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12th International Conference on Learning Representations, ICLR 2024

作者： Mo, Yujie Nie, Feiping Hu, Ping Shen, Heng Tao Zhang, Zheng Wang, Xinchao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China National University of Singapore Singapore Northwestern Polytechnical University United States School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Self-supervised heterogeneous graph learning has achieved promising results in various real applications, but it still suffers from the following issues: (i) meta-paths can be employed to capture the homophily in the heterogeneous graph, but meta-paths are human-defined, requiring substantial expert knowledge and computational costs;and (ii) the heterogeneity in the heterogeneous graph is usually underutilized, leading to the loss of task-related information. To solve these issues, this paper proposes to capture both homophily and heterogeneity in the heterogeneous graph without pre-defined meta-paths. Specifically, we propose to learn a self-expressive matrix to capture the homophily from the subspace and nearby neighbors. Meanwhile, we propose to capture the heterogeneity by aggregating the information of nodes from different types. We further design a consistency loss and a specificity loss, respectively, to extract the consistent information between homophily and heterogeneity and to preserve their specific task-related information. We theoretically analyze that the learned homophilous representations exhibit the grouping effect to capture the homophily, and considering both homophily and heterogeneity introduces more task-related information. Extensive experimental results verify the superiority of the proposed method on different downstream tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Predicting heart disease with logistic regression model 1st

Predicting heart disease with logistic regression model

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Tiwari, Avdhesh Kumar Chauhan, Kunal Yadav, Muskan Department of Computer Science and Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9781032874036

Heart illness is becoming more and more common these days, and early detection is key to preventing these issues. Determining whether a person has cardiac disease is a difficult but crucial task. Our study focuses on identifying patients who are at a higher risk of developing heart disease by utilizing various medical data. Based on a patient’s medical history, we developed a technique to predict if they will receive a heart disease diagnosis. To make our predictions better, we used a smart computer program Logistic Regression model, a sophisticated algorithm, to enhance the accuracy of predictions. This new method worked well, showing better results than the older ways, such as naive Bayes. This system doesn’t just help predict heart attacks but also makes medical care better and cheaper. Our Research, saved in a .pynb file, provides us with useful information to identify individuals who might have heart disease early on. This way, we can take better care of them before things get worse and we are not stop here if the person has symptoms of heart disease. Subsequently, the platform facilitates the scheduling of online appointments with certified healthcare professionals, ensuring swift intervention. To further empower patients, the platform offers visibility into the qualifications and certifications of healthcare providers. Users can review doctors’ credentials, confirming their verification status, fostering trust and transparency in the patient-doctor relationship. This enhanced accessibility not only improves the overall patient experience but also addresses concerns related to the credibility of healthcare practitioners. © 2025 the Author(s).

关键词： Cardiology

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AI Vigilance: Pioneering Malware Detection In The Android Realm 2

AI Vigilance: Pioneering Malware Detection In The Android Re...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nirosha, Veeramachaneni Akhil, Gopala Srinivas, G. Manikanta Anvesh, J. Kumar, G.Sai B V Raju Institute of Technology Computer Science Engineering Narsapur India

ISBN: (纸本)9798331528713

provides a different strategy for preventing malware from harming Android devices. By improving accuracy and speed, the research seeks to transform malware detection through the use of modern artificial intelligence (AI) approaches. Real-time monitoring systems, prediction models for finding complex malware behaviour patterns, and AI-driven feature extraction techniques are important elements. The research aims to determine the efficacy and dependability of its AI-based malware detection system through thorough testing and validation with various datasets. The focus is on smooth integration, performance optimisation, and scalability within the current Android security framework. The research findings have considerable potential to improve mobile security by providing preventive measures against new malware attacks.'AI Vigilance' is an innovative attempt aimed at establishing a more secure and robust Android computing environment in the face of constantly changing cybersecurity threats. © 2024 IEEE.

