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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Saraswathi, P. Prabha, M. Dhiyanesh, B. Akchay Kumar, R. Barith Ahamed, J. Mohamed Ismail, M. Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India Dr. N. G. P. Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350367171

Across the earth's surface, gases are abundant resources, yet some pose significant hazards to human life. While halting their production is unfeasible, managing and monitoring them is vital for safeguarding environmental and public health. Hazardous gases such as Particulate Matters (Sulfates, Nitrates, Ammonia, Black Carbon, Dust), Carbon Monoxide, Ozone, Nitrogen Dioxide, and Sulfur Dioxide permeate the air we breathe, their levels influenced by temperature and humidity. Leveraging the Espressif ESP32 DevKit V1 Board, our IoT-based system connects various sensors to detect these gases, ensuring air quality. Data transmission to the cloud via integrated WiFi enables real-time monitoring. Crucial for IoT applications, maintaining sensor data quality enhances harmful gas monitoring, control, and prediction. Prompt alerts are dispatched when threshold levels exceed safety limits, aiding swift response. Thorough analysis of live sensor data or datasets, utilizing statistical methods and threshold values, enables proactive gas detection. © 2024 IEEE.

关键词： Sulfur dioxide

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Web Extension For Phishing Website Identification: A Browser-Based Security Solution

Web Extension For Phishing Website Identification: A Browser...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sathvik, D. Prahasith, A. Pendam, Keerthi Dhanalakshmi, D. Hariharan, Shanmugasundaram Kukreja, Vinay Department of CSE Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350305708

With the rise of internet-based scams and cyberattacks, phishing remains one of the most prevalent threats to online users. In response to this ongoing challenge, this research paper presents the design and implementation of a Web Extension to enhance user security by identifying and alerting users to potential phishing websites in real time. Web Extension utilizes a combination of phishing detection techniques, including URL analysis, SSL certificate verification, and machine learning algorithms. By intelligently analyzing website URLs, the extension can detect suspicious patterns, misspellings, or anomalous subdomains commonly associated with phishing attempts. It further verifies the authenticity of SSL certificates to ensure a secure connection between users and websites. To enhance its accuracy, Web Extension can also leverage external APIs or databases containing known phishing websites. This cross referencing enables prompt identification of blacklisted websites and prevents users from inadvertently falling victim to phishing scams. © 2023 IEEE.

关键词： Websites

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A Comparison Study on Machine Learning Approaches for Thyroid Disease Prediction 8

A Comparison Study on Machine Learning Approaches for Thyroi...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Dhanamithra, R.G. Anly Antony, M. Kumar, R Satheesh Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Kerala India

ISBN: (纸本)9781665408165

Thyroid disease is rising fast among individuals these days and it affects more in women than males. Thyroid disorders are most commonly caused by abnormal thyroid hormone production. Hyperthyroidism is a condition caused by an excess of hormones. Insufficient hormone production causes hypothyroidism. Tiredness, dry skin, cold intolerance, facial swelling, menstrual cycles, and hair loss are all symptoms of thyroid dysfunction. It is far more vital to prevent rather than cure such disorders, because most therapies involve long-term medication. As a result, it's critical to look at the thyroid dataset for early disease detection so that steps can be done to avoid the deadly condition of thyroid disease. Machine learning techniques are important in the medical profession because they help doctors make better decisions, diagnose diseases more accurately, and save patients money and time. To make the data rudimentary enough for analytics to highlight the likelihood of patients developing thyroid disease, data cleaning techniques were used. The purpose of this study is to compare the accuracy and other performance criteria of multiple machine learning algorithms for predicting thyroid disease. This paper focuses on the current machine learning techniques utilized in the diagnosis and prediction of thyroid detection. © 2022 IEEE.

关键词： Forecasting

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Jointly Preserving Subject and Object Anonymity in Nearby Friends Querying for Curious Service Providers

Jointly Preserving Subject and Object Anonymity in Nearby Fr...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Abul, Osman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering TOBB University of Economics and Technology Ankara Ankara Turkey

ISBN: (纸本)9798400716928

This work studies the urban area location privacy preserving location updates and nearby friends (NF) querying for the centralized proximity based services (PBSs). The urban area constraint forces the user mobility to be on the city road network, modeled as a weighted directed graph. The location privacy model exploits location anonymity through subgraph cloaking. Two kinds of location privacy issues have been identified and handled in a unified framework, (i) Subject Anonymity on location updates, i.e., providing location anonymity of the location updater (against the curious PBS provider), and (ii) Object Anonymity on NF querying, i.e., providing location anonymity of friends in NF query responses (against the curious query issuer). In the former, the user simulates the PBS provider's belief (a subgraph) on his location whereabouts, and locally blocks anonymity violating location update requests which is understood by a simulated attack. In the latter, as the NF query processor, the PBS provider simulates the user's mutual beliefs (subgraphs) on friends' location whereabouts obtained through previous NF queries. By a simulated attack, some friends' existence are suppressed from the NF query responses. An experimental evaluation on a real dataset has been conducted. © 2024 ACM.

