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computer sciences, Engineering, and technology Innovation (ICoCSETI), International Conference on

作者： Handrizal Poltak Sihombing Erna Budhiarti Nababan Mohammad Andri Budiman Doctoral Program in Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

In today's digital era, the influence of social media influencers has grown significantly. A commonly used feature by business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, highlighting the need for a more sophisticated approach to influencer detection. This study proposes a novel method for influencer detection that integrates the Leiden coloring algorithm and Degree centrality. This approach leverages network analysis to identify patterns and relationships within large-scale datasets. First, the Leiden coloring algorithm is employed to partition the network into distinct communities, identifying potential influencer clusters. Subsequently, Degree centrality is utilized to identify nodes within these communities exhibiting high connectivity, indicating individuals with significant influence. The proposed method was validated using data crawled from Twitter (X) social media, employing the keyword "GarudaIndonesia." The data was collected using Tweet-Harvest between January 1, 2020, and October 16, 2024, resulting in a dataset of 22,623 rows. The proposed method was compared to the Louvain coloring method, demonstrating an increase in the modularity value of the Leiden coloring algorithm by 0.0195, a reduction in processing time by 13.93 seconds, and a decrease in the number of communities by 649.

关键词： Technological innovation Social networking (online) Blogs Clustering algorithms Network analyzers Feature extraction Partitioning algorithms Business

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Advanced Semantic Communication Techniques for IoT Using Disentangled information Bottleneck

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IEEE Internet of Things Journal 2025年第12期12卷 20122-20137页

作者： Xie, Wenwu Xiong, Ming Yang, Liang Wang, Ji Li, Xingwang Yang, Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China

This paper explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on Disentangled information Bottleneck (DisenIB) is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier β in the classical information Bottleneck (IB) framework is eliminated, which avoids the need to manually optimize β several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems. © 2014 IEEE.

关键词： Channel coding

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Implementation Combination of Case-Based Reasoning and Rule-Based Reasoning for Diagnosis of Herpes Disease 6

Implementation Combination of Case-Based Reasoning and Rule-...

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6th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2022

作者： Rachmawati, Dian Elviwani Siregar, Ary Boby Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350320718

The skin is the outermost part of the body that is sensitive to external stimuli. One of the skin diseases that can be transmitted is herpes. Belated in understanding herpes skin disease can be fatal to sufferers because herpes disease that is too late to be treated can affect more serious diseases. Therefore, we need a mobile-based application that can identify the infectious skin disease herpes. An expert system is one of the fields of science that can solve a problem in a system. The expert system has several methods that can be used, namely case-based reasoning (CBR) and rule-based reasoning (RBR) methods. CBR can be used to solve a new problem by utilizing previous problems and RBR is a method that can be used to solve a problem using a rule base, where each rule is obtained from experts and literature studies. By applying the combination method of CBR and RBR which can be used to diagnose herpes skin disease, it is hoped that it will prevent the spread of herpes disease from spreading. © 2022 IEEE.

关键词： Case based reasoning

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Fake review detection using transformer-based enhanced LSTM and RoBERTa

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International Journal of Cognitive Computing in Engineering 2024年第1期5卷 250-258页

作者： Mohawesh, Rami Bany Salameh, Haythem Jararweh, Yaser Alkhalaileh, Mohannad Maqsood, Sumbal College of Engineering Al Ain University Al Ain United Arab Emirates Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Department of Computer Science Jordan University of Science and Technology Jordan College of Education Al Ain University Al Ain United Arab Emirates School of Information Technology University of Tasmania TAS Australia

Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake. © 2024

关键词： Fake detection

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Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

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CAAI Transactions on Intelligence technology 2024年第5期9卷 1126-1171页

作者： Zong-Shan Wang Shi-Jin Li Hong-Wei Ding Gaurav Dhiman Peng Hou Ai-Shan Li Peng Hu Zhi-Jun Yang Jie Wang School of Information Science and Engineering Yunnan UniversityKunmingChina Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhenChina Academic Affairs Office Yunnan University of Finance and EconomicsKunmingChina Faculty of Management and Economics Kunming University of TechnologyKunmingChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirutLebanon Department of Computer Science and Engineering University Centre for Research and DevelopmentChandigarh UniversityChennaiIndia Department of Computer Science and Engineering Graphic Era Deemed to Be UniversityDehradunIndia School of Computer Science Fudan UniversityShanghaiChina Rackham Graduate School University of MichiganAnn ArborMIUSA Research and Development Department Youbei Technology Co.LTDKunmingChina School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhouChina

