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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

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Facial Emotion Recognition for Virtual Customer Service Agents

Facial Emotion Recognition for Virtual Customer Service Agen...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Gupta, Shaswat Saxena, Srishti Verma, Vandana Nishad, D.K. Galgotias College Of Engineering. & Technology Department Of Computer Science And Engineering Greater Noida India Dsmnru Department Of Electrical Engineering UP Lucknow India

ISBN: (纸本)9798350366846

Due to the rapidly changing customer support environment, firms are using virtual agents to improve customer interactions, reduce response times, and deliver better solutions to customer questions, concerns, and issues. Virtual agents must understand and respond to user emotions to enable more human-like interactions. Face recognition, or Facial Expression Recognition (FER) holds significant importance in the field of computer vision and artificial intelligence to understand the emotions of the eyes. Deep learning is used to classify face expressions into emotion categories in this study. We train and test our models on the CK+ dataset for high accuracy. We also offer an interactive online interface for real-time emotion recognition using the Streamlet library. FER improves virtual customer care agents' emotional intelligence and user experience, as shown by our studies. © 2024 IEEE.

关键词： Emotion Recognition

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Cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

Cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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Integrating 4QS Factors to Enhance Learning Quality Using Machine Learning 2

Integrating 4QS Factors to Enhance Learning Quality Using Ma...

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

作者： Kale, Ruchita Tasare, Mohan Dhumale, Yogita Rawat, Manoj Kumar Sawale, Gaurav Prmit&r Department of Computer Science & Engineering Badnera Amravati India Prof. Ram Meghe College of Engineering & Management Department of Electrical Engineering Badnera Amravati India Medicaps University Department of Computer Science & Engineering Indore India

ISBN: (纸本)9798331528713

The ultimate goal of any educational institution is to offer the best educational experience and impart knowledge effectively to students. Identifying students who need extra support and taking appropriate actions to enhance their performance is crucial in achieving this goal. This study explores the application of machine learning techniques to predict students' final grades based on their historical data. The proposed strategy integrates the 4QS Model, which focuses on intelligence, adversity, spirituality, emotional, and stress factors to address essential aspects of the academic environment. By targeting these key factors, the aim is to improve the teaching and learning environment, leading to enhanced student performance and future success. This approach enables educators to identify areas where students may struggle and tailor their teaching methods accordingly, resulting in a more effective and supportive educational experience for both faculty and students. A classification machine learning algorithm was employed to predict student performance. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT Enabled Smart Farming: A Controlled Environment Agriculture Application

IoT Enabled Smart Farming: A Controlled Environment Agricult...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Velu, Anitha Ramamoorthy, Raghu Saravana Kumar, E. Shruthi, K. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313987

Horticulture is the science of sustainable production, advertising and marketing the intensively cultivated food and decorative plants. The major intricacy in horticulture is maintaining the temperature within the controlled environment such as polyhouse or green house which leads to plant damage like root rot, leaf spots, downy mildew. This results in IoT of human intervention in checking the temperature periodically and provide remedial measures to maintain it. In order to overcome such issues this work proposes an IoT-based Automatic Temperature Controller (ATC) system using Arduino board interfaced with the environmental data by deploying Internet of Things (IoT). The proposed work incorporates two phases: (i) Collection of data for the different floral plants and uploading it to the IoT cloud (ii) Implementing the hardware part using Arduino UNO kit for ATC system. The ATC system gets the values of temperature and humidity from DHTII sensor where, the entire system is connected to the Arduino microcontroller which compares the recorded value with the threshold value set by user in cloud. The motor will be turned on by the controller being the observed temperature is found larger than the threshold value. The proposed system has been tested on different floral plants that are of high productivity in and around Hosur, Tamil Nadu circumstances namely Gerbera daisy, Button rose, Chrysanthemum and Rose. This work can be extended for other agricultural application purposes like creepers, mushroom cultivation etc. © 2023 IEEE.

