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information Technology for Education and Development (ITED)

作者： Juliana Ngozi Ndunagu Rasheed Gbenga Jimoh Ugwuegbulam Chidiebere George Deborah. Opeoluwa Department of Computer Science National Open University of Nigeria Abuja Nigeria Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Management Information System ACETEL – National Open University of Nigeria Abuja Nigeria

The data gleaned from the National Open University of Nigeria's (NOUN) E-ticketing system was studied in this paper. NOUN is one of the Open and Distance Learning (ODL) institutions, where students and their facilitators are in different physical locations. Multinomial Naive Bayes algorithm, preferred using “intent” for its classification method for the chatbot system. Within 4 months of the launch of the NOUN E-ticketing system, 38,263 tickets (students' complaints and inquiries) were generated and 30,601 have been manually responded to and closed while the remaining 7,662 tickets are still in progress. The chatbot's goal is to respond to students inquiries quickly and efficiently while easing the burden on the management system. With the availability of chatbot, students' responses will be automated and accessible 24/7. The NOUN chatbot will increase student engagement, strengthen communication and create a seamless interaction for both the ODL institutions and its students all together culminating to a robust congenial student-ODL relationship ultimately leading to a higher attraction rate and more importantly, a lower attrition rate, not just in ODL institutions alone, but to other conventional higher institutions.

关键词： computer aided instruction Technological innovation Social networking (online) Education multimedia Web sites Data warehouses Chatbots

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Multi-Representation Convolutional Neural Network to Recognize Human Emotion

Multi-Representation Convolutional Neural Network to Recogni...

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information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Dwi Wahyu Prabowo Noor Akhmad Setiawan Johan Debayle Hanung Adi Nugroho Department of Electrical and Information Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Department of Information System Faculty of Computer Science Universitas Darwan Ali Sampit Indonesia MINES Saint-Etienne CNRS UMR 5307 LGF Centre SPIN Saint-Etienne France

Emotion recognition using brain-computer interfaces (BCIs) is an emerging field with the ongoing challenge of developing robust and efficient classification methods. This study introduces an original approach, the multi-representation convolutional neural network (MrCNN), designed to exploit the complementary features of recurrence plots (RP) and spectrograms. The efficacy of this new method was empirically evaluated and compared with the standalone RP and spectrogram methods in emotion classification tasks using electroencephalogram (EEG) signals. Two widely used datasets, DEAP and SEED, were used as the experimental platforms for this study. Employing a multifaceted analytical approach involving descriptive statistics, data visualization, and hypothesis tests, this study revealed that MrCNN significantly outperforms the standalone RP and spectrogram methods in terms of key performance metrics. Specifically, MrCNN achieved an accuracy of 0.9663, sensitivity of 0.9848, specificity of 0.9825, kappa score of 0.9511, and F1 score of 0.9656 for the DEAP dataset. For the SEED dataset, the accuracy was 0.9695, with sensitivity, specificity, kappa scores, and F1 score of 0.9844, 0.9842, 0.9543, and 0.9695, respectively. These results were statistically validated, and a comparative analysis with existing literature substantiated the robustness and superiority of the MrCNN method. This study sets new performance benchmarks and offers a reliable and effective approach for emotion classification using EEG signals.

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Effect of mono corpus quantity on statistical machine translation Indonesian-Lampung dialect of nyo 3

Effect of mono corpus quantity on statistical machine transl...

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3rd International Conference on Applied sciences Mathematics and Informatics, ICASMI 2020

作者： Abidin, Z. Permata Ahmad, I. Rusliyawati Department of Information System Faculty of Engineering and Computer Science University of Teknokrat Indonesia Jl. ZA Pagar Alam no 9-10 Bandar Lampung Indonesia Department of Informatics Faculty of Engineering and Computer Science University of Teknokrat Indonesia Jl. ZA Pagar Alam no 9-10 Bandar Lampung Indonesia

