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International Conference on computer and Information Technology (ICCIT)

作者： Zinnia Sultana Md Saiful Islam Farhina Akter Sabikun Naher Nova Department of Computer Science and Engineering International Islamic University Chittagong Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

telecommunication networks, including the internet and various communication protocols, have created a global web of connectivity. Several telecommunication companies have emerged in Bangladesh. They are competing with each other to provide their users with superior experiences. This encompasses the interchange of information, concepts, preferences, and other modes of communication. With millions of customers interacting with these companies daily through various platforms, such as social media, surveys, customer support, and online reviews, gathering and going through millions of feedback would take a lot of time and work. The purpose of this study is to analyze the customer feedback on mobile telecommunication companies in Bangladesh using sentiment analysis. Due to the lack of labeled data, a large number of multilingual comments were extracted from several verified Facebook pages of telecommunication operators in Bangladesh and categorized to utilize. To evaluate the sentiment of the multilingual comments, machine learning algorithm, Support Vector Machines (SVM) was integrated. This study also included Long Short-Term Memory network (LSTM) and Bidirectional LSTM (BiLSTM) to tackle this discrepancy by harnessing the advanced capabilities of deep learning models. Subsequently, the performance of the models was assessed by obtaining potentially relevant measures, with the experimental results revealing the accuracy levels achieved across all algorithms and demonstrating their relative informativeness. This sentiment analysis will help telecommunication operators to gain a comprehensive understanding of client attitudes regarding new initiatives, products and offers, enabling companies to refine their services and remain competitive in this rapidly evolving and customer-centric sector.

关键词： Support vector machines Deep learning Sentiment analysis Social networking (online) Reviews Customer satisfaction Bidirectional long short term memory Companies Communications technology Multilingual

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty 6

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023

作者： Andriadi, Kus Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Yogyakarta55281 Indonesia

ISBN: (纸本)9798350358346

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large. © 2023 IEEE.

关键词： Generative adversarial networks

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification 1

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kumar, Vinod Jain, Isha Dongla, Durga Sritha Rai, Shivangi Rastogi, Vani Kaushik, Eva Kaushik, Rohit George Washington University School of Engineering and Applied Science WashingtonDC United States Dr. Akhilesh Das Gupta Institute of Technology and Management Computer Science and Engineering Department New Delhi India Gandhi Institute of Technology and Management Computer Science and Engineering Department Andhra Pradesh Visakhapatnam India Data Scientist DXC Technology Noida India Data Analytics Program University of Illinois CS and Mathematics Department Springfield United States

ISBN: (纸本)9798350356816

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Hybrid Framework for Energy-Efficient Clustering and Routing in Heterogeneous IoT-Driven Wireless Sensor Networks 7

A Novel Hybrid Framework for Energy-Efficient Clustering and...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gupta, Gopal Kumar Jain, Arpit Sharma, Khemraj Ashok Kumar, P.M. Gupta, Shubhi Agarwal, Sheenu Nagpur Campus Pune440008 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Nanhua University Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Uttarakhand Technical University Department of Electronics and Communication Engineering U.K. Dehradun India

ISBN: (纸本)9798350350067

Smart computing has been particularly notable in the development of wireless sensor networks (WSNs), which have many applications. Battery-powered, self-configuring sensor nodes are the basis of these networks. Energy and resources are limited when it comes to sensors. Unbalanced nodes in the network consume more power, which adversely affects the network's lifespan. An improved energy-efficient cluster-based routing protocol for heterogeneous wireless sensor networks (WSNs) based on the Internet of Things is proposed in this study. Several performance metrics were used to evaluate the proposed model's effectiveness, including energy efficiency, alive nodes, dead nodes, network lifetime, and residual energy. The results of the comparison were compared with existing methodologies, such as LEACH, PSO, and hybrid PSO. The proposed approach outperforms the existing model by a substantial margin in networks with 50 and 100 nodes, according to the simulation results. © 2024 IEEE.