关键词： Arduino Uno Automatic Temperature Controller System Digital Temperature and Humidity sensor Horticulture Internet of Things

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Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

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Chinese Journal of Electronics 2023年第4期32卷 896-907页

作者： ZHANG Zhe WANG Bilin YU Zhezhou ZHAO Fengzhi College of Computer Science and Technology Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation—attention guided enhancement network(AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps(CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic ***, the CAM produced by the lower layer can capture the complete object region but with many ***, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation *** on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

关键词： Training Annotations Semantic segmentation Scalability Semantics Benchmark testing Boosting

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VGE: Gene-Disease Association by Variational Graph Embedding

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International Journal of Crowd science 2024年第2期8卷 95-99页

作者： Han, Peng Zhang, Xiangliang Department of Computer Science University of Electronic Science and Technology of China Chengdu611731 China College of Engineering University of Notre Dame Notre DameIN46556 United States

Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting the new disease-gene associations, due to its strong capability to capture the relevance of disease and gene in the new semantic embedding space. However, a single embedding vector cannot informatively represent a disease or gene and cannot characterize the uncertainty of their features. We propose to learn a distribution for a disease or gene under the variational autoencoder framework, which enables disease-gene associations to be modeled by the Kullback-Leibler divergence. The experiment results show that our model outperforms the state-of-the-art models significantly in various metrics. © The author(s) 2024.

关键词： Genes

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Deep learning based approach for rice prediction from authenticated block chain mode

Deep learning based approach for rice prediction from authen...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Tallapragada, V. V. Satyanarayana Chaudhary, Sumit Glory, J. Sherine Venkatesan, G. Maheswari, B. Uma Kumar, E. Rajesh Department of Electronics and Communication Engineering Mohan Babu University Andhra Pradesh Tirupati India School of Computer Science and Artificial Intelligence TransStadia University Gujarat Kankaria Ahmedabad India Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai Tamil Nadu Chennai India Department of Civil Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781032669663

Various quality and safety issues with rice, a significant food crop worldwide, are strongly tied to human health. The rice supply chain is a crucial topic of food safety research that has received more and more attention. This study, which used block chain technology, looked into issues with data privacy and circulation efficiency brought on by complicated systems for the supply of rice, continuous quality periods, and various performance factors at every connection. Initially constructed a valuable data helps to establish for every connection in the production field after deconstructing the quality and safety of each connection at the information level. Based on that, we developed a block chain-based information supervision model for the rice supply chain. To fulfill the requirements of authorities for efficient supervision, a variety of encryption methods are utilized to protect the private information of businesses in the give connection. Additionally, we suggest a Deep Convolutional Neural Network algorithm that evaluates enterprise node credit, optimizes master node selection, and guarantees high efficiency and cheap cost. The findings showed that the suggested approach can improve rice prediction and offer a workable remedy for the grain and oil quality and safety supervision. © 2024 The Author(s).

关键词： Supply chains

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Enhanced Cloud Data Security with Lightweight Chaotic Map and Quantum Key Distribution

Enhanced Cloud Data Security with Lightweight Chaotic Map an...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Devi, M. Varshana Aasha, G. Vimal, P. Gowtham, K. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Vellore Institute of Technology Computer Science and Engineering Vellore632014 India

ISBN: (纸本)9798350359688

In today's digital landscape, ensuring the security of data transmitted and stored in the cloud is paramount. Traditional encryption methods face challenges in scalability and susceptibility to quantum threats. This abstract presents a novel approach to cloud data security leveraging lightweight chaos encryption and quantum key distribution (QKD). The proposed mechanism combines the dynamic properties of chaos encryption, which offers high entropy and resistance to conventional attacks, with the quantum-level security provided by QKD. By integrating chaos-based encryption techniques with QKD protocols, our system achieves enhanced security for data communications in cloud environments. Furthermore, the lightweight nature of chaos encryption ensures efficient utilization of computational resources, making it suitable for cloud-based applications. This abstract outlines the theoretical framework and operational principles of the proposed security mechanism, highlighting its potential to address the evolving challenges of data security in cloud computing. © 2024 IEEE.

关键词： Cloud security

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for engineering Problems

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Journal of Bionic engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： Meta-heuristic algorithms engineering optimization Spherical evolution algorithm Global optimization

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