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2023 IEEE International Conference on Innovative Data communication Technologies and Application, ICIDCA 2023

作者： Sangeetha, A. Krishnan, R. Santhana Kumar, G. Praveen Julie, E. Golden Ganesh, R. Karthik Robinson, Y. Harold PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India V.S.B Engineering College Department of Information Technology Karur India Anna University Department of Computer Science and Engineering Regional Campus Tirunelveli India SCAD College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Swimming has become one of the major routines nowadays among people. People mostly use swimming pools for exercise or recreation. According to statistics one hour of swimming burns 30% more calories than one hour of running. So many people prefer swimming to keep their body fit. But few people use the swimming pool without having proper swimming skills. This leads to loss of their lives. Around the world nearly 42 people drown per hour. The pH level of water in the swimming pool should be between 7.4 to 7.6. The high pH water can cause rashes on human skin and scaling on pool equipment. Low pH causes eye irritation, skin irritation etc. Hence, a smart system is required to monitor the pH level of the water and to track the condition of the swimmers inside the pool. So with the support of few sensors and Arduino, an automatic swimming pool monitoring system, which is capable of sensing the pH level of the water available in swimming pool and condition of the swimmers is developed. The proposed system will also update all the observed status in the cloud with the support of the Internet facility. © 2023 IEEE.

关键词： Swimming pools

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Optimising Business Processes in Project Tracking using CRM 2

Optimising Business Processes in Project Tracking using CRM

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Ramprasath, J. Saranya, N. Arun Kumar, M. Chandru Vignesh, C. Ramprakash, P. Krishnaraj, N. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of AI & DS Coimbatore India

ISBN: (纸本)9798331534950

Customer relationship management, called CRM, is essential for businesses looking to sustain profitable growth in today's fiercely competitive business environment. This essay examines cutting-edge methods to improve clients in the digital era and thoroughly explains CRM practices. The research examines best-serving customers through various touchpoints by integrating data analytics, AI, and personalized marketing strategies. In addition, it explores the role of organizational alignment and a customer-centric culture in developing enduring customer relationships. The study uses theoretical frameworks and practical data to highlight the main obstacles and changes associated with implementing efficient CRM procedures. As a result, synthesizing pertained research and case studies provides insights into new trends and industry best practices for building long-lasting relationships with customers. The results highlight the significance of proactive involvement, ongoing innovation, and ethicizing deliberations in fostering trust and allegiance across various consumer categories. This paper adds to the current conversation on CRM by providing practice advice that companies can use to adjust to and prosper in a constantly changing market. © 2024 IEEE.

关键词： Sales

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Real-Time Deep Learning for Dynamic Scene Understanding and Autonomous Robot Navigation

Real-Time Deep Learning for Dynamic Scene Understanding and ...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Muthukrishnan, A. Pushparani, S. Poongavanam, N. Arun, M. Ilangovan, S. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Saveetha University Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India K.L.N.College of Engineering Department of Information Technology Sivaganga India

ISBN: (纸本)9798331542344

In this paper, a new approach in dynamic scene understanding and robot navigation especially for autonomous robots through the integration of deep learning approach with real time processing is proposed. For object recognition and its localization on the image the system uses convolutional neural networks, also semantic segmentation relies on convolutional neural networks, for autonomous navigation, the deep reinforcement learning is used. The data for the research are gathered from publicly available databases and new data captured with web-cameras and LiDAR sensors installed on robots exploring the chosen scenarios. The deep learning models are for real time usage by using TensorFlow Lite and the models are run on Jetson Xavier. In order to investigate object detection efficiency, scene segmentation differentiate, success rate of providing navigation and system latency, various experiments were performed inside and outside the building. The outcomes also show a high degree of 'indoor' stability and an interacting environment obstacle avoidance efficiency over real-Time system operation. While these difficulties were noticed in rural outdoor environment, the system managed to navigate in space populated with dynamic obstacles and safely avoid them even if environment conditions changed, for instance, due to wind. Deep learning with sensor fusion as a solution offered accurate scene understanding to make sure that autonomous navigation is possible on both indoor and outdoor settings. Thus, the research provides valuable information on the improvement of safety and efficiency in autonomous robots for use in various industries such as self-driving cars, robots for a smart warehouse etc. © 2024 IEEE.