关键词： Location

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Smart signal control by detecting traffic intensity using IR Sensors

Smart signal control by detecting traffic intensity using IR...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Praneeth, Mididoddi Sai Saketh, Kotagiri Sai, Vuppu Amith Dhanalakshmi, D. Hariharan, Shanmugasundaram Kukreja, Vinay Department of CSE Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350305708

The Population increase in the countries like India is increasing Day-by-Day. The Traffic in the roads of the city is also becoming worse in some cases. The people are wasting their valuable time on road and leaving many important works because of this heavy traffic. This waiting time in the Traffic causes time wastage, wastage of fuel, damage of environment. In so many cities people are complaining that they cannot reach their destination on time because of this heavy traffic situation on every places in the city and Accidents are also increasing on the roads due the Traffic conditions. The proposed study presents an approach to solve this traffic problem by Traffic Signal Automation by sensing traffic intensity by IR sensors using IoT (Internet of Things). So The Proposed system can control the traffic by adjusting the timings of the signals based up on the calculations of Traffic intensity on all the sides of the roads. It provides a better Traffic Signal control. These IR sensors are placed on the all sides of the roads to detect the data of the Traffic congestion. So this way we can control the traffic in many kind of situations. © 2023 IEEE.

关键词： Dynamic IOT Microcontroller Signal Automation Traffic Signal Traffic Signal

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A Deep Learning Framework for Prediction of Heart Diseases from Clinical Observations 2

A Deep Learning Framework for Prediction of Heart Diseases f...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Thomas, Daze Mohan, M. Narasimharaj, V. Yamuna, S. Sns College of Engineering Artificial Intelligence & Data Science Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India

ISBN: (纸本)9798350325799

Among today's youth, heart disease has emerged as a major health concern. Many forms of cardiac disease can be traced back to bad habits and clinical characteristics. Many other models have been proposed by researchers to aid in the diagnosis of various conditions, however the vast majority either has poor accuracy performance or are overly complex to implement in real-time clinical settings. In addition, these models tend to function correctly for smaller class sets, but their performance degrades with an increase in class count. To facilitate answer these issues, this study suggests creating a novel, effective, and extensible strategy for disease prediction. This employs a deep learning technique, which boosts multiple sets of clinical data using a combination of StackingCV classifier-bagging and feature-boosting. Feature variance is increased by augmenting these vectors with a boosting of classifiers at the outset. AdaBoost strategy (ABA), Support-Vecto-Classifier (SVC), and Extreme-Gradient-Boosting (XGBoost) are then applied to these characteristics to help improve accuracy performance at the cost of minuscule increases in computational delays. Single illness classifier accuracy was 93.44% and multiple disease classifier accuracy was 60.5% when the model was tested using datasets from the University of California, Cleveland. When contrasted to various state-of-the-art methods and classifiers, such as a Deep-Neural-Network (DNN), Random-Forest (RF), XGBoost, &SVC, the suggested model was shown to be at least 3.9% more accurate. Thus, it is appropriate for use in live medical settings. © 2023 IEEE.

关键词： Adaptive boosting

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Retrieval Across Any Domains via Large-scale Pre-trained Model 41

Retrieval Across Any Domains via Large-scale Pre-trained Mod...

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41st International Conference on Machine Learning, ICML 2024

作者： Yan, Jiexi Yin, Zhihui Xu, Chenghao Deng, Cheng Huang, Heng School of Computer Science and Technology Xidian University Shaanxi Xi'an China School of Electronic Engineering Xidian University Shaanxi Xi'an China Department of Computer Science University of Maryland College Park United States

In order to enhance the generalization ability towards unseen domains, universal cross-domain image retrieval methods require a training dataset encompassing diverse domains, which is costly to assemble. Given this constraint, we introduce a novel problem of data-free adaptive cross-domain retrieval, eliminating the need for real images during training. Towards this goal, we propose a novel Text-driven Knowledge Integration (TKI) method, which exclusively utilizes a pre-trained vision-language model to implement an "aggregation after expansion" training strategy. Specifically, we extract diverse implicit domain-specific information through a set of learnable domain word vectors. Subsequently, a domain-agnostic universal projection, equipped with a non-Euclidean multi-layer perceptron, can be optimized using these assorted text descriptions through the text-proxied domain aggregation. Leveraging the cross-modal transferability phenomenon of the shared latent space, we can integrate the trained domain-agnostic universal projection with the pre-trained visual encoder to extract the features of the input image for the following retrieval during testing. Extensive experimental results on several benchmark datasets demonstrate the superiority of our method. Copyright 2024 by the author(s)