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation *** the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO *** alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is ***,the adaptive elite-guided search mechanism enhances the balance between exploration and *** modified mutualism phase reinforces the information interaction among particles and local optima *** cooperation of these two mechanisms boosts the overall performance of the basic *** AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test *** results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and *** addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)*** experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

关键词： ANT COLONY optimization CLOUD COMPUTING GENETIC ALGORITHMS SWARM intelligence

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Performance Assessment of Data Centric and Model Centric Approach for Premature Detection of Leaf Disease

Performance Assessment of Data Centric and Model Centric App...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Prabhu, A. Kumar, Sanjay Pundeer, Shivali Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

After rice and maize, cassava is the third most common source of carbohydrates in the world. Recognizing the critical relationship between agricultural productivity and a country's economy, specialists have made significant investments in the early diagnosis of leaf diseases in order to protect the entire plant from degeneration. The abundance of data, improved resource availability, and improvements in learning algorithms are all credited with the recent achievements of Machine Learning (ML) models. In order to improve system efficiency, the traditional method for detecting leaf diseases uses a Model-Centric approach that optimizes hyperparameters. The emphasis has recently switched from model-entered to data-centered techniques, in which the model is kept constant, but dataset quality is increased by addressing issues like label consistency and systematic sampling of training data while choosing the right batches. The current paper comprises using confidence learning to locate and remove noisy labels from the Cassava Leaf Disease Classification dataset. The model is then fed with newly produced high-quality data, and the performance of the model-centric and data-centric approaches are contrasted. This paper intends to highlight the underappreciated potential for increasing system functionality using a data-centric approach rather than to minimize the significance of model-centric approaches. The findings indicate that the data-centric method leads to a performance enhancement of 7.84%. © 2024 IEEE.

关键词： Learning algorithms

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Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data 4

Hand-Foot-Mouth Disease Classification using Features from F...

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4th IEEE International Conference on Artificial Intelligence in Engineering and technology, IICAIET 2022

作者： Mahmood, Atif Azzuhri, Saaidal Razalli Qureshi, Adnan N. Jaan, Palwasha Sadia, Iqra University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia Virtual University of Pakistan Faculty of Science & Technology Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Hand, Foot and Mouth disease (HFMD) is a common viral childhood disease affected by the family of enterovirus and Coxsackie. Current laboratory identification is based on the RT-PCR test, which is expensive, time-consuming, and unsuitable for the pandemic. The SPR- TFBG was biofunctionalized with monoclonal antibody (Mab). Mab is a bioreceptor with an affinity for the virus for detecting EV-A71. A dataset of reflectance spectra of 660 samples of different virus impurities measured with SPR-TFBG biosensor to detect EV-71 virus was developed. The extracted signal has around 4000 different features based on wavelength information. The first subset was selected based on the region of interest analysis, and the dimension has reduced from 4000 to 1496 features. The dimensionality of the large feature set is reduced based on the statistical feature engineering procedure using 10 features including mean, variance, skewness, RMS, kurtosis, standard deviation, range, crest factor, impulse factor and shape factor. Subsequently, classification of the virus (signal) data is achieved through SVM and it is evaluated with different types of kernels. For the evaluation of classifiers, we used accuracy, sensitivity, precision and F1 score performance metrics. The obtained results of accuracy are 87.88 for linear SVM, 86.06 for radial basis, 75.76 for sigmoid SVM, and 75.15 for polynomial SVM, respectively. The results show that for our experiments, Linear SVM performs better than radial, polynomial and sigmoid kernels. This is because projecting the data onto higher dimensions is not required as data exhibits linear properties confirmed by White Neural Network (WNN) test for nonlinearity. © 2022 IEEE.

