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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Investigating Writer-Independent Deep Learning Techniques for Offline Handwritten Signature Verification 1

Investigating Writer-Independent Deep Learning Techniques fo...

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

作者： Vamsikrishna, M. Bogireddy, Srinivasa Rao Gangopadhyay, Amit Mishra, Nilamadhab Sundaram, Ajith Sharma, Priya Aditya University Dept. of Computer Applications Kakinada India Horizon Systems Inc Phoenix Sr Software Engineer AZ United States Mohan Babu University Dept. of ECE Andhrapradesh Tirupati India VIT Bhopal University Dept. of CSE Sehore India Amrita Vishwa Vidyapeetham Dept. of Business Kochi India Techno India University Dept of EEC Kolkata India

ISBN: (纸本)9798350356816

Research proposes a deep learning-based approach for writer-independent authentication of handwritten signatures. Though handwritten signatures prove to be one of the most popular methods for biometric authentication, verification of the same is quite challenging because of the high variability in the writing styles across subjects. This paper proposes a new way of extracting relevant features from signature images and learning writer-independent representations of signatures through Convolutional Neural Networks. We evaluate our approach on the publicly available dataset of CIDER and demonstrate that such an approach outperforms the state-of-the-art techniques in both aspects: accuracy and robustness against different forgeries. Our results imply a rather promising approach towards practical applications, as a CNN-based system will lead to a very satisfying solution for signature verification. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements in Jute Leaf Disease Detection: A Comprehensive Study Utilizing Machine Learning and Deep Learning Techniques 2

Advancements in Jute Leaf Disease Detection: A Comprehensive...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Haque, Rezaul Miah, Md Miraz Sultana, Shayma Fardin, Hasib Noman, Abdullah Al Al-Sakib, Abdullah Hasan, Md Kamrul Rafy, Al Shihabur, Rahman Md Rahman, Shafiur East West University Dept. of Cse Dhaka1212 Bangladesh Brac University Dept. of Cse Dhaka1212 Bangladesh International American University Dept. of Management Information System CA90010 United States Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University Dept. of Information Technology CA92614 United States Touro University Dept. of Cyber Security CA94592 United States Kyungdong University Dept. of Computer Engineering Gangwon-do Korea Republic of Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331517984

Detecting diseases in jute leaves is difficult due to the variability in how the diseases appear. Manually identifying these diseases is challenging because it requires expert knowledge and visual inspections take a lot of time. Machine Learning (ML) offers a promising solution to these challenges by automating the detection process. However, research in this area is limited due to the lack of specific datasets. This study aims to address this by creating a comprehensive dataset of 10,800 high-quality images of jute leaf diseases. The main goal is to develop a robust system for classifying jute leaves into three categories: Yellow Mosaic, Powdery Mildew, and Healthy. Our methodology involved extensive image preprocessing, including resizing and various augmentation techniques, to enhance the dataset's diversity and ensure model robustness. We trained various ML and Deep Learning (DL) models and conducted a comparative analysis of their performance. Additionally, we compared our approach with the state-of-the-art methods. The results showed that DL models, particularly Inception V3, achieved an outstanding accuracy of 99.98%, compared to 89.75% for Random Forest (RF). This highlights the potential of DL techniques in improving the accuracy of jute leaf disease detection. Our findings contribute to better disease management strategies and increased productivity in jute cultivation. © 2024 IEEE.

关键词： Adversarial machine learning

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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A Preliminary Result of Food Object Detection using Swin Transformer 22

A Preliminary Result of Food Object Detection using Swin Tra...

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8th International Conference on computer Technology Applications, ICCTA 2022

作者： Jung, Daeil Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Dept. of Computer Science Dankook University Korea Republic of Dept. of Computer Engineering San Jose State University United States Dept. of Electrical Engineering San Jose State University United States Dept. of Software Science Dankook University Korea Republic of Dept. of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

An inappropriate diet is one of the main causes of poor health. However, it is difficult to sustain a quantitative diet assessment in the general living environment. Food object detection is a key method for solving this problem;still, it is difficult to find studies that apply recent object detection techniques. In addition, the currently used high-performance food object detection models have a special architecture that combines two deep learning models-food localization and food classification-in series to achieve high accuracy. The disadvantage of this architecture is that it is difficult to predict the scalability of a model. In this study, we built an end-to-end food object detection model using the Swin Transformer, which is one of the latest backbone models. The experiment was conducted to compare the performance of the UECFOOD dataset with other food object detection studies. For the UECFOOD-100 dataset, a mAP(mean Average Precision) of 0.522 was obtained;also, a mAP of 0.52 was obtained for the UECFOOD-256 dataset. The findings show that the proposed model that uses only end-to-end object detection produces better performance than previous studies using a combination of food localization and food classification. © 2022 ACM.

关键词： Object detection

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Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review 3

Frameworks, Applications and Challenges in Streaming Big Dat...

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3rd International Conference on Innovations in computer Science and software engineering, ICONICS 2022

作者： Nabeel Mustafa, Syed Muhammd Umer Farooque, Muhammad Tahir, Muhammad Khan, Shariq Mahmood Qamar, Rohail NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Sir Syed University of Engineering and Karachi Dept. of Software Engineering Pakistan

ISBN: (纸本)9781665461412

Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term "streaming Big data"refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-Time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-Time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered. © 2022 IEEE.

关键词： Big data

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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Design of a Smart and Green Industry 4.0 Architecture

Design of a Smart and Green Industry 4.0 Architecture

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Information Technology (ACIT)

作者： Khaled Hamdan Okba Kazar Dept. of Computer Science and Software Engineering College of IT UAEU UAE Dept. of Information Systems & Security College of IT UAEU UAE

The industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. This fourth industrial era gives birth to a new generation of factory called “Digital Factory”, “Industry 4.0”. This industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. The commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. This is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. The goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). The motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation.

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