关键词： Convolutional neural networks

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Software Reliability Assessment Using Hybrid Neuro-Fuzzy Model

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computer Systems Science & engineering 2022年第6期41卷 891-902页

作者： Parul Gandhi Mohammad Zubair Khan Ravi Kumar Sharma Omar H.Alhazmi Surbhi Bhatia Chinmay Chakraborty Department of Computer Applications Manav Rachna International Institute of Research and StudiesFaridabad121006India Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia Department of Computer Applications Chandigarh Group of CollegesLandran140307India College of Computer Sciences and Information Technology King Faisal UniversitySaudi Arabia Department of Electronics&Communication Engineering Birla Institute of TechnologyMesra JharkhandIndia

Software reliability is the primary concern of software developmentorganizations, and the exponentially increasing demand for reliable softwarerequires modeling techniques to be developed in the present era. Small unnoticeable drifts in the software can culminate into a disaster. Early removal of theseerrors helps the organization improve and enhance the software’s reliability andsave money, time, and effort. Many soft computing techniques are available toget solutions for critical problems but selecting the appropriate technique is abig challenge. This paper proposed an efficient algorithm that can be used forthe prediction of software reliability. The proposed algorithm is implementedusing a hybrid approach named Neuro-Fuzzy Inference System and has also beenapplied to test data. In this work, a comparison among different techniques of softcomputing has been performed. After testing and training the real time data withthe reliability prediction in terms of mean relative error and mean absolute relativeerror as 0.0060 and 0.0121, respectively, the claim has been verified. The resultsclaim that the proposed algorithm predicts attractive outcomes in terms of meanabsolute relative error plus mean relative error compared to the other existingmodels that justify the reliability prediction of the proposed model. Thus, thisnovel technique intends to make this model as simple as possible to improvethe software reliability.

关键词： Software quality reliability neural networks fuzzy logic neuro-fuzzy inference system

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Investigating the Benefits of Text Mining for information Analysis

Investigating the Benefits of Text Mining for Information An...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Singh, Vaishali Venkadeshwaran, K. Jha, Gaurav Arunadevi, R. Sharma, Yogesh K. Maheswaran, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Textual content mining is the manner of extracting significant information and insights from large volumes of unstructured textual information. Many businesses are beginning to recognize the capacity of text mining in gaining effective insights and presenting greater accurate statistics. This paper will talk the ability blessings of using text mining strategies in data analysis. It's going to present quite number possible applications, such as herbal language processing, sentiment evaluation, subject matter modeling, and predictive analytics. It also evaluation the numerous technical techniques and algorithms used for textual content mining, together with lateens semantic indexing, and will look at how text mining can be used to guide a range of various commercial enterprise targets. Sooner or later, the paper will address how text mining can help pick out useful styles and traits in unstructured information, and the way it may be utilized in tandem with conventional data processing methods to achieve higher effects. This paper will finish by way of analyzing the capacity challenges that may be encountered when using text mining and how ability dangers can be mitigated. © 2023 IEEE.

关键词： Semantics

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Origins to Innovations: A Comprehensive Journey Into Artificial Intelligence's Future Application

Origins to Innovations: A Comprehensive Journey Into Artific...

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Saraswat, Shivam Mehra, Ritika Gurpreet Yuvaraj, K. Karthigaikumar, P. Wankhede, D.S. Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Haryana Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India Department of Computer Applications Invertis University Uttar Pradesh Bareilly India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Karpagam College of Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350355338

In the contemporary era characterized by the proliferation of extensive data sets and the advent of Industry 4.0, artificial intelligence (AI) emerges as a prominent catalyst for economic growth. It assumes a pivotal role in facilitating the integration of advanced technologies like bitcoin, virtualization, the Internet of Things (IoT), and graphics processors, hence fostering innovation and development. In this scholarly essay, a comprehensive examination of artificial intelligence (AI) and learning methodologies spanning the years 1961 to 2018 is conducted. Through an extensive examination of AI, encompassing various aspects such as causal factors, practical systems, fundamental machine learning, commercial achievements, and current advancements, this investigation serves as a highly valuable resource for both academic scholars and industry experts. There is a substantial number of challenges associated with artificial intelligence (AI). However, it is undeniable that AI has seen significant advancements, emerging as an innovative and transformative tool across all domains and applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Developing Novel Approaches for Implementing Machine Learning in Medicine 3

Developing Novel Approaches for Implementing Machine Learnin...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Mercilin Raajini, X. Inamdar, Fatima M. Kannagi, A. Agrawal, Trapty Vishnoi, Manish Yuvaraj, K. Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Civil Engineering Jaipur India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350319125

Recent advances in computing hardware and software program have enabled the improvement and deployment of state-of-the-art system getting to know (ML) models in diverse scientific domain names. ML has the capacity to leverage large volumes of affected person facts to generate insights and actionable answers, allowing unique prognosis and treatment choices. In spite of promising ability, enforcing ML in medicine has been hard because of numerous realistic problems, inclusive of records bias and series technology, lack of robust evaluation frameworks, and ethical and privacy worries. To cope with these demanding situations, researchers have begun to expand novel methods to allow green and powerful ML deployment in medical practice. these tactics contain leveraging novel records resources such as wearable's and glide cytometry, the use of federated studying algorithms for privacy-preserving version development, and incorporating multi-level evaluation metrics which include explain ability and fairness. Together, these novel methods provide a roadmap for correctly and safely deploying ML in medication to higher help and enhance patient care. © 2023 IEEE.

