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Smart Devices (ICSD), International Conference on

作者： Ramnarayan Nagendar Yamsani Girija Shankar Joshi Jasjit Singh Kshitij Nautiyal Gaurav Kumar CSE Department UIT Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand Masters in Mechanical Engineering Concordia University Canada Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

Technologies are the patronage for the sustainable development and growth in the field of hospitality industry. Today’s hotel visitors are accustomed to using remote keyless entry to open and lock their vehicles, placing food orders directly from a smartphone app, and using the technology to switch on the needful thing, add something to their shopping list, or to on the Television and music also. Many of these visitors also anticipate having the same frictionless experience while staying at hotels. Prior studies tended to be preoccupied with technological service innovation, leaving human-related service innovation relatively unexplored. TBSI (Technology Based Service and Innovation) can be examine in this study to develop the sustainable application and system can have the stronger role for the customer satisfaction and delight. For the hospitality business to flourish and grow quickly in the future, we now also need to know what the customers are saying. In this study, we focus on technology and how it may help the hotel sector grow based on client input.

关键词： Industries Technological innovation TV Customer satisfaction Switches Sustainable development Smart devices Business

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Comparative Evaluation of Fraud Detection in Online Payments Using CNN-BiGRU-A Approach

Comparative Evaluation of Fraud Detection in Online Payments...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Prabhakar K M. S. Giridhar Amrita Tatia Trupti Mandar Joshi Subharun Pal Upendra Singh Aswal Department of Computer Science and Engineering School of Engineering and Technology CMR University Bangalore India Department of Electrical and Electronics Engineering Lakireddy Bali Reddy College of Engineering Jntuk Kakinada Mylavaram Andhra Pradesh India Indira School of Business Studies Pune Maharashtra India Masters of Business Administration Indira School of Business Studies Pune Maharashtra India Department of Computer Science and Engineering Indian Institute of Technology Jammu Jammu & Kashmir India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

Online payment systems are now ubiquitous in people's daily lives. However, with this increased convenience comes a new security risk. Due to the nature of cybercrime involving an online payment service, which is often characterized by specialization, diversification, concealment, industrialization, scenario, and cross-regional elements, online payment security is notoriously difficult to implement and maintain. Effective and comprehensive online payment fraud detection is crucial. The first three stages of the proposed technique are preprocessing, feature selection, and model training. Remove Null Value, Data Transform, Resample, and Normalize Data are all preprocessing techniques used to improve the quality of raw data. Feature selection makes use of PCA and IG as a means of analysis. CNN-BiGRU-A is used to train the models after information gain is used to choose relevant features. When compared to the two most common alternatives, BiGRU and CNN, the proposed technique performs better.

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Role of RPA & AI in Optimizing Network Field Services

Role of RPA & AI in Optimizing Network Field Services

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Dileep Kumar Yendluri Jainath Ponnala Reshmi Tatikonda M. Kempanna Ramya Thatikonda A. Bhuvanesh Masters in Computer Science University of the Cumberlands USA Bachelors in ECE Jawaharlal Technological University Hyderbad India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector.

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Impact of Robotic Process Automation on Enterprise Resource Planning Systems

Impact of Robotic Process Automation on Enterprise Resource ...

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Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), International Conference on th

作者： Dileep Kumar Yendluri Jainath Ponnala Ramya Thatikonda M. Kempanna Reshmi Tatikonda A. Bhuvanesh Masters in Computer Science University of the Cumberlands USA Bachelors in ECE Jawaharlal Technological University Hyderbad India PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ERP systems are merging their powers to generate a dynamic collaboration. By examining the integration between RPA (Robotic Process Automation) and ERP (Enterprise Resource Planning) systems, we can identify their shared potential for enhancing process flow. Combining automation and ERP integration makes RPA a powerful tool for process management across multiple industries. The paper examines the basics of RPA and its ERP integration, highlighting the advantages of enhanced productivity and fewer errors. Addressing security, compliance, and change management issues simultaneously. In addition to top-down and bottom-up methods, process analysis and selecting suitable processes are covered. Practical examples offer insightful observations on this integration’s success. The union of RPA and ERP creates a powerful combination that can elevate operational effectiveness, facilitate strategic insights, and reshape the workplace.

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Next generation Fruit Vending Machine using Artificial Intelligence 6

Next generation Fruit Vending Machine using Artificial Intel...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Sivasubramanian, S. Kalyana Sundaram, N. Padhi, S.N. Uike, Dipesh Uma Maheswari, B. Banupriya, V. New Prince Shri Bhavani College of Engineering and Technology Department of Information Technology Tamil Nadu Chennai600073 India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh Vaddeswaram522302 India Dr. Ambedkar Institute of Management Studies and Research Masters in Business Administration Maharashtra Nagpur440010 India St. Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600119 India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamil Nadu Karur639113 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

An automatic vending machine is designed to supply people with a variety of items, such as snacks, beverages, newspapers, and tickets without any human intervention. According to the money that is deposited into a vending machine as well as the product that has been selected by the user, the machine will determine the item and will distribute it to the user. In the proposed work, the vending machine has been designed to distribute fruits to the user as per their requirement. Classification algorithms have been used to predict the type of fruits required by the user with the help of the input provided by camera. The load cell is used to measure the kilogram or the quantity of the fruits as per the requirement by using some input peripherals like keyboard. The proposed system is also a user interactive based once. Here, there is a display device that has interfaced with the system and the display device will provide information such as the fruit which has been chosen and the quantity of the fruit that the user has entered and also shares the information on the status of the requirements. So, it will be useful for the user to know the process going in the vending machine. The raspberry pi microprocessor has employed here as a processor along the required input and output peripherals like LCD, Keypad, Load cell, camera, and motors. The machine learning algorithm like a support vector machine has been employed to predict the type of fruit as per the requirements of the user. The insertion of intelligence like machine learning algorithms in the vending machine is comparatively providing better performance. The long-term objective is to equip a vending machine solution that is both affordable and efficient, therefore boosting the shopping experience of customers and increasing the need for widespread deployment of intelligence in smart vending machines. © 2022 IEEE.