关键词： Visual languages

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Cyclical Training Framework with Graph Feature Optimization for Knowledge Graph Reasoning

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computers, Materials & Continua 2025年第5期83卷 1951-1971页

作者： Xiaotong Han Yunqi Jiang Haitao Wang Yuan Tian School of Artificial Intelligence Jilin UniversityChangchun130012China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOEChangchun130012China College of Computer Science and Technology Jilin UniversityChangchun130012China

Knowledge graphs(KGs),which organize real-world knowledge in triples,often suffer from issues of *** address this,multi-hop knowledge graph reasoning(KGR)methods have been proposed for interpretable knowledge graph *** primary approaches to KGR can be broadly classified into two categories:reinforcement learning(RL)-based methods and sequence-to-sequence(seq2seq)-based *** each method has its own distinct advantages,they also come with inherent *** leverage the strengths of each method while addressing their weaknesses,we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the policy-based RL training phase using a transformer ***,a multimodal data encoding(MDE)module is introduced to improve the representation of entities and relations in *** module treats entities and relations as distinct modalities,processing each with a dedicated network specialized for its respective *** then combines the representations of entities and relations in a dynamic and fine-grained manner using a gating *** experimental results from the knowledge graph completion task highlight the effectiveness of the proposed *** five benchmark datasets,our framework achieves an average improvement of 1.7%in the Hits@1 metric and a 0.8%average increase in the Mean Reciprocal Rank(MRR)compared to other strong baseline ***,the maximum improvement in Hits@1 exceeds 4%,further demonstrating the effectiveness of the proposed approach.

关键词： Knowledge graph reinforcement learning transformer

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Machine Learning Approaches for IC Fault Localization and Diagnosis 9

Machine Learning Approaches for IC Fault Localization and Di...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Dwivedi, Yagya Dutta Aparna, P. Prasad, S. J. Suji Dineshkumar, S. Hemalatha, R. Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad500043 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India Kongu Engineering College Department of Electronics And Instrumentation Engineering Tamil Nadu Erode638060 India M.Kumarasamy College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Karur639113 India St. Joseph's College of Engineering Omr Department of Computer Science And Engineering Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350365092

The conducted researchprovide a machine learning-driven method for thermal testing of integrated circuits. This method involves comparing the current temperature distribution with a reference distribution that is suitable for the energy state of the system. Furthermore, the research explores a methodology for positioning temperature sensors. This placement approach is advised for utilization in the identification and troubleshooting of issues with integrated circuits (ICs). Our failure localization method utilizes the average temperatures in several sub- areas of the integrated circuit, together with the interconnections between those temperatures. This research provides the simulation results for the sensor placement technique, fault detection, and localization. The results on the productivity of the testing approach are presented utilizing statistical methodologies. © 2024 IEEE.

关键词： Timing circuits

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Bridging Devotion and Safety: IoT-Driven Contactless Temple Bell Ringing During the Pandemic Era

Bridging Devotion and Safety: IoT-Driven Contactless Temple ...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sangeetha, A. Subramanian, M. Prasath, S. Ram Gokulakrishnan, K. Rajkumar, G. Vinoth Muthu, A. Essaki Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India College of Engineering Guindy Anna University Department of Electronics and Communication Engineering Chennai India Jp College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798350300857

An IoT-driven Contactless Solution for Temple Bell Ringing in the Pandemic Era is an innovative research work that aims to address concerns regarding the transmission of diseases similar to COVID-19 and enable individuals to safely engage in religious practices. By employing Arduino UNO R3 and ESP8266 boards, this work presents a distinctive solution that allows individuals to ring temple bells without the need for physical contact. The system incorporates an ultrasonic sensor that is connected to one of the ESP8266 boards. This sensor effectively detects individuals within a specific distance range. By establishing a Wi-Fi connection between two ES P8266 boards, the ultrasonic sensor transmits a message to a relay connected to the Arduino UNO. Consequently, the relay activates a motor, which, in turn, triggers the ringing of the temple bell in an automated fashion. Through this contactless approach, the system effectively eliminates the necessity for individuals to physically touch the temple bell, thereby significantly reducing the risk of disease transmission within public spaces. By leveraging the power of IoT technology, this system ensures that individuals can fulfill their religious obligations without any constraints while simultaneously prioritizing their safety. This research serves as a testament to the remarkable potential of IoT in effectively mitigating the challenges posed by the pandemic and facilitating safer religious practices. It exemplifies technology's ability to adapt to and overcome public health crises, presenting innovative solutions that empower individuals to continue their devotional activities in a completely contactless manner. © 2023 IEEE.

关键词： COVID-19

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