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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Priya Shirley Muller Mudarakola Lakshmi Prasad Poornima A Sikrant Pundru Chandra Shaker Reddy Swati Sharma Nipun Sharma Department of Mathematics Sathyabama Institute of Science and Technology Chennai India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India Sri Ramachandra Faculty of Management Sciences Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India School of Computer Science Engineering and Information Science Presidency University Bangalore Karnataka India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Worldwide, breast cancer ranks high among women's leading causes of mortality. The likelihood of mortality from breast cancer can be decreased with early detection and rapid treatment. Among those residing in rural regions with limited access to healthcare, machine learning on the cloud is currently playing a crucial role in illness diagnosis. Machine learning-based diagnostic systems can supplement radiologists' work as secondary readers to ensure accurate illness diagnosis, while cloud-based systems can facilitate remote diagnostics and telehealth services. The potential of artificial neural networks (ANN)-based techniques for illness diagnosis has piqued the interest of numerous academics. Among the many ANN versions, extreme learning machine (ELM) stands out as a promising candidate for tackling a wide range of classification issues. Three distinct areas of study are brought together in the suggested framework of this work: The first area where ELM is used is in the detection of breast-cancer. Next, the gain-ratio feature selection strategy is utilized to remove features that are not significant. Finally, this research offers an ELM-based CC-approach for remote breast-cancer diagnostics. Several cutting-edge technologies for illness diagnosis are contrasted with the cloud-based ELM's performance. The outcomes obtained on the Wisconsin-Diagnostic-Breast-Cancer(WBCD) dataset demonstrate that the ELM technique hosted in the cloud surpasses alternative approaches. When comparing ELM's performance in both the standalone and cloud contexts, the former proved to be superior.

关键词： Cloud computing Technological innovation Accuracy Extreme learning machines Telemedicine Mortality Medical services Artificial neural networks Breast cancer Classification algorithms

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Application of BiLSTM-CRF Approach and its Application for Better Decisions in Human Resource management Processes

Application of BiLSTM-CRF Approach and its Application for B...

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

作者： Umanesan, R. Shunmugasundaram, M. Rani, Pushpa Balasubramanian, K. Rakesh, K. Chandel, Pankaj Singh Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Management Studies Chennai India Tecnia Institute of Advanced Studies Department of Management Sciences Delhi India Kalasalingam Academy of Research and Education-Deemed to Be University Department of Computer Applications India Malla Reddy Engineering College for Women Department of Ece Telangana India Dev Sanskriti Vishwavidyalaya Department of Tourism Management Haridwar India

ISBN: (纸本)9798350313987

Recent advances in information technology (IT) have had a profound effect on human resources (HR) procedures and practices. Despite this, there is a dearth of research on its effectiveness, and the studies that have been undertaken have not, in general, assessed how well these new technologies aid firms in attaining their HR goals of attracting, motivating, and retaining personnel. Current systems have limitations, such as being one-way, passive, impersonal, limiting opportunities for human connection, and creating too much physical and intellectual barrier between individuals and enterprises. Data preparation, feature extraction, and model training are the first three stages of the suggested procedure. Normalization is a tool used in the preliminary processing of data. PCA is used to extract features and identify the most informative ones. After feature extraction, the models are trained using BiLSTM-CRF. When compared to the two most common alternatives, BiLSTM and CRF, the proposed technique achieves better performance. © 2023 IEEE.

关键词： Bidirectional Long Short Term Memory (BiLSTM) Extreme Learning Machine (ELM) Principal Component Analysis (PCA

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Leveraging Deep Learning for COVID-19 Detection: A Comparative Study Using ResNet Models on X-Ray Images

Leveraging Deep Learning for COVID-19 Detection: A Comparati...

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Disha Tuteja Anurag Sinha Deepak Chandra Uprety Dyuti Banerjee Ayushi Dewan Chetna Sharma Department of ECE College Guru Tegh Bahadur Institute of Technology Delhi India Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering (CS) School of Computer Science in Emerging Technologies Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh India Rawal Institutions Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (AI) and machine learning (ML) offer promising possibilities. In medical diagnostics, AI, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic.