Lampung Province is located on the island of Sumatera. For the immigrants in Lampung, they have difficulty in communicating with the indigenous people of Lampung. As an alternative, both immigrants and the indigenous people of Lampung speak Indonesian. This research aims to build a language model from Indonesian language and a translation model from the Lampung language dialect of nyo, both models will be combined in a Moses decoder. This research focuses on observing the effect of adding mono corpus to the experimental statistical machine translation of Indonesian-Lampung dialect of nyo. This research uses 3000 pair parallel corpus in Indonesia language and Lampung language dialect of nyo as source language and uses 3000 mono corpus sentences in Lampung language dialect of nyo as target language. The results showed that the accuracy value in bilingual evalution under-study score when using 1000 sentences, 2000 sentences, 3000 sentences mono corpus show the accuracy value of the bilingual evaluation under-study, respectively, namely 40.97 %, 41.80 % and 45.26 %. © Published under licence by IOP Publishing Ltd.

关键词： Computational linguistics

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Data collection in studies on internet of things (IoT), wireless sensor networks (WSNs), and sensor cloud (SC): Similarities and differences

TechRxiv

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TechRxiv 2021年

作者： Ihsan, A.L.I. Department of Computer System & Technology Faculty of Computer Science & Information Technology University of Malaya Malaysia

Data collection is an essential part of sensor devices, particularly in such technologies like Internet of Things (IoT), wireless sensor networks (WSN), and sensor cloud (SC). In recent years, various literature had been published in these research areas to propose different models, architectures, and contributions in the domains. Due to the importance of efficient data collection regarding reducing energy consumption, latency, network lifetime, and general cost, a momentous literature volume has been published to facilitate data collection. Hence, review studies have been conducted on data collection in these domains in isolation. However, a lack of comprehensive review collectively identifies and analyzes the differences and similarities among the data collection proposals in IoT, WSN, and SC. The main objective of this research is to conduct a comprehensive survey to explore the current state, use cases, contributions, performance measures, evaluation measures, and architecture in the IoT, WSN, and SC research domains. The findings indicate that studies on data collection in IoT, WSN, and SC are relatively consistent with stable output in the last five years. Nine novel contributions are found with models, algorithms, and frameworks being the most utilized by the selected studies. In conclusion, key research challenges and future research directions have been identified and discussed. © 2021, CC BY.

关键词： Wireless sensor networks

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Social Media Multi-Emotion Detection 2

Social Media Multi-Emotion Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 IEEE.

关键词： Tweets

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Discoffery: a Coffee Types Detection Apps using an EfficienNet-Lite Architecture 22

Discoffery: a Coffee Types Detection Apps using an EfficienN...

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Proceedings of the 7th International Conference on Sustainable information Engineering and Technology

作者： Satrio Arif Budiman Andi Pramono Bayu Priyambadha Annisa Maharani Arya Putri Agung Purnomo Ilham Setyo Nugroho Information System Computer Science Faculty University of Brawijaya Indonesia Interior Design Department School of Design Bina Nusantara University Indonesia Department of Informatics Computer Science Faculty University of Miyazaki Japan & Brawijaya University Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Brawijaya University Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia

ISBN: (纸本)9781450397117

According to data from the World Food and Agriculture Organization (FAO), Indonesia produced the fourth-most coffee in the world in 2017 and 2018. Gayo, Robusta Dampit, and Toraja coffees are only a few well-known coffee varieties Indonesian growers produce. This research aims to create an app that can identify the type of coffee and serve as a coffee-related educational tool. A single case study was the research methodology used. By employing the EfficienNet-Lite architecture for transfer learning, a model for categorizing coffee beans is created. Users can get information through the photographs they submit with the help of the type of application development that uses deep learning to do categorization based on image data of coffee bean types. The coffee bean classification feature was built using transfer learning with the EfficientNet architecture. A training accuracy of 87% and a validation accuracy of 81% were achieved using the EfficientNet-Lite 0 architecture.

