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Dost-Mental Health Assistant Chatbot

Dost-Mental Health Assistant Chatbot

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advances in science and technology (ICAST), international conference on

作者： Atulya M. Nayar Zulfa Attar Shabbir Kachwala Tanaya Biswas Sharmila K. Wagh Computer Engineering Savitribai Phule Pune University Pune India

ISBN: (纸本)9781665492645

Mental health is a topic that is still new and must be introduced to everyone with tremendous care and caution and slowly made mainstream, so people don't view it as taboo and get appropriate treatment from professionals. Until then, chatbots will play an integral part in mental health assistance, for citizens who can't afford expensive treatments. the main aim of this paper is to introduce a mental health assistant, Dost, a chatbot built on Rasa framework and deployed on Telegram. the aim is to understand the user's problems and proceed to suggest ways to improve the user's condition through daily, casual conversations. Chatbots will play an integral role in the future of healthcare, to make mental health facilities accessible to everyone, from students to senior citizens and offer 24/7 assistance in the absence of doctors.

关键词： Sentiment analysis Emotion recognition Medical treatment Mental health Oral communication Chatbots Distance measurement

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Revolutionize Infectious Prevention Using Artificial Intelligence and Deep Learning 8th

Revolutionize Infectious Prevention Using Artificial Intelli...

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8th international conference on advances in Computing and Data sciences, ICACDS 2024

作者： Verma, Dinesh Kumar Singh, Shweta Dubey, Shivendra Raghuwanshi, Kapil Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal India Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9783031709050

the study and treatment of human infection continues to be examined in relation to generative AI, deep learning, machine learning, and AI (artificial intelligence). We provide an overview of AI’s current and prospective uses, as well as how it relates to clinical infection control. Research studies, Clinical trial, and meta-analytic received precedence when screening 1617 the PubMed database findings. the review’s narrative format centres on investigations that employ clinically validated prospectively acquired data from the real world, as well as studies with transformative possibility, including unique pharmaceutical discovery and microbiome-based interventions. Clinical imaging analysis (e.g., tuberculosis of the lungs diagnosis), tools for clinical decision-support (e.g., antimicrobial recommending, sepsis prediction), digital culture plate reading, antimicrobial resistance profiling, and malaria diagnosis are a few areas where there is proof to support the medical value of artificial intelligence (AI) used for diagnostics in laboratories. To date, most studies have not included medical metrics or real-world validation. Comparability is hampered by substantial variation in research methodology and reports. there are a lot of practical and ethical concerns, such as bias risk and algorithm transparency. though the practical medical value of artificial intelligence (AI) tools for infections investigation and control seems to be much more modest, enthusiasm for the research and creation of these tools is certainly gaining momentum. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Development of an Arabic Sign Language Recognition System Utilizing Deep Convolutional Neural Network 5

Development of an Arabic Sign Language Recognition System Ut...

引用

5th IEEE international conference on advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Kale, Meenal R. Kaur, Chamandeep Kumar, Indrajeet Mahdi, Haider Sharif Uprikar, Vijay Sankari, V. Yeshwantrao Chavan College of Engineering Department of Humanities Nagpur India Jazan University Department of Computer Science & Information Technology Jizan Saudi Arabia Department of Cse Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun India Al-Farahidi University Department of Biomedical Engineering Baghdad Iraq Datta Meghe Institute of Management Studies Nagpur India K. Ramakrishnan College of Engineering Department of Ai&ds Trichy India

ISBN: (纸本)9798350356236

the ability to recognize sign language movements, especially Arabic Sign Language (ArSL), is necessary to increase the conversation accessibility for the those who are hard of hearing or deaf. However, there are a number of drawbacks to the current ASLR techniques, such as their dependency on manually created features, lack of datasets, and inefficiencies in real-time applications. this study suggests a novel method that utilize Deep Convolutional Neural Network (DCNN) model designed especially for ASLR applications to overcome these difficulties. the suggested DCNN overcomes the drawbacks of conventional handmade feature-based techniques by automatically extracting discriminative features from unprocessed sign language images using hierarchical feature learning. Preprocessing techniques such as normalization and scaling are applied to improve the 7,857 completely labeled images in the RGB Arabic Alphabet Sign Language (AASL) the data set, which is provided for ASLR model evaluation and training. the results of the study demonstrate how successful the recommended DCNN model is, as evidenced by its astounding 99% accuracy on the AASL dataset. Comparison with similar approaches like VGG-16, ResNet-18, and other Efficient Net based architecture, shows the accuracy to which the proposed method can classify Arabic sign language alphabets. the findings presented evidence for how the chosen architecture for DCNNs can significantly enhance ASLR improvement, open up opportunities for improving communication interaction with Deaf and hard-of-hearing people and their integration. In doing the proposed work, we use Python. © 2025 IEEE.

