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computer Applications & Technological Solutions (CATS)

作者： Kefaiat Lamia Ehsani Ehsanur Rahman Rhythm Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

A computer programme that imitates and processes human interaction, either through the use of voice or text communication, is known as a chatbot. Its purpose is to be of assistance in the process of finding a solution to a problem. The transformation brought on by advances in technology has had an effect on every industry. The chatbot provides assistance with a wide variety of tasks, including Reservations, Customer Service, and a great number of other services. The fast development of technologies relating to artificial intelligence and natural language processing has resulted in an increase in the use of chatbots in a variety of fields, most notably in the field of customer service. Customers could receive advice that is prompt, accurate, and personalised through the use of chatbots, which has the potential to completely transform customer service. Because it can automate customer service and reduce the amount of work that needs to be done by humans, it has gained a lot of popularity in the business world. Which can help businesses improve the experience they provide for their customers. The purpose of this research is to undertake a comparative review of customer service chatbots, with a particular emphasis on their efficiency, usability, and application across a variety of business sectors. The research will uncover best practises, difficulties, and potential for improvement by analysing a variety of chatbot solutions.

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Assessing the Validity of Automated data Analysis Methods Based on Artificial Intelligence 3

Assessing the Validity of Automated Data Analysis Methods Ba...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Yuvaraj, K. Balakumar, P. Kadam, Prema S. Mishra, Prakash Raja Kumar, Jambi Ratna Verma, Anupama Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Jabalpur India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India

ISBN: (纸本)9798350319125

Automated records analysis strategies primarily based on artificial intelligence (AI) have become increasingly famous in recent years because of their capability to quickly and effectively method enormous quantities of facts. However, concerns about the validity of those methods and their potential for introducing bias or mistakes have been raised. This study aims to assess the validity of automatic facts evaluation methods based on AI. It entails evaluating those methods' accuracy, reliability, and consistency compared to conventional guide evaluation methods. An expansion of study methods may be used, which includes experimental research, case research, and surveys. Facts will be gathered from computerized AI techniques and manual strategies, after which they will be compared and evaluated to decide the extent of agreement between them. One capability mission in this research is the dearth of a gold standard for statistics analysis. Unlike in medical fields wherein there may be a clean popular for prognosis, information analysis methods can also vary depending on the unique dataset and research question. Expanding a hard and fast of criteria or metrics for evaluating the validity of different AI techniques is essential. Another challenge is the rapidly evolving nature of the AI era. As new techniques and algorithms constantly evolve, keeping up with modern-day improvements and their potential impact on records analysis could be challenging. © 2023 IEEE.

关键词： evolved mission modern potential research

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TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment 29th

TX-Phase: Secure Phasing of Private Genomes in a Trusted ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dokmai, Natnatee Zhu, Kaiyuan Sahinalp, S. Cenk Cho, Hyunghoon Department of Biomedical Informatics and Data Science Yale School of Medicine New HavenCT United States Department of Computer Science and Engineering UC San Diego La JollaCA United States Cancer Data Science Laboratory National Cancer Institute National Institutes of Health BethesdaMD United States Department of Computer Science Yale University New HavenCT United States

ISBN: (纸本)9783031902512

Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns. Although privacy-preserving analysis tools have been developed to broaden access to these servers, none of the existing methods support haplotype phasing, a critical component of the imputation workflow. The complexity of phasing algorithms poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of Trusted Execution Environments (TEEs). TX-Phase allows users’ private genomic data to be phased while ensuring data confidentiality and integrity of the computation. We introduce novel data-oblivious algorithmic techniques based on compressed reference panels and dynamic fixed-point arithmetic that comprehensively mitigate side-channel leakages in TEEs to provide robust protection of users’ genomic data throughout the analysis. Our experiments on a range of datasets from the UK Biobank and Haplotype Reference Consortium demonstrate the state-of-the-art phasing accuracy and practical runtimes of TX-Phase. Our work enables secure phasing of private genomes, opening access to large reference genomic datasets for a broader scientific community. Our open-source software is available at https://***/hcholab/txphase. The full manuscript is available at https://***/10.1101/2024.09.16.613301. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sensitive data

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Sentiment Analysis of Amazon Reviews Using Machine Learning Classifier

Sentiment Analysis of Amazon Reviews Using Machine Learning ...