关键词： Viruses

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STDP-Driven Automated Retinal Circuit with 7nm FinFET for Motion and Looming Detection: A Hybrid Model with Image Analysis

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IEEE Access 2025年 13卷 95594-95608页

作者： Rabeya, Rubina Akter Islam, Md Turiqul Uddin, Shah Muhammad Imtiyaj Al-Shidaifat, Alaaddin Song, Hanjung Choi, Heung-Kook Kim, Hee-Cheol Department of Digital Anti-Aging Healthcare Inje university Gimhae50834 Korea Republic of Department of Nanoscience and Engineering Centre for Nano Manufacturing Inje university Gimhae50834 Korea Republic of Middle East University Faculty of Information Technology Computer Science Department Amman11831 Jordan Department of Computer Engineering Inje university Gimhae50834 Korea Republic of

This paper presents a spike-timing-dependent plasticity (STDP) driven silicon retina designed using 7nm FinFET technology for detecting object motion and looming threats inspired by the biological visual system. The proposed neuromorphic architecture integrates an automated motion spiking circuit with STDP and advanced image analysis, mimicking natural retinal processing to identify potential hazards. Two specialized circuits are introduced to generate bipolar spiking signals for object motion and looming detection. These spikes are processed by an STDP-based spiking neural network, qualifying real-time distance estimation of approaching objects. Model Results demonstrate accurate collision time prognosis based on object dynamics. YOLOv5 and MiDaS enhance object recognition and depth perception, improving situational awareness. The proposed model uses a hybrid strategy combining object detection and depth estimation to identify objects with different spatial distances and confidence levels accurately. Experimental results show a motion detection accuracy of 93.2% and an average latency of around 15 ms, 66 frames per second, which proves this is suitable for time-sensitive applications. Because the 7nm FinFET technology improves system efficiency and responsiveness, it can be used in real-time applications where high-precision motion detection is necessary in dynamic conditions. However, potential challenges include scalability for large-scale deployment and hardware constraints associated with real-time processing on FinFET-based circuits. The proposed system can be used for robotic vision systems, autonomous vehicles, and other contexts where real-time visual processing and threat detection are needed. © 2013 IEEE.

关键词： Motion estimation

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Synchronization and implementation of real-time traffic signal optimization regulator

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Multimedia Tools and Applications 2025年第12期84卷 10725-10745页

作者： Varshney, Abhilasha Dakshayini, M. Gururaj, H.L. Hu, Yu-Chen Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

In most cosmopolitan cities like Bengaluru, the major problems are the woes of congestion, such as increased vehicular density, travel time, stress, and vehicular carbon emissions are the major problems. These issues have led to quantifiable and non-quantifiable losses, such as high vehicle operation and maintenance costs, reduced human productivity and mobility, increased health hazards, etc. Sustainable transportation coupled with Technological interventions could alleviate these issues by efficiently managing the traffic flow. The major aims of this work are to reduce the overall commute time, traffic congestion, and air pollution, making the commutation of public stress-free and happy. So, we propose a comprehensive Real-time Traffic Signal Optimizer and Regulator (RTSOR) that identifies the right High-Density Corridors (HDC) and achieves these aims by successfully attaining the coordination among the Adaptive Traffic Signal Control system (ATSCS) fixed on the successive junctions of HDC considered in this work as a pilot initiative by upgrading the existing traffic signal in Bengaluru City to the latest ATSCS technology. Implementing this proposed RTSOR has effectively lessened the waiting time in traffic, travel time, and fuel consumption and improved air quality by reducing pollution and lowering stress levels, leading to improved quality of health and life of the public. In Bengaluru city, the current innovation would be implemented for the first time, and no prior model exists that could help optimize the travel time. The method that the study envisaged through RTSOR focuses on collecting real-time data on an HDC to elucidate our claim of originality in reducing the delay time on roads by signal synchronization. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Travel time

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The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-32页

作者： Rifat, Mohammad Rashidujjaman Das, Dipto Podder, Arpon Jannat, Mahiratul Soden, Robert Semaan, Bryan Ahmed, Syed Ishtiaque Department of Computer Science University of Toronto TorontoON Canada Khulna University of Engineering & Technology Khulna Bangladesh Department of Women and Gender Studies University of Dhaka Dhaka Bangladesh Computer Science and School of the Environment University of Toronto TorontoON Canada Department of Information Science University of Colorado Boulder BoulderCO United States

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW’s literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication. © 2024 Copyright held by the owner/author(s).

关键词： Social psychology

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