关键词： Convolutional neural networks

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DETECTING MALICIOUS PDF DOCUMENTS USING SEMI-SUPERVISED MACHINE LEARNING 17th

DETECTING MALICIOUS PDF DOCUMENTS USING SEMI-SUPERVISED MACH...

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17th IFIP WG 11.9 international conference on Digital Forensics

作者： Jiang, Jianguo Song, Nan Yu, Min Chow, Kam-Pui Li, Gang Liu, Chao Huang, Weiqing Cyber Security at the Institute of Information Engineering Chinese Academy of Sciences Beijing China Computer Science at the University of Hong Kong Hong Kong Information Technology at Deakin University Burwood Australia

ISBN: (纸本)9783030883812;9783030883805

Portable Document Format (PDF) documents are often used as carriers of malicious code that launch attacks or steal personal information. Traditional manual and supervised-learning-based detection methods rely heavily on labeled samples of malicious documents. But this is problematic because very few labeled malicious samples are available in real-world scenarios. this chapter presents a semi-supervised machine learning method for detecting malicious PDF documents. It extracts structural features as well as statistical features based on entropy sequences using the wavelet energy spectrum. A random sub-sampling strategy is employed to train multiple sub-classifiers. Each classifier is independent, which enhances the generalization capability during detection. the semi-supervised learning method enables labeled as well as unlabeled samples to be used to classify malicious and benign PDF documents. Experimental results demonstrate that the method yields an accuracy of 94% despite using training data with just 11% labeled malicious samples.

关键词： Malicious PDF documents machine learning semi-supervised learning

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REEF: A Framework for Collecting Real-World Vulnerabilities and Fixes

REEF: A Framework for Collecting Real-World Vulnerabilities ...

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IEEE international conference on Automated Software Engineering (ASE)

作者： Chaozheng Wang Zongjie Li Yun Pena Shuzheng Gao Sirong Chen Shuai Wang Cuiyun Gao Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Computer Science and Engineering Department The Chinese University of Hong Kong Hong Kong China

Software plays a crucial role in our daily lives, and therefore the quality and security of software systems have become increasingly important. However, vulnerabilities in software still pose a significant threat, as they can have serious consequences. Recent advances in automated program repair have sought to automatically detect and fix bugs using data-driven techniques. Sophisticated deep learning methods have been applied to this area and have achieved promising results. However, existing benchmarks for training and evaluating these techniques remain limited, as they tend to focus on a single programming language and have relatively small datasets. Moreover, many benchmarks tend to be outdated and lack diversity, focusing on a specific codebase. Worse still, the quality of bug explanations in existing datasets is low, as they typically use imprecise and uninformative commit messages as explanations. To address these issues, we propose an automated collecting framework REEF to collect REal-world vulnErabilities and Fixes from open-source repositories. We focus on vulnerabilities since they are exploitable and have serious consequences. We develop a multi-language crawler to collect vulnerabilities and their fixes, and design metrics to filter for high-quality vulnerability-fix pairs. Furthermore, we propose a neural language model-based approach to generate high-quality vulnerability explanations, which is key to producing informative fix messages. through extensive experiments, we demonstrate that our approach can collect high-quality vulnerability-fix pairs and generate strong explanations. the dataset we collect contains 4,466 CVEs with 30,987 patches (including 236 CWE) across 7 programming languages with detailed related information, which is superior to existing benchmarks in scale, coverage, and quality. Evaluations by human experts further confirm that our framework produces high-quality vulnerability explanations.

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Malware Classification Based on System Call

Malware Classification Based on System Call

引用

7th international conference on advances in Visual Informatics, IVIC 2021

作者： Zaharin, Mohamad Redza Izudin Abu Shariff, Shafiza Mohd East Coast Infosec Sdn. Bhd Petaling Jaya Selangor Malaysia Malaysian Institute of Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9783030902346

It has always been a never-ending battle between security analysts and malware developers due to the complexity of malware changing as quickly as innovation grows. Current research focuses on the application of machine learning techniques for malware detection due to its ability to combat the malware evolution. Furthermore, malware is hard to identify due to the enormous number of samples and its unknown activities. therefore, a platform is needed to analyze the malware samples and identify their activities. A comparison between classification models via system calls made by the malware is implemented within the platform that capable of differentiating between a normal program and malware. the development incorporated DRAKVUF and Debian OS. the result is determined by the ability to log and identify the malware activities via system calls based on classification model in order to differentiate between malware and a normal program. In conclusion, the integration of malware analysis techniques and classification techniques will help provide more information to identify and distinguish malware from normal applications. © 2021, Springer Nature Switzerland AG.

关键词： Learning systems

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A New Evolutionary Approach to Multiparty Multiobjective Optimization 12th

A New Evolutionary Approach to Multiparty Multiobjective Opt...