关键词： Android malware

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Augmented Reality Enabled Development of the SIBI Alphabet for Deaf Communication 11

Augmented Reality Enabled Development of the SIBI Alphabet f...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Fatimah, Dini Destiani Siti Satria, Eri Firmansyah, Refi Garut Institute of Technology Department of Computer Science West Java Garut Indonesia

ISBN: (纸本)9798350389654

Communication with deaf people requires a particular language, namely sign language. The Indonesian Language Sign System (SIBI) is used in the school curriculum as the official sign language for the deaf in Indonesia. For communication with deaf people to be smooth, it is necessary to provide convenience for the community to learn sign language. This research aims to produce an application that can be used to help deaf people learn sign language. The target users of this application are deaf students in the seventh grade of Junior High School (SMP). The Isyarat Kita application is developed using the Multimedia Development Life Cycle (MDLC) method, a rigorous six-stage process that ensures the quality and effectiveness of the application. This application presents three-dimensional objects and hand gesture animations that demonstrate the letters of the SIBI alphabet from various angles with rotations of up to 360 degrees. This application also provides an SIBI letter and word recognition video feature. Alpha testing shows that all functions work as designed, while beta testing using the End User computing Satisfaction method results in the Very Satisfied criterion with a score of 93.9 percent. © 2024 IEEE.

关键词： Curricula

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“Half of the Node Records Are Forged?”: The Problemof Node Records Forgery in Ethereum Network

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computer Modeling in Engineering & sciences 2024年第2期138卷 1713-1729页

作者： Yang Liu Zhiyuan Lin Yuxi Zhang Lin Jiang Xuan Wang School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)Shenzhen518055China Peng Cheng Laboratory Shenzhen518055China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055China

Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network *** this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.

关键词： Blockchain ethereum peer-to-peer networks node discovery protocol malicious behavior

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Social Media Text Analysis for Disaster Management Using DistilBERT Model

Social Media Text Analysis for Disaster Management Using Dis...

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2024 International Conference on science, technology, Engineering and Management, ICSTEM 2024

作者： Varghese, Sherin R. Juliet, Sujitha Athish, N.S. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350376913

There is no doubt on the fact that disasters like floods, earthquakes and other man-made ones have a destructive impact on human lives. Timely response is crucial to minimize the casualties and aid recovery. Social media in such times, are vital as people share experiences, news and request assistance. This study investigates the efficiency of Transformer-based models for fine-grained sentiment analysis of disaster tweets on social media. Five models, including Distil-BERT, are compared and analyzed using Twitter data. The analysis categorises tweets into ten classes reflecting human sentiments during a disaster. Model performance is evaluated using accuracy, precision, recall, F1-score and execution time. While all the models demonstrate strong performance, Distil-BERT achieved comparable accuracy with a significant reduction in training and execution time (45%-55%). These findings suggest the potential of Distil-BERT (Distilled BERT) as the core component in disaster management applications;enabling organizations to gain a deeper understanding of the multifaceted public sentiment during disasters and make informed decisions for improved public aid and resource allocation. © 2024 IEEE.

关键词： Sentiment analysis

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Analysis of Social Engineering Security Challenges and Its Emerging Technologies 1st

Analysis of Social Engineering Security Challenges and Its E...

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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Vidyasri, P. Suresh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031759567

Information technology is advancing at a record rate in the twenty-first century. It has grown to be an integral component of our daily lives. As the globe develops and more people use ubiquitous computing, cyber-security risks and privacy issues also grow proportionately. Cyber security is crucial to the information technology industry. Cyber-crimes, which are on the proliferate daily, are the first thing that transpire to mind whenever we think about cyber security. In spite of several safety measures, many people are still utterly concerned about cyber security. According to studies, social engineering attacks, which account for a sizable portion of all cyberattacks, are growing more common. By manipulating consumers’ minds, social engineers might trick them into revealing sensitive information or compromising their security. One of the best ways to defend against social engineering attacks is to be able to identify them. Phishing is a common method of social engineering attack that is used to start more than 90% of successful hacks and data breaches. Cyber-attack detection and prevention are challenging tasks. This paper provides an extensively analysis of social engineering attacks, including their categories and a pop-up phishing classification system that we developed uses one of the best-known classifiers Support Vector Machine of Supervised Learning Paradigm in Machine Learning and Wrapper-based feature selections that incorporates the metadata of URLs are used extracts elements intended to defeat popular phishing detection techniques in order to provide a security model and to make predictions. Furthermore, this study offers an overview of the problems with phishing attacks now as well as recommendations for future research in this area. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Phishing

