS Pilani K K Birla Goa Campus Department of Computer Science and Information Systems India BITS Pilani K K Birla Goa Campus APPCAIR Goa India

ISBN: (纸本)9798331531195

We present an annotation tool designed to collect fine-grain continuous emotion ground truth labels and physiological responses as users watch different videos. In the existing literature, continuous emotion annotations are typically collected as self-reports based on the 2D Circumplex model through auxiliary devices (a joystick or similar tools) during video consumption. However, the users face challenges as they need to use an auxiliary device and rely on the Circumplex model's valence and arousal axis without clear indication of distinct emotion labels. Therefore, to overcome these challenges, we introduce PResCon, a tool that allows to collect emotion annotations using keyboard arrow keys only (without using any auxiliary device) and guiding users towards the discrete emotions (e.g., amused, stressed, bored, relaxed) by embedding them on the Circumplex plane. The tool encompasses two major components - (a) a web interface that displays various stimuli videos and provision of collecting emotion labels (valence and arousal) by moving the keyboard arrow keys and (b) a sensor setup (based on Arduino) that allows to collect the physiological responses (heart rate and galvanic skin response) passively during video consumption. In summary, the PResCon tool facilitates research in the domain of affective computing by allowing to collect fine-grain continuous emotion annotations and the physiological responses in real-time during video consumption. © 2025 IEEE.

关键词： Auxiliary equipment

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Textual Emotion Recognition: Advancements and Insights 6

Textual Emotion Recognition: Advancements and Insights

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Esmail, Ahmed A. Abdallah, Sayed M. Elsayed, Mohamed A. Ghaith, Amr E. Elbanhawy, Magy Al-Zoghby, Aya M. Moawad, Ibrahim F. Artificial Intelligence Science New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computers and Information Dakhlia Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Portsaid University Faculty of Engineering Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Department Damietta Egypt Ain Shams University Information Systems Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Emotion Recognition

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Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

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Multimedia Tools and Applications 2024年 1-33页

作者： Sharma, Santosh Kumar Muduli, Debendra Rath, Adyasha Dash, Sujata Panda, Ganapati Shankar, Achyut Dobhal, Dinesh Chandra Department of Computer Science and Engineering C.V. Raman Global University Odisha India Department of Information Technology School of Engineering and Technology Nagaland University Dimapur India Department of Cyber Systems Engineering WMG University of Warwick CoventryCV74AL United Kingdom University Centre for Research & amp Development Chandigarh University Punjab Mohali140413 India School of Computer Science Engineering Lovely Professional University Punjab Phagwara144411 India Center of Research Impact and Outcome Chitkara University Punjab Rajpura India Department of Computer Science & amp Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. The proposed model adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Afterwards, we have utilized a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. For classification purposes, we have employed a least square support vector machine (LS-SVM) equipped with three kernels: linear, polynomial, and radial basis function (RBF). This setup has been utilized to classify fundus images as either indicative of glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more streamlined network layout compared to conventional classifiers. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Radial basis function networks

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Privacy Concerns from Single-Word Search Query Leakage from Web Browsers Through DNS

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 48-52页

作者： Isobe, Katsuki Kondo, Daishi Tode, Hideki Computer Science Course College of Engineering Osaka Prefecture University Sakai599-8531 Japan Department of Computer Science and Intelligent Systems Graduate School of Engineering Osaka Prefecture University Sakai599-8531 Japan

Some Web browsers working on certain operating systems (OSs) specify that a single-word search query entered into the search bar be sent to the recursive resolver of the domain name system (DNS) as a DNS request that includes the word. Such behavior is unexpected for users and induces privacy concerns through single-word search query leakage. We present our survey on the conditions during which this behavior occurs. By analyzing the DNS traffic captured at the recursive resolvers, we demonstrate that a considerable number of queries are leaked and the interests of individuals or organizations can be inferred through passive eavesdroppers. © 2019 IEEE.

关键词： Search engines

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SDN-Enabled Digital Twins: A Framework for Wireless SDN Simulation and Optimization 22

SDN-Enabled Digital Twins: A Framework for Wireless SDN Simu...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Trotta, Angelo Miccichè, Mario Heideker, Alexandre Di Felice, Marco University of Bologna Department of Computer Science and Engineering Italy Advanced Research Center on Electronic Systems 'Ercole De Castro' University of Bologna Italy

ISBN: (纸本)9798331508050

The dynamic nature of modern wireless networks, combined with the need for efficient management, has heightened interest in Software-Defined Networking (SDN). This paper presents a simulation framework designed to effectively model SDN in wireless network environments. The framework addresses key challenges in managing heterogeneous networks, offering a versatile platform for the development and evaluation of SDN applications. A key feature of the framework is the integration of a Digital Twin (DT) module within the SDN controller, leveraging the controller's comprehensive view of the network. This integration allows for real-time construction and updating of a virtual representation of the network, enhancing the controller's decision-making capabilities. By predicting future network states through a neural network model, the DT facilitates proactive management strategies, such as routing adjustments and resource reallocation, which are essential for maintaining optimal network performance. The paper details the architectural design and implementation of the framework, including the integration of Mininet, OMNeT++, and the Ryu controller. Our results demonstrate the framework's effectiveness in simulating complex SDN scenarios and providing detailed analyses of network behavior. © 2025 IEEE.

关键词： Risk perception

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