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12th international conference on advances in Swarm Intelligence, ICSI 2021

作者： She, Zeneng Luo, Wenjian Chang, Yatong Lin, Xin Tan, Ying School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong518055 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230027 China and Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9783030788100

Multiparty multiobjective optimization problems (MPMOPs) are a type of multiobjective optimization problems (MOPs), where multiple decision makers are involved, different decision makers have different objectives to optimize, and at least one decision maker has more than one objective. Although evolutionary multiobjective optimization has been studied for many years in the evolutionary computation field, evolutionary multiparty multiobjective optimization has not been paid much attention. To address the MPMOPs, the algorithm based on a multiobjective evolutionary algorithm is proposed in this paper, where the non-dominated levels from multiple parties are regarded as multiple objectives to sort the candidates in the population. Experiments on the benchmark that have common Pareto optimal solutions are conducted in this paper, and experimental results demonstrate that the proposed algorithm has a competitive performance. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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An Optimized Approach Towards Malware Detection Using Java Microservices 8th

An Optimized Approach Towards Malware Detection Using Java M...

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8th international conference on advances in Computing and Data sciences, ICACDS 2024

作者： Goel, Mandhar thakur, Subodh Kumar, Nishant Gupta, Nishant Singh, Mayank Department of CSE Sharda University Greater Noida India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9783031709050

A malware can completely destroy the confidentiality, integrity, and availability of a system once it comes into contact with it, it poses a serious threat to computer security. Various mitigation and detection techniques have been developed over decades to address this problem. In order to deal with complex datasets that result in a high computational overhead and resource consumption, a new model that uses microservices to implement the random forest algorithm for malware detection and analysis has been proposed in this paper. the latter part of this paper discusses the overall microservice model and how it can lead to improved results overtime when the size of dataset provided increases by thousands. In conclusion a detailed difference between the micro- services-based model and the non-microserviced model has been presented by use of the acquired results gained during the application phase, the comparison details the parameters such as ROC curve, precision recall curve, bootstrapping resampling for AUC & a detailed feature selection of dataset to showcase the difference in values between the microservices model dataset and baseline model dataset. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： accuracy Malware microservices parameters

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An Advanced Deep Learning Detection of Rice Plant Diseases Based on Residual Neural Networks 8th

An Advanced Deep Learning Detection of Rice Plant Diseases B...

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8th international conference on advances in Computing and Data sciences, ICACDS 2024

作者： Huong, Nguyen thanh Lan, Nguyen Dang Dong, Trinh Cong thanh, Bui Dang School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Institute for Control Engineering and Automation Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031709050

For countries with the large rice export in the world, taking care of rice plants for them to grow healthily is very necessary. One of the techniques is to detect diseases promptly, at the right time, on the right diseases in order that necessary treatment can be taken to minimize the harmful effects of diseases on rice plants as well as prevent them from breaking out into epidemics. Previously, disease detection was carried out manually, which took a lot of time and labor. Nowadays, automated methods have been increasingly applied for precision agriculture applications. In the article, a method to automatically detect rice diseases based on machine learning, especially deep learning concentrated method is proposed. In this article, our study concentrates on detecting four most common rice diseases: brown spot, hispa, blast and blight. the dataset for training, testing and validation collected and constructed from Kaggle and Google dataset includes approximately 400 images for each type of rice leaf diseases. From the experiment to assess the performance of the proposed learning method, the accuracy can reach approximately 97%. thereby, the method shows feasibility and reasonableness, with extensively potential usage for practical precision agriculture application. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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MECE Guided Systematic Analysis and Fussy Assessment of Major Sports Event Broadcasting Center Service Quality—Take the 7th Military World Games as an Example 3rd

MECE Guided Systematic Analysis and Fussy Assessment of Majo...

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3rd international conference on computer science, Engineering and Education Applications, ICCSEEA 2020

作者： Wang, Ziye Lu, Fei Zhao, Xinpeng Zhang, Qingying College of Journalism and Communication Wuhan Sports University Wuhan430079 China Wuhan 430056 China School of Logistics Engineering Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9783030555054

the service of broadcasting center is indispensable for sports events. While recording events in various forms, it can timely share the process, results and wonderful moments of the competition to the public. Whether the services provided by the broadcasting center for journalists and media organizations are in place directly affects the quality of event communication. Taking the 7th world military games as an example, this paper analyzes the functions, characteristics and functions of the event broadcasting center by using the MECE principle, probes into the construction requirements of the event broadcasting center of Wuhan military games, confirms what problems need to be analyzed by using the principle of logical thinking, and seeks for the breakthrough point to solve the problems. On this basis, a structural model of the factors influencing the service quality of the event broadcasting center is established. According to the steps of establishing factor set, evaluation set, weight set, single factor fuzzy evaluation and fuzzy comprehensive evaluation of the service quality of the relay center of Wuhan military games, the comprehensive evaluation of the service quality of the relay center of Wuhan military games is completed, which is significant to provide valuable reference for the media service of large-scale events. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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