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International Conference on computer and Information Technology (ICCIT)

作者： Razin Sumyta Monsoor Tania Sultana Tamanna Salequzzaman Khan Shehrin Hoque Mahdi Islam Ehsanur Rahman Rhythm Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Reviews can significantly impact a company’s reputation in the market, potentially influencing its overall business outcomes, either positively or negatively. This is especially crucial for companies that operate primarily through e-commerce platforms. Hence, it is vital for companies to pay close attention to customer reviews. Sentiment Analysis, often referred to as "opinion mining," is a significant procedure in Natural Language Processing (NLP) which serves the purpose of ascertaining the emotional tone of a provided text and categorizing it into positive, negative, or neutral perspectives. In this paper, sentiment analysis methodology is presented for classifying Amazon reviews which utilizes a large dataset of reviews and employs Multinomial Naïve Bayesian (MNB), Support Vector Machine (SVM), Maximum Entropy (ME), and Logistic Regression as the primary classifiers by the authors. With the aid of machine learning, we employed a supervised learning approach to an extensive Amazon dataset in order to categorize it based on sentiment polarity, achieving a high level of accuracy for the results. Here, we utilized the Kaggle dataset that includes a substantial volume of reviews and associated metadata which comprises customer reviews and ratings on Amazon products.

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Skin Lesion Detection and Classification Using Machine Learning: A Comprehensive Approach for Accurate Diagnosis and Treatment

Skin Lesion Detection and Classification Using Machine Learn...

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International Conference on computer and Information Technology (ICCIT)

作者： H M Layes Delower Tasin Mohammad Shifath Jahan Prity Maliha Binta Islam Mohammod Tahseen Mansoor Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Cutaneous abnormalities, commonly known as skin lesions, form a broad spectrum of skin irregularities that necessitates proper identification and immediate treatment. A significant development in the utilization of machine learning approaches for analyzing medical imagery has been observed recently - particularly its effectiveness in the automatic detection and categorization of skin lesions. This academic study discusses an extensive technique for recognizing and categorizing skin lesions using machine learning protocols. The key cornerstone is the HAM10000 dataset, which comprises 10,000 images portraying discolored skin conditions varying in types and patient demographics. Our analysis examines the efficiency of Decision Trees, Support Vector Machines (SVMs), Random Forests, and K-Nearest Neighbors (KNN) in detecting and classifying such tensions on the dermis. Stringent evaluation procedures involving Accuracy, Precision, Recall rate, along with F1-score have been employed to measure these algorithms’ efficacy alongside their possible influence within clinical settings. Overall model performance was strong, with Support Vector Machines (SVMs) acquiring the highest accuracy of 94.8%, while Decision Tree gave an accuracy of 94.3%, Random Forest coming to a close third with an accuracy of 94.1%, and finally K-Nearest Neighbors (KNN), which gave us an accuracy of 93.7%. The presented methodology contributes meaningfully to progress in dermatology by generating precise diagnostic instruments that are beneficial for both healthcare professionals as well as patients suffering from these anomalies. This inquiry underscores how machine learning could elevate health outcomes by improving early recognition processes and enabling personalized therapeutics directed at treating skin lesions effectively.

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Text-based Q&A: Automated Question Generation and Answering for Enhanced data Processing

Text-based Q&A: Automated Question Generation and Answering ...

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computer Applications & Technological Solutions (CATS)

作者： Ehsanur Rahman Rhythm Abdul Halim Hosain Nusrat Zaman Raya Kazi Al Refat Pranta Tonusree Talukder Trina Md Sabbir Hossain Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bengalidesh

In the age of information overload, where the world is getting increasingly digital, traditional methods of learning are becoming tedious and extensively outdated. Imagine a system with just a few clicks that can quickly generate perplexing questions and enlightening solutions from a given text. Likewise, this paper represents a groundbreaking system that uses stateof-the-art of natural language processing techniques to analyze subject-specific chapters to create questions and corresponding solutions of varying lengths. The system’s versatility as an ideal tool for a wide range of users, including students, researchers, and educators, is a result of its capability to handle a wide range of domains. By offering questions of insight along with appropriate answers, this aforementioned structure demonstrated its extraordinary accuracy and competency in our studies on an array of informational datasets. Altering the learning process and promoting knowledge discovery, this program is flexible in delivering brief or comprehensible solutions to inquiries, having the potential to completely change how individuals interact with written material, whether they are reading for short reference or conducting any depth research. Our suggested framework establishes a dynamic platform for immediate information that enables people to learn substantially more and comprehend any topic in depth. With this leading-edge method, bid goodbye to exhausting manual question generation and get ready to embrace a new era of seamless and fruitful learning with this cutting-edge system.

