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11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Average Consensus over Directed Networks in Open Multi-Agent Systems with Acknowledgement Feedback 63

Average Consensus over Directed Networks in Open Multi-Agent...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Makridis, Evagoras Grammenos, Andreas Oliva, Gabriele Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis University of Cyprus School of Engineering Department of Electrical and Computer Engineering Aglantzia Nicosia2109 Cyprus University of Cambridge Department of Computer Science and Technology CambridgeCB2 1TN United Kingdom University Campus Bio-Medico of Rome Via Alvaro Del Portillo Department of Engineering Roma21-00128 Italy

ISBN: (纸本)9798350316339

In this paper, we address the distributed average consensus problem over directed networks in open multi-agent systems (OMAS), where the stability of the network is disrupted by frequent agent arrivals and departures, leading to a timevarying average consensus target. To tackle this challenge, we introduce a novel ratio consensus algorithm (OPENRC) based on acknowledgement feedback, designed to be robust to agent arrivals and departures, as well as to unbalanced directed network topologies. We demonstrate that when all active agents execute the OPENRC algorithm, the sum of their state variables remains constant during quiescent epochs when the network remains unchanged. By assuming eventual convergence during such quiescent periods following persistent variations in system composition and size, we prove the convergence of the OpenRC algorithm using column-stochasticity and masspreservation properties. Finally, we apply and evaluate our proposed algorithm in a simulated environment, where agents are departing from and arriving in the network to highlight its resilience against changes in the network size and topology. © 2024 IEEE.

关键词： Consensus algorithm

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Jointly Preserving Subject and Object Anonymity in Nearby Friends Querying for Curious Service Providers

Jointly Preserving Subject and Object Anonymity in Nearby Fr...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Abul, Osman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering TOBB University of Economics and Technology Ankara Ankara Turkey

ISBN: (纸本)9798400716928

This work studies the urban area location privacy preserving location updates and nearby friends (NF) querying for the centralized proximity based services (PBSs). The urban area constraint forces the user mobility to be on the city road network, modeled as a weighted directed graph. The location privacy model exploits location anonymity through subgraph cloaking. Two kinds of location privacy issues have been identified and handled in a unified framework, (i) Subject Anonymity on location updates, i.e., providing location anonymity of the location updater (against the curious PBS provider), and (ii) Object Anonymity on NF querying, i.e., providing location anonymity of friends in NF query responses (against the curious query issuer). In the former, the user simulates the PBS provider's belief (a subgraph) on his location whereabouts, and locally blocks anonymity violating location update requests which is understood by a simulated attack. In the latter, as the NF query processor, the PBS provider simulates the user's mutual beliefs (subgraphs) on friends' location whereabouts obtained through previous NF queries. By a simulated attack, some friends' existence are suppressed from the NF query responses. An experimental evaluation on a real dataset has been conducted. © 2024 ACM.

关键词： Location

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Implementation of Convolutional Neural Network with EfficientNet-B0 Architecture for Brain Tumor Classification 8

Implementation of Convolutional Neural Network with Efficien...

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8th International Conference on informatics and Computing, ICIC 2023

作者： Hadi, Vinsensius Haryo Bhaskoro Mutiara, Achmad Benny Refianti, Rina Gunadarma University Informatics Engineering Depok Indonesia Gunadarma University Computer Science and Information Technology Depok Indonesia

ISBN: (纸本)9798350342604

Brain tumor is a type of cancer that is difficult to detect in the early stages and can be a fatal disease. Early detection of brain tumors is crucial to increase the chances of successful treatment. The use of imaging technologies such as CT-Scan and MRI has helped in detecting brain tumors, but accurate and fast diagnosis remains a challenge. In this study, the Convolutional Neural Network (CNN) method was used as an alternative method to detect brain tumors faster and more accurately. Some CNN architectures used include VGGNet, InceptionResNet, and the latest architecture called EfficientNet-B0. This research aims to compare the performance of EfficientNet-B0 with other CNN architectures in classifying brain tumors on MRI images. The results of this study are expected to provide an understanding of the effectiveness of using EfficientNet-B0 compared to other architectures in classifying brain tumors on MRI images. The dataset used consists of 3264 images with glioma, normal, pituitary, and meningioma classes. Testing was conducted with several scenarios for epochs and testing against optimizers such as Adam and RMSProp. The results from testing the model using confusion matrix showed that in experiments with an epoch scenario of 25 and Optimizer obtained an average precision value reaching 98%, average recall reaching 98%, and average F1-Score reaching 98%. The implementation of the best-designed model in the form of a system successfully predicted types of glioma, normal, pituitary, and meningioma tumors. Additionally, the classification model will be implemented into a website using Python programming language along with Flask framework. © 2023 IEEE.