关键词： Support vector machines

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A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection

A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intellig...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Khadija Danladi Sankara G. Saritha S. Anitha Elavarasi S. Saradha D. Arul Kumar Masters of Software Engineering Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM's ability to capture temporal dependencies in sequential data and GAN's strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications.

关键词： Adaptation models Accuracy Predictive models Generative adversarial networks Data models Hybrid power systems Cyber threat intelligence computer security Long short term memory Anomaly detection

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AI-Based Risk Management in Financial Services

AI-Based Risk Management in Financial Services

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Madhavi Katamaneni Prateek Agrawal Sathiesh Veera Ashok Kumar Sahoo Kawerinder Singh Sidhu Mohammed Faez Hasan Information Technology Vrsiddhartha Engineering College Masters of Computer Applications Gurukula Kangri (Deemed to be University) BTech: Bachelor of Engineering in Communications Science & Engineering Anna University Kalasalingam Academy of Research and Education (A Deemed University) Tamilnadu UIM Uttaranchal University Dehradun Uttarakhand Finance Finance and Banking Department Kerbala University Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

When it comes to the financial services industry, efficient risk management is very necessary to guarantee stability and compliance in the face of constantly shifting market conditions. The purpose of this research study is to investigate the potential roles that artificial intelligence (AI) could play in improving risk management methods throughout financial organizations. To identify, evaluate, and mitigate financial risks, we investigate a variety of artificial intelligence techniques, such as machine learning algorithms, natural language processing procedures, and predictive analytics. Artificial intelligence models can deliver real-time insights and prediction skills that traditional approaches frequently lack. These capabilities are achieved through the analysis of historical data, transaction patterns, and market trends. Within the scope of this research project, the effectiveness of AI-driven approaches is compared to that of traditional risk management tactics. Particular attention is paid to enhancements in terms of accuracy, efficiency, and scalability. According to the findings, artificial intelligence dramatically improves risk detection and management, providing financial institutions with more powerful tools to tackle regulatory obstacles and uncertainties. This research highlights the revolutionary potential of artificial intelligence in redefining risk management frameworks, thereby paving the way for financial risk mitigation measures that are more proactive and data-driven.

关键词： Industries Uncertainty Scalability Predictive models Stability analysis Real-time systems Risk management Artificial intelligence Financial services Risk mitigation

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ITCLab PID Control Tuning Using Deep Learning 9

ITCLab PID Control Tuning Using Deep Learning

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Rahmat, Basuki Waluyo, Minto Rachmanto, Tuhu Agung Agussalim Pratama, Alfan Rizaldy Muljono, Muljono Diyasa, I Gede Susrama Mas Swari, Made Hanindia Prami Hendrasarie, Novirina Universitas Pembangunan Nasional 'Veteran' Jawa Timur Information Technology Masters Department Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Industrial Engineering Department Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Environmental Engineering Department Surabaya Indonesia Center for Research and Innovation on Advanced Transportation Electrification Politeknik Elektronika Negeri Surabaya Indonesia Universitas Dian Nuswantoro Computer Science Department Semarang Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Informatics Department Surabaya Indonesia

ISBN: (纸本)9798350306835

This paper presents a novel approach for tuning Proportional-Integral-Derivative (PID) controllers using deep learning techniques, specifically applied to the iTCLab, an advanced industrial control laboratory system. PID controllers are widely used in industrial processes for their simplicity and effectiveness. However, their performance heavily relies on proper tuning, which can be a complex and time-consuming task. In this study, we leverage the capabilities of deep learning to automate and optimize the PID tuning process for the iTCLab setup. We propose a data-driven methodology that combines system identification, Deep Learning networks, and optimization algorithms to achieve superior PID controller tuning. © 2023 IEEE.

关键词： Three term control systems

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Deep learning for audio

TechRxiv

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TechRxiv 2020年

作者： Sai Priyanka, Kotha Sravani, Nallagari Fiadhi, Jinan Masters in Computer Science Lakehead University Canada

Speech is the most efficient and convenient way of communication. The learning capabilities of the deep learning architecture can be used to develop the sound classification system to overcome the efficiency issues of... 详细信息

关键词： Speech communication

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Capsule networks: An alternative approach to image classification using convolutional neural networks

TechRxiv

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TechRxiv 2020年

作者： Sood, Karan Fiaidhi, Jinan Masters in Computer Science Lakehead University Canada

Convolutional neural networks tend to lose lot of information about the image due to their pooling operation, which leads them to misclassify images. In this paper, I discuss how we can preserve the information about the various aspects of the image such as translational information, rotational information etc. by using Capsule networks suggested by the godfather of deep learning- Geoffrey Hinton. © 2020, CC BY-SA.

关键词： Convolutional neural networks

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