关键词： computing generate improvement medication patient

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Forest Fire Detection and Temperature Monitoring Alert using IoT and Machine Learning Algorithm 5

Forest Fire Detection and Temperature Monitoring Alert using...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Venkataramanan, V. Kavitha, G. Joel, M. Robinson Lenin, J. D.J Sanghvi College of Engineering Department of Electronics and Communication Engineering Maharashtra Mumbai India Department of Computer Science and Engineering Tamil Nadu Namakkal India Kings Engineering College Department of Information Technology Tamil Nadu Chennai India Al Musanna College of Technology Department of Information Technology Muladdah Muscat Oman

ISBN: (纸本)9781665474672

Forest fires are a prevalent hazard in forests that significantly damage wildlife and the environment. It may be averted if a comprehensive system is installed in forest regions to detect fires and inform firefighting authorities to take timely action. The goal of this project is to create an Internet of Things (IoT)-based real-time detection system that detects fires and sends emergency notices to authorities. A GSM/GPRS module interacts with an IoT server because network bandwidth is typically very poor or nonexistent in forest regions. As a result, a 2G network is ideal for communicating with the server. A real-time fire monitoring system that differentiates fire and smoke is used to identify an actual fire occurrence. The nano-based Atmega328 IoT gateway identifies the forest's fire as soon as possible and acts rapidly before it spreads across a large area. Here, this uses machine learning algorithms to detect the event of a fire in a large region. The system uses a flame sensor to detect the flame and a temperature sensor to measure the temperature in a particular area. By using the machine learning method, the system can easily give the result with actual detection and intimate the authority about the temperature condition. The data are then sent to the cloud-based application. In the event of an unusual rise in forest temperature, this will alert the forest authorities and sound the fire alarm. It can also predict future fires by using machine learning. This is accomplished using the fog computing method. Due of the sensors' efficiency, this might potentially be applied in industrial settings. Any type of forest can make use of it. From this experiment, this research study deduced that it has a remarkable accuracy of 98% in predicting forest fires. As a result, the possibility of a false alarm is significantly decreased. © 2023 IEEE.

关键词： Fires

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Improving Bandwidth Utilization for Underwater Fiber Optic Networks Through Intelligent Optimization Techniques

Improving Bandwidth Utilization for Underwater Fiber Optic N...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Sakhare, Nitin N. Yadav, Rakesh Kumar Rajesh, A. Hassan, Inzimam Ul Vinithra Banu, T. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology UP India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

Underwater Fiber Optic Networks (UFONs) are getting increasingly more important because of the emergence of the marine economic system, the enlargement of ocean exploration sports, and the need for reliable net get entry to. However, their bandwidth utilization is negative due to insufficient useful resource allocation techniques, constrained community design facts, and restricted expertise about consumer traffic styles. This paper proposes clever optimization techniques to improve the bandwidth usage of UFONs. These strategies are primarily based on gadget studying strategies along with neural networks and particle-swarm optimization. Numerous performance metrics are used to assess the proposed techniques. Simulations and experiments are performed to compare the overall performance of the proposed techniques with conventional techniques. Effects show the proposed smart optimization strategies are able to enhancing the bandwidth utilization of UFONs by means of as much as sixteen%. The proposed shrewd optimization strategies can provide significant enhancements for actual-time aid allocation, dynamic network control, and monitoring of underwater traffic styles. © 2023 IEEE.

关键词： Particle swarm optimization (PSO)

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Dynamic Scheduling Algorithms for Serverless Computing Solutions in the Cloud

Dynamic Scheduling Algorithms for Serverless Computing Solut...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Mohammed, Abdul Sajid Saddi, Venkata Ramana Gopal, Santhosh Kumar Jiwani, Nasmin Logeshwaran, J. School of Computer and Information Sciences University of The Cumberlands KY United States Ace American Insurance Chubb Group RaleighNC United States Sciences CharlotteNC United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350394399

Server less computing solutions within the cloud presentsan exceptional opportunity for corporations to gain progressed scalability, availability, and fee efficiency. To reap such blessings, establishments need to utilize the handiest dynamic scheduling algorithms that are tailor-made to their unique needs. These algorithms can include predictive scheduling, call for-driven scheduling, and application-conscious scheduling, among others. Predictive scheduling algorithms are looking for to expect capacity call for to prevent erratic performance. This study focuses on dynamic scheduling algorithms for serverless computing solutions in the cloud. The researchers explore the characteristics of serverless computing models and the challenges of dynamic scheduling. A comprehensive evaluation is conducted on various scheduling algorithms, taking into consideration performance metrics such as throughput, response time, and resource utilization. The results show that dynamic scheduling algorithms are effective in optimizing resource allocation and improving overall system performance. Specific values derived from the results include a significant reduction in resource wastage, improved scalability, and increased cost-effectiveness. These findings suggest that dynamic scheduling algorithms are crucial for efficient and scalable serverless computing solutions in the cloud. With the aid of applying the maximum suitable dynamic scheduling algorithms tailor-made to precise desires, corporations could be higher prepared to fulfill their formidable cloud-computing dreams. © 2024 IEEE.

关键词： Scheduling algorithms

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