关键词： Sensor nodes

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A Comparative Selection of Best Activation Pair Layer in Convolution Neural Network for Sentence Classification using Deep Learning Model 7

A Comparative Selection of Best Activation Pair Layer in Con...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Nahar, Lutfun Zabu, Zinat Ara Raihan, Ahamed Islam, Md. Inzamamul Emon, Md. Ismail Siddiqi Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali Bangladesh Feni University Department of Computer Science and Engineering Feni Bangladesh Morgan State University Department of Physics BaltimoreMD United States Brac University 66 Mohakhali Department of Electrical and Electronics Engineering Dhaka Bangladesh

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Many natural language processing jobs need sentiment classification of text content. There is an urgent need, particularly with the rise of social media, to extract meaningful information from vast volumes of data upon that Internet utilizing sentiment analysis. We're interested in adopting deep learning models to handle sentiment classification because of the advances that have been made. We present a framework named fastText along with Convolutional Neural Network in this study (CNN). To commence, we use fastText to create words vector representations that will be fed into the CNN. FastText's purpose is to generate a generative model of a word as well as reflect word distance. This one will enable the parameters to also be established at an advantageous CNN point, which will help neural nets to perform much better in this circumstance. Second, we create a CNN architecture that is appropriate for sentiment analysis. We use two sets of convolutional layers along with pooling layers in this design. This is the first time, to our knowledge, that a 9-layer architecture and design model is developed based on fastText as well as CNN was used to assess the sentim ent of phrases. We use the Rectified Linear Unit (ReLU), Normalization, and Dropout techniques to improve the accuracy and generalizability of our model. We put our methodology to the test on a publicly available dataset of movie review extracts with five labels: negative, slightly negative, neutral, moderately positive, and positive. In this dataset, ourReLU pairwise network obtains a test accuracy of 96.4 percent, outperforming existing neural network models. © 2022 IEEE.

关键词： Convolutional neural networks

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3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

3D Image Generation from X-ray Projections Using Generative ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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Regulation of regeneration in Arabidopsis thaliana

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aBIOTECH 2023年第4期4卷 332-351页

作者： Md Khairul Islam Sai Teja Mummadi Sanzhen Liu Hairong Wei Computational Science and Engineering Program Michigan Technological UniversityHoughtonMI 49931USA Computer Science Michigan Technological UniversityHoughtonMI 49931USA Department of Plant Pathology Kansas State UniversityManhattanKS 66506USA College of Forest Resources and Environmental Science Michigan Technological UniversityHoughtonMI 49931USA

We employed several algorithms with high efficacy to analyze the public transcriptomic data,aiming to identify key transcription factors(TFs)that regulate regeneration in Arabidopsis ***,we utilized CollaborativeNet,also known as TF-Cluster,to construct a collaborative network of all TFs,which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder(CoSE)*** analysis of these subnetworks led to the identification of nine subnetworks closely associated with *** further applied principal component analysis and gene ontology(GO)enrichment analysis to reduce the subnetworks from nine to three,namely subnetworks 1,12,and *** for TF-binding sites in the promoters of the co-expressed and co-regulated(CCGs)genes of all TFs in these three subnetworks and Triple-Gene Mutual Interaction analysis of TFs in these three subnetworks with the CCGs involved in regeneration enabled us to rank the TFs in each ***,six potential candidate TFs-WOx9A,LEC2,PGA37,WIP5,PEI1,and AIL1 from subnetwork 1-were identified,and their roles in somatic embryogenesis(GO:0010262)and regeneration(GO:0031099)were discussed,so were the TFs in Subnetwork 12 and 17 associated with *** TFs identified were also assessed using the CIS-BP database and Expression *** analyses suggest some novel TFs that may have regulatory roles in regeneration and embryogenesis and provide valuable data and insights into the regulatory mechanisms related to *** tools and the procedures used here are instrumental for analyzing high-throughput transcriptomic data and advancing our understanding of the regulation of various biological processes of interest.

关键词： Regeneration Pluripotency Transcription factor Collaborative network

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SEMU5G: an Emulator for Improving Model Accuracy and Performance Evaluation of 5G Network Core Architectures based on SDN 10

SEMU5G: an Emulator for Improving Model Accuracy and Perform...

