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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Fatima, Shaista Sangeetha, G. Ponmurugan, P. Arularasan, A.N. Prabhu Chakkaravarthy, A. Denis, R. Chinnasamy, A. Department of Electronics and Communication Engineering MJ College Telangana Hyderabad500053 India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu Thiruvallur India Department of Research Meenakshi Academy of Higher Education and Research Tamil Nadu Chennai600078 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Tamil Nadu Chennai600123 India Department of Networking and Communications School of Computing College of Engineering and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science Mount Carmel College Autonomous Karnataka Bengaluru560052 India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur campus Tamil Nadu 603203 India

ISBN: (纸本)9798350398496

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry. © 2023 IEEE.

关键词： Machine learning

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Setting up SLAs using a dynamic pricing model and behavior analytics in business and marketing strategies in cloud computing

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Personal and Ubiquitous Computing 2023年第6期27卷 2225-2241页

作者： Mehlabani, Ehsan Gorjian Javadpour, Amir Zhang, Chongqi Ja’fari, Forough Sangaiah, Arun Kumar Department of Probability and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon

Increasing amounts of data are being generated every year. Sustainable computing systems have become capable of extracting and learning information from the underlying data. Edge and AI (artificial intelligence) are expanding into industrial systems requiring new computing and networking infrastructure. Due to this, SLA computing is becoming increasingly challenging to handle in these emerging cloud environments. The cloud is a service that provides virtual resources to users. Qualitative and quantitative findings in market-oriented approaches are one of the most common methods for managing virtual and physical machines in a network. When allocating services, price is an important factor to consider. In this study, we aim to determine the initial price of VMs while considering the dynamic pricing model in a competitive, sustainable computing system. Besides negotiation-based trading, a multifactor architecture is used for trading in the marketplace. Based on the simulation results, it was found that the performance could be improved by categorizing the VMs based on regression. According to the simulation results, the cloud market system provides a better service-level agreement (SLA) and response time when assigning virtual machines to the market. Based on the results, we found that using the regression method for categorizing the VMs to manage the market improved the SLA. © 2023, The Author(s).

关键词： Network architecture

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A Smart Electronic Wheelchair Design Using Artificial Intelligence and Intelligent Sensors Association 2

A Smart Electronic Wheelchair Design Using Artificial Intell...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Balamurugan, R. Kanchana, D. Anni Princy, B. Jaisudha, J. Malathi, P. Glory, E. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India Simats Department of Biosciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair's controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair's DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user's pulse rate, as well as the wheelchair's functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner. © 2024 IEEE.

关键词： Accelerator AI Artificial Intelligence Batteries DC Power Intelligent Sensors Mobile-Wheelchair MWC Wheelchair

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Large covariance matrix estimation with factor-assisted variable clustering

arXiv

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arXiv 2025年

作者： Li, Dong Qiao, Xinghao Yu, Cheng Department of Statistics and Data Science Tsinghua University Beijing China Faculty of Business and Economics The University of Hong Kong Hong Kong Department of Statistics London School of Economics London United Kingdom

This paper studies the covariance matrix estimation for high-dimensional time series within a new framework that combines low-rank factor and latent variable-specific cluster structures. The popular methods based on assuming the sparse error covariance matrix after taking out common factors may be invalid for many financial applications. Our formulation postulates a latent model-based error cluster structure after removing observable factors, which not only leads to more interpretable cluster patterns but also accounts for non-sparse cross-sectional correlations among the variable-specific residuals. Our method begins with using least-squares to estimate the factor loadings, followed by identifying the latent cluster structure by thresholding the scaled covariance difference measures of residuals. A novel ratio-based criterion is introduced to determine the threshold parameter when performing the developed clustering algorithm. We then establish the cluster recovery consistency of our method and derive the convergence rates of our proposed covariance matrix estimators under different norms. Finally, we demonstrate the superior finite sample performance of our proposal over the competing methods through both extensive simulations and a real data application on minimum variance portfolio. Copyright © 2025, The Authors. All rights reserved.