关键词： Python

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Unveiling Depression: Monitoring Daily Activity Changes for Anomalies 3rd

Unveiling Depression: Monitoring Daily Activity Changes for...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Shaker, Rana Ibrahim, Nada Abdennadher, Slim Media Engineering and Technology German University in Cairo New Cairo Egypt Informatics and Computer Science German International University New Cairo Egypt

ISBN: (纸本)9783031655210

According to the world health organisation (WHO), 1 in 26 people globally suffer from depression, many of which go undiagnosed. The daily actions of individuals may reflect the presence of depressive symptoms, yet these signs could be disregarded due to negative social perceptions or insufficient awareness, resulting in untreated symptoms. Efforts to enhance the identification of depressive symptoms propose utilizing data from mobile devices and wearables rather than relying solely on clinical assessments and self-report questionnaires like the patient health questionnaire (PHQ). However, current methods often concentrate on singular aspects, which may restrict their efficacy. This study introduces an innovative method for identifying depressive symptoms by analyzing various data collected from mobile devices, including information on location, sleep patterns, phone usage, and call durations. A discrete mobile application was developed to collect and analyze this data from participants, guaranteeing privacy and confidentiality. Additionally, both participant-generated data and a benchmark dataset were analyzed using multiple anomaly detection algorithms, allowing for robust insights into depressive symptom identification. Results decisively demonstrated a significant association: fewer visited locations correlated with heightened depression severity among both app users and dataset participants. This underscores the potential of multifaceted data analysis in mental health research and intervention strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Anomaly detection

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Adaptive 360° Video Streaming Over Wireless Communication Channels

Adaptive 360° Video Streaming Over Wireless Communication C...

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International Conference on Frontiers of Signal Processing (ICFSP)

作者： Ioanna Valiandi Marios S. Pattichis Efthyvoulos Kyriacou Andreas S. Panayides VIDEOMICS Group CYENS Centre of Excellence Nicosia Cyprus Department of Electrical and Computer Engineering University of New Mexico Albuquerque USA Department of Electrical Eng. Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798350353235

ISBN: (纸本)9798350353242

360° video streaming is one of the prevalent communication technologies for enhancing user experience and has thus seen widespread adoption in virtual and mixed reality applications. However, delivering content at scale while securing the quality of wirelessly communicated 360°videos in real-time poses significant challenges. 360°videos come in ultra-high definition, necessitate unprecedented bitrate demands and involve high encoding complexity. The time-varying nature of underlying wireless channels further introduces a destabilizing factor, calling for video systems to seamlessly adjust to varying bandwidth throughput to maintain adequate quality of service and experience. To address this issue, in this study, we have developed a multi-objective optimization framework for real-time video encoding adaptation. The objective is to optimize both video quality and encoding efficiency while minimizing the required bitrate, subject to real-time application constraints. To achieve this, we relied on generating (offline) precise forward prediction models of video quality, bitrate demands, and encoding time, that can be used to select the optimum encoding configuration in real-time. To validate our methods, we implemented an adaptive video encoding controller, and ran emulations employing actual network traces from 5G mobile video streaming scenarios, using the popular open-source x264 and x265 codecs for video encoding. A dataset of 4K omnidirectional videos at 30 frames per second was used.

关键词： Wireless communication Bit rate Quality of service Streaming media Predictive models Encoding Real-time systems Quality assessment Video recording Optimization

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A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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computer Systems Science & engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection *** optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold ***,these methods do not take into account the effect of sample size and its effect on improving CoR *** general,a large sample size results in more reliable detection,but takes longer sensing time and increases ***,the locally sensed sample size is an optimization ***,optimizing the local sample size for each cognitive user helps to improve CoR *** this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)*** the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： Cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

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