关键词： COVID-19 Deep learning Measurement Technological innovation Accuracy Pandemics Medical services Public healthcare X-ray imaging Testing

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Cloud Computing Applications for Protecting the Information of Healthcare department Using Smart Internet of Things Appliance 5

Cloud Computing Applications for Protecting the Information ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Jain, Amit Kumar Ross, Dhyana Sharon Babu, M. Kishore Dharamvir Uike, Dipesh Gangodkar, Durgaprasad Delhi University Department of Computer Science Delhi India Tamil Nadu Chennai India Koneru Lakshmaih Education Foundation KL Business School Andhra Pradesh Guntur India The Oxford College of Engineering Department of MCA Bangalore India Dr. Ambedkar Institute of Management Studies and Research Maharashtra Nagpur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Combining cloud computing and Internet of Things concept is the emerging trend for online management efficiently and the sensor data processing. Pervasive healthcare sector or using the Internet of Things paradigm and the study offers the platform the disc based on cloud computing for the administration of information healthcare department. The development of clear and organized healthcare monitoring systems is made possible by the Cloud IoT convergence of the Cloud and IoT. Different types of healthcare applications use various IoT based and cloud-based application for transmission of data and to connect with each other that enables the provision of clinical healthcare solutions effectively. The research study proposes a novel hybrid system that uses IoT smart appliance using metaheuristic swarm intelligence technique for storing data into the cloud then to balance the load with real-time providers at low-cost that receives data from the sensor devices of hospital and stored in cloud. The computer resources are revolutionizing algorithms for individualized inference and actions in health management and the data is protected using blockchain technology. © 2022 IEEE.

关键词： Internet of things

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Cryptocurrency and Data Privacy in Human Resource management 3

Cryptocurrency and Data Privacy in Human Resource Management

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3rd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2023

作者： Chandrasekeran, Indhumathi Dharmaraj, A. Juyal, Ashima Shravan, M. Deb Barman, Rajesh Lourens, Melanie Department of Commerce with Computer Application PSG College of Arts & Science CoimbatoreTamil Nadu India Department of Management Karpagam Academy of Higher Education CoimbatoreTamil Nadu India Uttaranchal Institute of Technology Uttaranchal University India School of Business SR University WarangalTelangana India Department of Commerce Bodoland University Kokrajhar BTR Assam783370 India Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (纸本)9798350399264

Cryptocurrency and data privacy are two important considerations in human resource management. Cryptocurrency, such as Bitcoin, can be used to pay employees, provide incentives, and manage payroll, but it also poses risks such as price volatility and regulatory issues. Data privacy, on the other hand, is the protection of personal information from unauthorized access and use. The use of blockchain technology, which is the underlying technology behind many cryptocurrencies, can enhance data privacy in human resource management by providing a secure and tamper-proof record of all transactions. However, it's important to comply with all applicable laws and regulations related to the use of cryptocurrency and data privacy in human resource management. © 2023 IEEE.

关键词： Human resource management

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Integration of Genetic Algorithm and Support Vector Machine for Water Discharge Prediction in River Basin System

Integration of Genetic Algorithm and Support Vector Machine ...

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Disruptive technologies (ICDT), International Conference on

作者： Arvind Yadav Haripriya Mishra Ashwini Kumar Pradhan Basant Sah Devendra Joshi Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Civil Engineering Einstein Academy of Technology and Management Khurda Odisha India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Civil Engineering Dr. Vishwanath Karad MIT World Peace University Kothrud Pune India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Water discharge (WD) is essential component of river hydrodynamics. It is crucial to predict WD for constructing reservoirs and dams, operations, management and river engineering studies. WD includes various complex nonlinear processes which depends on many variables. The WD prediction is complicated using traditional methods because these are unable to handle the complex nonlinear processes of it. WD prediction using novel artificial intelligence (AI) techniques is highly encouraged for its effectiveness in flood forecasting. A hybrid AI approaches is useful for WD prediction in Mahanadi River (MR), India. Support vector machine (SVM) is integrated with Genetic algorithm (GA) (GA-SVM) for optimization of all associated SVM parameters. Temperature(T), rainfall (R), water level (WL) and sediment yield (SY) are used as inputs parameters for prediction of WD. The standard statistical measures are used for the performance evaluation of GA-SVM model. Hybrid GA-SVM with automated parameter tuning model is developed for prediction of WD at Tikarapara location. It is outermost downstream station in the MR using hydro-climatical parameters. The developed GA-SVM model is compared to SVM, and multiple linear regression (MLR) to evaluate the ability of the models to predict the WD. The proposed GA-SVM model is provided satisfactory and better results for estimation of WD in the MR among all comparative models. This modelling approaches can be applied for the prediction of WD at gauge or ungauged places in MR due to its easiness in implement and satisfactory results.

