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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Lande, Jayapal Syamala, M. Priyadarsan, Padhyala Sunil Kumar, V. Global-A Genpact Company Bangalore India Sr University School of Computer Science & Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

In social media platforms, there is a growing interest in developing automated solutions for toxicity classification using machine learning algorithms. This paper presents a novel approach to automated toxicity classification using semantic topic models, achieving high accuracy and transparency. The report emphasizes the importance of detecting and adjusting for biases to ensure fair categorization. Moreover, the report highlights the challenges of natural language processing tasks, importance of understanding and addressing potential biases in machine learning models. Datasets are produced by the Origami project at Google, labeled by human labelers with adjectives such as 'toxic,' 'very poisonous,' 'accusation,' 'danger,' 'obscene,' and 'identity hatred.' From the analysis, it shows that proposed method achieved better results on toxicity classification in terms of accuracy (0.85), precision (0.87), recall (0.84) and f1-score (0.85) respectively. For non-toxicity classification, the proposed method achieves accuracy (0.92), precision (0.89), recall (0.88), f1-score (0.88) and AUC-ROC (0.96) respectively. © 2023 IEEE.

关键词： Latent Dirichlet Allocation Machine Learning Methods Natural Language Processing Semantic Topic Models Social Media Safety Support Vector Machine Toxic Comments

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Big Data Analytical Engine for Exploring the Power in Modern Business

Big Data Analytical Engine for Exploring the Power in Modern...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Enquero, Shreya Patchala Padhi, Manoj Kumar Yamsani, Nagendar Anitha, M. Revanasiddappa, Revanasiddappa Global-A Genpact Company Bangalore India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women It Department Hyderabad India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Most current applications of Big Data focus on examining consumer preferences and feedback expressed through social media and legitimate websites. Data is generated by all parties involved in the company, including mobile device location data and data created by machines with high-tech sensors, automated controls, and relevant software. We are currently living through an Internet revolution fueled by Big Data following the Industrial and Internet revolutions. Therefore, the implementation of Big Data platforms represents a significant opportunity for enterprises today. This article presents three case studies that highlight the potential of Big Data in the cellular phone business, online retail, and insurance sales over the Internet. To help companies locate, gather, and analyze huge amounts of data, a Big Data Analytical Engine has been proposed. From the analysis, it clearly shows that proposed model achieves better results in terms of accuracy (0.92), precision (0.93), recall (0.91) and f1-score (0.92) respectively. © 2023 IEEE.

关键词： Big Data Analytics Comparative Study Customer Churn Data Preprocessing Industry-Specific Recommendations Machine Learning Algorithms Performance Evaluation

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including ai-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

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Futuristic Authentication: Development of an Artificial Intelligence based Enhanced Face Detection and Recognition System 7

Futuristic Authentication: Development of an Artificial Inte...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rama Satish, K.V. Jadhav, Dattatraya Arun Siva Prasad, M.V. Gopal Thatipudi, Jayendra Tejasri, V. Divya, J. Rnsit Dept of Ai Ml Bengaluru India Keystone School of Engineering Electronic And Telecommunication Department Pune India Anurag Engineering College Department of Cse Kodad India University of North Texas Dept. of Computer Science India S.R.K.R.Engineering College Department of It Chinaamiram Andhra Pradesh Bhimavaram India R.M.K.Enginnering College Dept of Computer Science And Design India

ISBN: (纸本)9798350359299

This research presents the design and implementation of an advanced Face Detection and Recognition System employing an Artificial Intelligence Assisted Face Recognition Model (aiFRM). The proposed system leverages the powerful Cascaded VGG16 architecture to enhance the accuracy of face detection and recognition tasks. The methodology involves a two-step approach, beginning with robust face detection using the cascaded VGG16 model. This is followed by feature extraction and recognition using the same architecture, resulting in a highly efficient and accurate facial recognition system. The cascaded VGG16 model is trained on diverse datasets to ensure adaptability to various scenarios and lighting conditions, enhancing the system's real-world applicability. The experimental results demonstrate the effectiveness of the proposed system in achieving state-of-the-art accuracy in face detection and recognition. The cascaded VGG16 model proves to be a formidable backbone for the aiFRM, contributing to the system's success in handling complex scenarios. The system's high accuracy, reaching 97%, positions it as a reliable solution for security, surveillance, and authentication applications in diverse environments. © 2024 IEEE.