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10th International Conference on Wireless and Telematics, ICWT 2024

作者： Zaki Hamidi, Eki Ahmad Lazuardi, Arga Valen Rahmansyah, Muhammad Litfan Novel Bajri, Salman Alfarizi Tulloh, Rohmat Negara, Ridha Muldina Ahdan, Syaiful Jupriyadi UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia Telkom University Telecommunication Technology School of Applied Science Bandung Indonesia The University Center of Excellence Intelligent Sensing-IoT Telkom University Indonesia Telkom University School of Electrical Engineering Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia

ISBN: (纸本)9798350377057

The rapid advancement of 5G Radio Access Network (RAN) architecture is facilitating the construction of 5G networks, marking a significant milestone in telecommunications evolution. Given the complexity of the 5G core architecture, traditional simulation methods are insufficient, necessitating novel approaches. Emulator systems are crucial for creating dynamic, controlled environments that enable exploration of real-world scenarios without physical constraints. SEMU5G, a 5G core emulator, SDN controller, and RAN simulator, utilizes Open5GS implemented in Docker Container for test system flexibility and isolation. Additionally, an SDN controller is integrated to monitor data flows in the User Plane Function (UPF) and gNB simulated by UERANSIM in Mininet-WiFi. This comprehensive integration facilitates effective and flexible real-world exploration, providing a dynamic and controlled test environment for 5G core research. Scenario testing comprises two stages: firstly, a fixed network topology is employed to compare resource usage and confirm successful SEMU5G integration. Secondly, a mobile network topology is utilized to implement a mobile device scenario and compare the Quality of Service (QoS) of SEMU5G with other available emulators. These stages ensure thorough evaluation of SEMU5G's performance and its comparative advantage over existing solutions. © 2024 IEEE.

关键词： Wi-Fi

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Exploring CST Simulation Techniques for TEM Mode and Unit Cell Analysis to Determine Refractive Index 12

Exploring CST Simulation Techniques for TEM Mode and Unit Ce...

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12th International Electrical engineering Congress, iEECON 2024

作者： Malathong, Kritthanit Konpang, Jessada Kongjareansuk, Sayree Akkaraekthalin, Prayoot Chudpooti, Nonchanutt Thitimahatthanakusol, Phatsakul Rajamangala University Of Technology Krungthep Faculty Of Engineering Department Of Electrical And Telecommunication Engineering 2 Nanglingee Rd. Sathorn Thungmahamek Bangkok10120 Thailand Rajamangala University Of Technology Krungthep Faculty Of Engineering Department Of Electrical And Telecommunication Engineering Bangkok Sathorn10120 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering 1518 Pracharat 1 Road Bangsue Bangkok10800 Thailand King Mongkut's University Of Technology North Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok10800 Thailand Rajamangala University Of Technology Isan Faculty Of Engineering And Technology Department Of Telecommunication Engineering 744 Sura Narai Rd Nai-muang Mueang Nakhon Ratchasima Nakorn Ratchasima30000 Thailand

ISBN: (纸本)9798350383591

The study investigates the accuracy of CST simulations in TEM mode and unit cell simulations for determining the refractive index (n) of materials. The results in TEM mode align with the expected properties of air, affirming the correctness of settings. However, when using unit cell simulations, the materials properties template inaccurately calculates n values, deviating from reality. Further examination reveals that unit cell simulations emit waves of different modes (TE, TM, or CP) instead of TEM, leading to inconsistencies. A revised equation is introduced to address this discrepancy, yielding results closer to actual n values. The importance of parameters like D and m is emphasized, as variations significantly impact the n values obtained. The introduced equation provides more accurate n cal-culations in unit cell simulations than the default CST template. © 2024 IEEE.

关键词： Refractive index

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Application of Machine Learning Algorithms to Path Loss Modeling: A Review 5

Application of Machine Learning Algorithms to Path Loss Mode...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Abdulkarim, Abubakar Faruk, Nasir Alozie, Emmanuel Sowande, Olugbenga. A. Olayinka, Imam-Fulani Yusuf Usman, Aliyu D. Adewole, Kayode S. Oloyede, Abdulkarim A. Chiroma, Haruna Garba, Salisu Imoize, Agbotiname Lucky Musa, Abdulwaheed Taura, Lawan S. Ahmadu Bello University Department of Electrical Engineering Nigeria Kampala International University Department of Electrical Telecommunications Computer Engineering Uganda Sule Lamido University Department of Physics Nigeria Directorate of ICT Sule Lamido University Nigeria University of Ilorin Department of Telecommunication Science Nigeria Ahmadu Bello University Department of Electronics and Telecommunication Engineering Nigeria University of Ilorin Department of Computer Science Nigeria College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Sule Lamido University Department of Computer Science Nigeria University of Lagos Department of Electrical and Electronics Engineering Nigeria Kwara State University Department of Electrical and Computer Engineering Nigeria

ISBN: (纸本)9781509064229

The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions. © 2022 IEEE.

关键词： 5G mobile communication systems

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