关键词： Time series

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Road Obstacles Detection using Convolution Neural Network and Report using IoT 4

Road Obstacles Detection using Convolution Neural Network an...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Abdul Kareem, D. Department of Artificial Intelligence and Data Science Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tamil Nadu Tiruttani India

ISBN: (纸本)9781665401180

Most of the highways accidents are occurred due to bad road conditions. To minimize the road accidents, fixing the cracks and potholes in the highways can be done. The objective of this work to detect the potholes and road cracks that lie below the ground surface those are called as negative obstacles. The USB camera configuration is used to capture and test with highway road scenes. A new evaluation and comparison method for automatic detection of road cracks has been presented. The collected crack pictures of the road were processed with convolution neural network classifier, the model is simulated with MATLAB. The classified obstacles and location details is reported to highways department by using IoT modules. The obstacles detection using the CNN classifier produced accurate values and the results will be communicated immediately to concern end point. © 2022 IEEE

关键词： Roads and streets

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Convolutional-Neural Network Bases Speaker Emotion Detection Based on Human-computer Interaction

Convolutional-Neural Network Bases Speaker Emotion Detection...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Nithya B Saranya R Mary Joseph R. Josphineleela Arunkumar Thirunagalingam Arun Kumar Kodali Dept of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Computer Science and Engineering V.S.B. Engineering College Karur Department of IT St. Joseph's Institute of Technology OMR Chennai Department of Computer Science and Engineering Panimalar Engineering College Data and Operations Mckesson Corporation Mphasis Corporation Data Architect

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic connections is made possible by emotional knowledge. Conventional techniques for detecting emotions depend on manually created characteristics, which are frequently laborious and less resilient to speech variability. By using CNNs' automatic feature extraction capabilities, the suggested system analyses speech signal spectrograms and extracts crucial emotional indicators. The method obtains notable gains in emotion classification accuracy by retraining the system on a varied collection of emotional speech samples. Important emotional states like happiness, anger, sadness, and neutrality are all successfully distinguished using the CNN model. This study demonstrates how deep learning can help develop emotion-sensitive HCI systems and promote more organic and contextually aware human-machine interactions. Applications in customer service, healthcare, and virtual assistants can benefit from the technique's adaptability and versatility.

关键词： Human computer interaction Emotion recognition Virtual assistants Medical services Feature extraction User experience Convolutional neural networks Robots System analysis and design Spectrogram

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The Assessment of Human Development Index in West Java Province of Indonesia Using Regression and R Programming Technology

The Assessment of Human Development Index in West Java Provi...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Franciscus, Ronan Pradhana, Widhi Halim, Jonathan Kingsley Diana Permai, Syarifah Arifin, Yulyani Pramudya, Fabian Surya Amirul Jabar, Bakti Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University Statistics Department Jakarta Indonesia Bina Nusantara University Mathematics Department Jakarta Indonesia

ISBN: (纸本)9781665468763

The effectiveness of economic development in developing countries is often only judged on an economic basis, even though it cannot be denied that improving the quality of human resources is also a sign of the success of economic development in a region. This study aims to determine and examine the variables that affect the HDI of West Java Province. We compared the two methods, Ordinary Least-Squares (OLS) and Weighted Geographic Regression (GWR), to represent non-spatial and spatial approaches. Twenty-seven districts in West Java Province in Indonesia were considered based on the Indonesian Central statistics Agency dataset. The method used in this research is dataset analysis sourced from the website of the Central statistics Agency (BPS). Six quantitative factors related to HDI were evaluated: life expectancy, expected length of education, the average length of education, per capita expenditure, number of the public health center, and open unemployment rate. The results of this study indicate that the variables of life expectancy, length of education, the average length of education, and expenditure per capita are four variables that have a significant effect on HDI figures in West Java, where GWR performs better than the OLS technology with a coefficient of determination (R2) and AIC value of 99.24113% and 35.10531. Further research is further developed by using robust regression to address outliers in several locations and adopting important factors that influence local policies on local policies. © 2022 IEEE.