关键词： Support vector machines Rain Estimation Predictive models Time measurement Rivers Artificial intelligence Optimization Tuning Genetic algorithms

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A new rung orthopair fuzzy SIR method with a multi-criteria decision-making approach

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Granular Computing 2024年第2期9卷 1-18页

作者： Shahzadi, Gulfam Luqman, Anam Shahzadi, Sundas Deveci, Muhammet Department of Mathematics University of Management and Technology Sialkot Campus Pakistan Division of Science and Technology Department of Mathematics University of Education Lahore Pakistan Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

In comparison to Fermatean, Pythagorean, and intuitionistic fuzzy sets, $$(p, q)-$$ rung orthopair fuzzy sets have a wider range of displaying membership grades and can therefore provide more uncertain situations. In this work, the accuracy of $$(p, q)-$$ rung orthopair fuzzy numbers is investigated using sine trigonometric functions. First, the $$(p, q)-$$ rung orthopair fuzzy data are extended to the sine trigonometric operational laws (STOLs). In this study, we suggest a novel $$(p, q)-$$ rung orthopair fuzzy superiority and inferiority ranking (SIR) approach to address the uncertainty group multiple-attribute decision-making (MADM) problem. This strategy handles unclear information, incorporates individual perspectives into group viewpoints, makes conclusions based on many criteria, and ultimately structures a specific decision map. The proposed SIR method utilizes two kinds of information, the superiority and the inferiority information, to obtain two types of flows, including the superiority and the inferiority flows. Then, these flows are utilized to rank the set of alternatives partially or completely. Using sine trigonometric functions and the flexibility of $$(p, q)-$$ rung orthopair fuzzy sets, novel STOLs have been created. Further, we conduct a case study of the selection of the best journal to demonstrate the feasibility and applicability of the developed technique. The main contributions of this article are as follows: (1) The aggregation operators for $$(p, q)-$$ rung orthopair fuzzy numbers and their characteristics have been studied under sine trigonometric functions. (2) The SIR approach has been developed under $$(p, q)-$$ rung orthopair fuzzy sets. The proposed technique is explained through a step-by-step Algorithm. (3) Then, a case study of journal selection is considered to apply the developed technique. (4) The results obtained have been compared with the ranking obtained through various existing techniques.

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Refining Pancreatic Cancer Detection and Classification Through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis

Refining Pancreatic Cancer Detection and Classification Thro...

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International Conference on Advances in Information Technology (ICAIT)

作者： P Arunachalam ME M. Kalpana Devi Naduvathezhath Nessariose Jose Vivekanandan G Rinku Mahesh Sharma Ganesh Kumar Yadav Department of Biomedical Engineering VelTech Rangarajan Dr. Sugunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor A. Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai India Department Of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learning with the Sparrow Search Algorithm unveils a new epoch in the medi

关键词： Deep learning Training Image analysis Accuracy Pancreatic cancer Sensitivity and specificity Feature extraction Classification algorithms Medical diagnostic imaging Optimization

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency

Optimizing Task Execution in Robotic Swarms: Coordination St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vandna Bansla Inderjeet Kaur Rudramani Bhutia Girish Singh Bisht Khemraj Sharma Shivalik Engineering College Dehradun U.K India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P. India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The “swarm robotics” is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate.

关键词： Robot kinematics Swarm robotics Organizations Planning Particle swarm optimization Informatics Optimization

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Design and Implementation of an Instrumented walking cane for Detection of Freezing of Gait 3

Design and Implementation of an Instrumented walking cane fo...

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3rd IEEE International Multidisciplinary Conference on Engineering Technology, IMCET 2021

作者： Haidar Ahmad, Joelle El-Asmar, Alain Abi Zeid Daou, Roy Hayek, Ali Boercsoek, Josef Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781665405874

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages. © 2021 IEEE.

关键词： Diagnosis

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