关键词： Smartphone EfficienNet-Lite Transfer Learning Coffee Beans Classification Deep Learning

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Combined Envelope Scaling with Modified SLM Method for PAPR Reduction in OFDM-Based VLC systems

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Journal of Optical Communications 2023年第s1期44卷 S1175-S1180页

作者： Touhami, Ridha Slimani, Djamal Abdulkafi, Ayad Atiyah Hussein, Yaseein Soubhi Alias, Mohamad Yusoff University of Ferhat Abbas Setif 1 Setif19000 Algeria Tikrit University Salahadin Tikrit43001 Iraq Department of Information Technology & Computer Science Ahmed Bin Mohmmed College Doha Qatar Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia

Orthogonal frequency-division multiplexing technique (OFDM) has been adopted widely as a modulation technique for radio frequency (RF) and optical communication systems such as visible-light communication (VLC) due to its high spectral efficiency and low-complexity implementation. VLC-OFDM is recommended in 5 G mobile communication. However, VLC-OFDM suffer from the high peak to-average power ratio (PAPR). In this paper, a modified selective mapping (MSLM) method is applied to the proposed system followed by a new envelope scaling process for further reductions in PAPR of VLC-OFDM system. Simulation results show that the proposed method reduces the PAPR by about 6.8 and 1.7 dB comparing with the original signal and the traditional SLM with a number of rotation vector U = 8, respectively. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Peak to average power ratio

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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A Mobile Cloud Architecture for M-Commerce Business Application’s Execution Performance

A Mobile Cloud Architecture for M-Commerce Business Applicat...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Sadaf Yasmin Swati Mishra Asharul Islam Rashid Hussain Faculty of Management Suresh Gyan Vihar University Jaipur India Department of Information System College of Computer Science King Khalid University Abha K.S.A Moti Babu Instute of Technology Bihar Engineering University Bihar India

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Many researchers have come up with different ways to build a business model architecture for efficient services to run m-commerce applications. The framework for developing mobile commerce applications needs to change at the same time as the IT expansion and business transformation. We are pursuing a theoretical business framework to find an optimal solution for the seamless service-oriented m-commerce application performance. In this approach, we identify the components of m -commerce applications that could reduce mobility barriers and improve application execution time and device energy efficiency. This led us to propose a framework for m-commerce application performance that uses the Mobile Cloud Computing (MCC) solutions. This plan focuses on making the application run faster and use less energy, and making it easier for users to move around. We help mobile commerce application developers make smart RDMCA (Resource-Demanding M-commerce Applications) that provide good services for mobile commerce businesses and consumers. A theoretical proposed business framework is being set up to find an optimal solution for service-oriented m -commerce application performance. In this way, we find the important parts of the m -commerce system (signal range determiner) that help make the application run faster, use less energy, and make it easier to move around. We think that improving this framework will help mobile commerce app developers to make RDMCA’s that provide good services for both mobile commerce enterprises and end users.

关键词： Performance evaluation Cloud computing Energy consumption Image processing computer architecture Energy efficiency Computational efficiency

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A Cloud-Based Offloading Process for MCommerce Application’s Performance in Cloud Environment

A Cloud-Based Offloading Process for MCommerce Application’...

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International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

An interest in cloud computing is transforming and developing information technology continuously for business and personal needs. Most e-commerce companies are migrating and converting services and applications for the rich resources provided through cloud platforms. This is a new business era that helps change the cloud. A process, state, or concept is defined as transformation. Through a dynamic process, it shows the technological transformation as the world changes and adapts to external and internal forces. Companies invest their capital in developing IT infrastructure, maintaining, and updating hardware, and software, and improving their services. The cloud-based transformation is an excellent method for establishing a fresh service-oriented, value-effective, adaptable, and adaptable technologies. Switching from the IT infrastructure to services as a pay-per-use business model is done using the IT service model. The latest trends in cloud computing technology and next-generation models are rented. Moreover, m-commerce applications are based on mobile execution performance, which is the major concern of e-commerce industries. As we know, there are three major obstacles of mobile devices natures: (1) Resource constraint device (2) Client mobility and (3) High energy consumption, moreover this is the major m-commerce applications performance challenges. When doing complicated computer work, the device battery will run out quickly and need to be charged again and again. We want to provide an overview of the concept, process, and research area of offloading computation for the energy efficiency of smartphones. The issues and challenges in computing offloading for mobile cloud environments are discussed.

关键词： Performance evaluation Cloud computing Adaptation models Computational modeling Companies Switches Software

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