关键词： Face recognition

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Rural Ecosystem Monitoring in Food Security Analysis Based on Sustainable Agriculture: Artificial Intelligence Application

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 56-64页

作者： M, Mohideen AbdulKader Kumaran, M. Senthil Keerthika, Vijay Reddy, Polu Srinivasa Rajendra, Alla R, Subbulakshmi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India Department of Artificial Intelligence and Machine Learning (AI & amp ML) MLR Institute of Technology Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad 500043 India Department of Civil Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore 641105 India

The ecological security patterns (ESPs) in the Qinling Mountains are facing significant challenges due to the worldwide issues of harsh climate and urbanization. Achieving sustainable development in China requires an understanding of the features of ESPs in the Qinling Mountains, an important ecological barrier. With a Yangxian focus, this work makes use of techniques like geographic information systems (GISs), remote sensing, and machine learning (ml). This research proposed a novel technique in rural region ecosystem monitoring-based food security based on sustainable agriculture using machine learning in artificial intelligence application. Here, input is collected as food security analysis for rural region ecosystem monitoring as well as processed for noise removal and normalization. Then, this data features are extracted and classified using reinforcement radial Gaussian encoder with adversarial Boltzmann temporal neural networks. Experimental analysis is carried out for parameters like training accuracy, random precision, sensitivity, AUC, and F-1 score. The proposed method obtained 98% of training accuracy, 96% of sensitivity, 95% of F-1 score, 97% of AUC, and 93% of random precision. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence application Ecosystem monitoring Food security Machine learning Rural region Sustainable agriculture

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support Systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Experimentation Analysis of VQC and QSVM on Sentence Classification in Quantum Paradigm 1

Experimentation Analysis of VQC and QSVM on Sentence Classif...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Sunkavalli, Jayaprakash Rao, B. Madhav Basu, M. Trinath Sharma, Harish Dutt Kumar, P. Ashok Anand, Ketan Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India SIR C R Reddy College of Engineering Department of CSE Andhra Pradesh Eluru India KL University Department of CSE TS Hyderabad India Uttaranchal School of Computing Sciences Uttaranchal University Uttarakhand Dehradun India TS Hyderabad India Sreenidhi institute of Science and Technology Department of AI&ML TS Hyderabad India

ISBN: (纸本)9798350353648

The quantum computing made complex computations may be executed at light speed, superpositions allow for parallelism, and massive quantities of data can be efficiently processed. Furthermore, researchers and engineers are quickly becoming interested in natural language processing (NLP) as a topic for developing more comprehensive computational models of NLP. Therefore, there is hope for future progress in NLP efforts that use quantum technology, especially in emotion classification. In this study, we use quantum computing models to delve deeply into two sentiment categorization approaches. To be more specific, the effectiveness of the models is evaluated by looking at VQC and QSVM. The analyzed models are evaluated by simulations in the Qiskit simulator and the real-time quantum computer Qiskit_ibm_osaka. Results demonstrate the importance of the experiment and validate it using positive predicted value, sensitivity and Fl-Score as performance metrices. © 2024 IEEE.

关键词： Quantum computers

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Real-Time IoT Cybersecurity using Machine Learning-based ai Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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Innovative Approaches in Network Architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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Optimizing IoT Threat Mitigation with Artificial Intelligence in Banking: A Multi-Objective Approach 2

Optimizing IoT Threat Mitigation with Artificial Intelligenc...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Pratap Singh Chauhan, Rudra Sonker, Sanjav Kumar Kaur, Manpreet Sharma, Chhaya Singh, Robin Singh, Ramendra Shri Shankracharya Institute of Professionals Management and Technology Department of CSE-AI and Ai Ml Raipur India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Institute of Computing Chandigarh University Gharuan Mohali India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering Ghaziabad India Graphic Era Hill University Dehradun India Ghaziabad India

ISBN: (纸本)9798350371055

This research introduces a novel ai-based mechanism for optimizing threat mitigation in IoT banking systems, addressing the growing vulnerabilities in this critical sector. The proposed mechanism, characterized by a precision of 0.88 and a balanced recall of 0.79, offers a robust defense against cyber threats. Leveraging a Deep Neural Architecture known as Pointer Networks, the mechanism adapts dynamically, ensuring high accuracy in threat identification (precision) while comprehensively covering potential threats (recall), resulting in a harmonious F1 score of 0.83. Through extensive threat-specific evaluations, the mechanism proves versatile, exhibiting high performance in scenarios involving malware (precision: 0.89, recall: 0.82, F1 score: 0.85), denial of service (DoS) attacks (precision: 0.87, recall: 0.78, F1 score: 0.82), and unauthorized access attempts (precision: 0.90, recall: 0.81, F1 score: 0.85). Scalability testing further validates its practical applicability, maintaining precision and F1 score values across varying sizes of IoT ecosystems. This research establishes the proposed mechanism as a potent and adaptable cybersecurity tool, poised to fortify the resilience of IoT banking systems against the dynamic landscape of cyber threats. © 2024 IEEE.

关键词： Artificial Intelligence Banking Cybersecurity IoT Threat Mitigation

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