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Hashing-Based Multi-Modal Semantic Communication

Hashing-Based Multi-Modal Semantic Communication

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IEEE Conference on Wireless Communications and Networking

作者： Yibo Zhu Hongyu Gu Jiangtian Nie Jianhang Tang Jiangming Jin Yang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China TuSimple

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The advanced sixth-generation (6G) wireless network is considered as an indispensable part of the Metaverse, where a substantial volume of communication content is transmitted through multiple modalities, placing significant transmission loads on communication channels. In this paper, we propose a framework for multi-modal semantic communication using hashing-based semantic extraction approach to produce optimal binary signatures (hash codes). Instead of directly using coarse-grained feature fusion methods, we capture deep semantics in self-attention manner, achieving fine-grained multi-modal feature fusion thereby strengthening the representation ability of hash codes. To enhance adaptability in practical situations, we then design a modality-completion module to address missing modalities in data, accommodating scenarios with both single-modal and cross-modal data. We evaluate the proposed semantic extraction framework on two popular multi-modal datasets, comparing it with the latest hashing methods and then demonstrate the effectiveness in various channel conditions.

关键词： 6G mobile communication Codes Metaverse Wireless networks Semantics Communication channels Feature extraction

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VCTP: A Verifiable Credential-based Trust Propagation Protocol for Personal Issuers in Self-Sovereign Identity Platforms

arXiv

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arXiv 2023年

作者： Mukta, Rahma Teh, Rue C. Paik, Hye-Young Lu, Qinghua Kanhere, Salil S. School of Computer Science and Engineering UNSW Sydney Australia Data61 CSIRO Sydney Australia

system that facilitates secure credential issuance and verification without placing trust in any centralised authority. To bypass central trust, most SSI implementations place blockchain as a trusted mediator by placing credential transactions on-chain. Yet, existing SSI platforms face trust issues as all credential issuers in SSI are not supported with adequate trust. Current SSI solutions provide trust support to the officiated issuers (e.g., government agencies), who must follow a precise process to assess their credentials. However, there is no structured trust support for individuals of SSI who may attempt to issue a credential (e.g., letter of consent) in the context of business processes. Therefore, some risk-averse verifiers in the system may not accept the credentials from individual issuers to avoid carrying the cost of mishaps from potentially inadmissible credentials without reliance on a trusted agency. This paper proposes a trust propagation protocol that supports individual users to be trusted as verifiable issuers in the SSI platform by establishing a trust propagation credential template in the blockchain. Our approach utilises (i) the sanitizable signature scheme to propagate the required trust to an individual issuer, (ii) a voting mechanism to minimises the possibility of collusion. Our implementation demonstrates that the solution is both practical and performs well under varying system loads. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps

Sentiment Analysis of Restaurant Reviews from Bangladeshi Fo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Ehsanur Rahman Rhythm Rajvir Ahmed Shuvo Md Sabbir Hossain Md. Farhadul Islam Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

In this study, we conducted sentiment analysis on restaurant reviews from Bangladeshi food delivery apps using natural language processing techniques. Food delivery apps have become increasingly popular in Bangladesh, and understanding the sentiment of customer reviews can provide valuable insights for restaurant owners and food delivery app companies. In this research, we have created a dataset named “Bangladeshi Restaurant Reviews” by gathering customer reviews of restau-rants available on Foodpanda and Hungrynaki, which are two popular food delivery apps in Bangladesh. We used Robustly Optimized BERT Pretraining Approach (RoBERTa), AFINN, and DistilBERT, a distilled version of Bidirectional Encoder Repre-sentations from Transformers (BERT) to perform the sentiment analysis. Overall, this research paper highlights the importance of sentiment analysis in the food delivery industry and demonstrates the effectiveness of different models in performing this task. It also provides insights for businesses looking to use sentiment analysis to improve their services and products. The accuracy of the models evaluated, RoBERTa, AFINN, and DistilBERT, were 74%, 73 %, and 77 % respectively.

关键词： Industries Sentiment analysis Analytical models Computational modeling Bit error rate Pipelines Transformers

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information Research Institute.

关键词： Support vector machines

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