关键词： Developing countries

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Classification using Deep CNN Feature Extraction and Hyper parameter Tuned Random Forest in Piperaceae plant Type

Classification using Deep CNN Feature Extraction and Hyper p...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： A. Pravin C. Deepa Department of Computer Science( Artificial Intelligence and Data Science) Sri Ramakrishna College of Arts and Science Coimbatore India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Piperaceae otherwise known as the piper family, are a flowering plant family in large amount. This piperacease family contains around 3,600 species are presently accepted in 5 genera. The huge amount of special in this piperaceae family is two major genera: Piper (2,171 species) and (over 1,000 species) Peperomia. Piperaceae plant family may include tiny trees, climbers plants, or herbs. The allocation of this group of plants is top distributed in tropical regions of the earths. The species best-known by everyone is Black Pepper (Piper nigrum), most peppercorns yields that are mainly used as cooking spices, including piper betle, even though its connections in the family include numerous other spices. Piper nigrum is cultured for its fruit, which is typically dried and other as a spice and flavouring every food. The fruit is a (stone fruit) drupe which is about (0.20 in) 5 mm in diameter (fully and fresh mature), which encloses a single piper seed, dark red, and contains a stone like structure. The (Piper betle) or betel is a trailing plant of the Piperaceae family, which includes kava and pepper. The betel plant is indigenous to Southeast Asia. It is an dioecious perennial, evergreen plant, with shiny white catkins and heart like shaped leaves. Betel plants are cultured for their leaves which are the majority used in chewing areca nut for flavouring.

关键词： Heart Forestry Flowering plants Feature extraction Prediction algorithms Seeds (agriculture) Security

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Comparative evaluation performances of capsule networks for complex image classification

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Journal of data, Information and Management 2022年第3-4期4卷 267-276页

作者： Afriyie, Yaw Weyori, Benjamin A. Opoku, Alex A. Department of Computer Science and Informatics School of Sciences University of Energy and Natural Resources Sunyani Ghana Department of Computer Science Faculty of Information and Communication Technology SDD University of Business and Integrated Development Studies Wa Ghana Department of Mathematics & amp Statistics School of Sciences University of Energy and Natural Resources Sunyani Ghana

Image classification has become a major task in the area of computer vision technologies. Capsule network (CapsNets), a new technique that uses activity vectors and dynamic routing between capsules to address some of the constraints of convolutional neural networks(CNNs), was recently developed. This work compares the performance of the CapsNets algorithm to four well-known datasets in this study (FMNIST, CIFAR-10, Brain tumor MRI, and blood cell dataset). We used the datasets with varying numbers of instances and classes, such as blood cell images and everyday objects, to test classification accuracies and losses. The results reveal that even for simple architectures, training the CapsNets algorithm necessitates a significant amount of computational resources, with Sabour’s model as a baseline, generalizing well on small datasets and performing poorly on complex images. We suggest, however, that CapsNets appears to be a promising new technique for image classification, and that future experimentation with more resilient computational resources and modified CapsNet structures should yield superior results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.

关键词： Capsule networks Convolutional neural networks Image classification Performance metrics

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HerbSimNet: Deep Learning -Based Classification of Indian Medicinal Plants with High Inter-Class Similarities

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Procedia computer science 2025年 258卷 765-774页

作者： N. Shobha Rani Bhavya K R Pushpa B.R. Ragavendra M. Devadas Department of Artificial Intelligence and Data Science GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM (Deemed to be) University India Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Medicinal plant species recognition is important across diverse sectors such as Ayurveda, agriculture, environment conservation and botanical research. Specific groups of plants in Indian medicinal plant ecosystem exhibit significant inter-class similarities due to varying abundance and ecological factors. To address the challenges involved in the process of classifying these species in this work a deep learning model Herb-SimNet is proposed. The Herb-SimNet analyzes similarity of plant species over other plant species using vision based deep learning and machine learning techniques. The proposed model works based on the combination of wavelet features and convolutional features extracted using three sequential convolution layers to extract the prominent features that distinguish variations among the inter class similarity plant species. To perform experiments, a dataset is created by capturing medicinal plant leaf images using box model in plain background and uniform lighting. A smart phone captured twelve Indian medicinal plant species comprising of about 1400+ samples that belongs different plant species but similar morphological structure is collected. Baseline experiments are carried out between Herb-SimNet and other state-of-the-art deep learning models for classification based on the proposed dataset. The outcomes demonstrate that Herb_SimNet provides clear interpretation one plant variety with others and achieves superior accuracy in prediction than that of state-of-the-art approaches. Furthermore, the model demonstrates better generalization towards the other inter-class similarity groups considered for testing. In conclusion, the proposed dataset and Herb-SimNet plays a a crucial role in advancement of research concerning Indian medicinal plant species classification resulting into enhancement of AI-based technology for biodiversity conservation and ethnobotanical studies.

关键词： Classification inter-class similarities confusing plant species medicinal plants wavelet image features

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