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Gracious, S. Shirley, C.P. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Heart disease is still the world's top cause of death, and becoming older is a major risk factor. Age-related risk for heart disease can be decreased by using early detection and prevention strategies. This research study presents a novel machine learning-based method for estimating an individual's risk of heart disease based on age-related variables and their medical history. This study analyzes the efficacy of ensemble learning methods, namely XGBoost and Random Forest models, in predicting the risk of heart disease by utilizing a tabular dataset that includes data on individuals' health records from various age groups. By means of thorough experimentation and model validation, the proposed model classifies heart disease with 93.26% accuracy. This research adds to the advancement of predictive modeling for age-related heart disease and holds promise for personalized healthcare interventions aimed at improving patient outcomes and lowering healthcare costs. The research findings not only highlight the potential of machine learning in healthcare but also highlight the significance of early intervention strategies tailored to age-related factors. © 2024 IEEE.

关键词： Machine learning

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Utilizing Ensemble Learning to enhance the detection of Malicious URLs in the Twitter dataset 4

Utilizing Ensemble Learning to enhance the detection of Mali...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Arora, Rakhi Gupta, Rishi Yadav, Pradeep Manipal University Computer Science & Engineering Jaipur India Institute of Technology & Management Computer Science & Engineering Gwalior India

ISBN: (纸本)9798350307757

Due of Twitter's widespread appeal as a platform for sharing activities, entertainment, and information, spammers have taken notice and are using it to propagate false information and harass clients. Researchers are faced with the issue of locating user profiles and malicious URLs on Twitter so that prompt action can be taken. Several prior studies have tackled this issue and stopped spammers' actions on Twitter by utilizing various tactics. In this study, we create multiple models to detect content and categorise the URLs in it as spam or not-spam, employing several features, including hybrid, content based, and profile based. To build a spam detection, we first gather and categorise a sizable dataset from Twitter. Next, we take different features out of the gathered dataset and combine them to generate a set of rich features. In addition, we utilise various ensemble learning methodologies to construct prediction models. We conducted a thorough analysis of various methods using the gathered dataset, evaluating their performance in terms of f1-score, accuracy, recall and precision. The outcomes demonstrated that distinct sets of learning strategies employed have produced better results in the classification of tweet spam. Most of the time, the numbers for several performance metrics are higher than 90%. According to these findings, utilising user profile, content, and hybrid data while detecting suspicious URLs helps in the development of more accurate prediction models. This paper provides a current overview of prominent ensemble algorithms used in malicious URL detection. Notably, the k-NN technique, when applied in both bagging and random forest ensemble methods, demonstrates the highest accuracy. © 2024 IEEE.

关键词： User profile

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Exploring Factors Influencing PCOS: Analysis using Data Visualization Techniques 10

Exploring Factors Influencing PCOS: Analysis using Data Visu...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Laya, N. Juliet, Sujitha Anitha, J. Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9798350384369

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder worldwide, presenting challenges such as miscarriage and fertility issues. Utilizing Data Visualization (DV) tools like bar graphs, scatter plots, and unique charts, this analysis aids in comprehending PCOS demographics, variations, and correlations, contributing to enhanced understanding of the syndrome. Bubble charts depict connections between age, weight, and PCOS, while a Sankey diagram clarifies dataset interconnections, simplifying PCOS comprehension. Stressing the importance of follicle counts as a diagnostic marker, the analysis highlights a higher count's significance in PCOS diagnosis. It advocates for further research to refine our understanding, facilitating more accurate diagnoses. Given PCOS 's prevalence and associated challenges, understanding it is paramount. The chosen DV tools aim to simplify complex PCOS data for researchers and healthcare professionals. Emphasizing follicle counts' diagnostic potential underscores the importance of accurate and timely diagnoses, prompting further investigation to enhance PCOS comprehension and management. © 2024 IEEE.

关键词： Data visualization

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