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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Michael, J Justina Thenmozhi, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In the next three decades, crop production must be doubled as the population is likely to increase by 35 percent. To enhance crop production to a great extent, the issues behind the scene should be rooted out. Right from the Neolithic era to this new age, weed identification and removal remains a significant challenge to attain a good crop yield. Technologies have supported agriculturalists on a large scale to overthrow this issue. This paper discusses such latest technologies, advancements, and methods imposed in the field of weed detection and removal. Overview of weeds, state-of-the-art deep learning techniques, and their research efforts in detecting weeds are briefed. Finally, current challenges, future improvements, and research ideas on weeds are pointed out for the sake of researchers. © 2022 IEEE.

关键词： Deep learning

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IoT Wearable Breast Temperature Assessment System 7

IoT Wearable Breast Temperature Assessment System

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ashreetha, B. Dankan Gowda, V. Anandaram, Harishchander Nithya, B.A. Gupta, Neeraj Verma, Basant Kumar Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India Department of Computer Science School of Applied Sciences Reva University Karnataka Bengaluru India Department of Computer Science and Engineering SGT University Haryana Gurugram India

ISBN: (纸本)9781665464086

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations. © 2023 IEEE.

关键词： Internet of things

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments 13

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kirubhakaran Karthikeswaran, D. Arun, M. Chakravarthy, R. Arun Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Surya Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350380583

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A∗ algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry. © 2024 IEEE.

关键词： Motion planning

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QSroute : A QoS Aware Routing Scheme for Software Defined networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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8th IEEE International Conference on Software engineering and computer Systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. Software Defined networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction 2

Impurity explicit optimal Machine learning and statistical p...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Nareshkumar, R. Shalinikumari, R. Kowsalya, S. Sri Nandhini Vijay Anand, P. Mohamed, Ajmal Gayathri, S. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India Bharath Institute of Higher Education and Research Selaiyur Department of Architecture Tamilnadu Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Tamilnadu Chennai India CMR Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Technology Department of Computer Science and Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350365269

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy. © 2024 IEEE.

关键词： Machine learning

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Multiple Circular Ring-canals on the Starfish Routing Backbone in Wireless Sensor Networks 4

Multiple Circular Ring-canals on the Starfish Routing Backbo...

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4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Mozumder, Md. Daiyan Uddin Hosen, Riaj Hossain, Md. Mamun Habib, Md. Ahsan Roy, Palash Razzaque, Md. Abdur Green University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Green Networking Research Group Department of Computer Science and Engineering Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Bangladesh

ISBN: (纸本)9781665490450

To capture real-time data from Wireless Sensor Network Applications, utilizing unmanned aerial vehicles (UAVs) is becoming popular for minimizing end-to-end latency and reducing packet loss issues with extended network lifetime. Existing works in the literature are either limited by using a static sink or ground vehicle-supported mobile slink. In this paper, we introduce Unmanned Aerial Vehicles (UAVs) as mobile sinks moving around multiple circular ring canals on the basic starfish routing backbone (bSFR). We also develop generalized mathematical equations for determining an optimal number of ring canals required for a terrain with certain width and length along with their radii. The results of experiments carried out in Network Simulator version 2 (NS2) depict that the proposed routing model significantly improves the performances in terms of end-to-end latency and network lifetime. © 2022 IEEE.

关键词： Wireless sensor networks

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

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World Journal of engineering and Technology 2024年第4期12卷 867-884页

作者： Jean Ndoumbe Ivan Basile Kabeina Gaelle Patricia Talotsing Soubiel-Noël Nkomo Biloo Laboratory of Computer Engineering Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.

关键词： Automatic Recognition Artificial Neural Networks K-Nearest Neighbors Machine Learning Analog Modulations Digital Modulations

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A Novel Evaluation of Cryptocurrency Future Price Prediction to Manage Fluctuations in Market by using Hybrid Learning Methodology

A Novel Evaluation of Cryptocurrency Future Price Prediction...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Pradeep, D. Divya, G. Rajeshwari, R. Deepa, R. Satyanarayana, T.V.V. M.Kumarasamy College of Engineering Department of CSE Karur India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India S.A. Engineering College Dept of MCA Chennai India R.M.K. Engineering College Department of Computer Science Engineering Chennai India Department of ECE AP Tirupati India

ISBN: (纸本)9798350319200

In recognition of its optimistic vision on the probable future of cryptocurrencies, the value of cryptocurrency has risen dramatically during the previous eight decades. Cryptocurrency offers significant benefits over the established financial system, and investors have taken notice. Cryptocurrency is unique in that it facilitates decentralized financial services, making it immune to control by large financial institutions. With the rise of electronic currencies like Ethereum (ETH) and BTT, business may be conducted entirely online. One illustration of the distributed structure of physical currencies is the reality that all consumers who use virtual money have utilization of operations without the need for a third-party they can depend on. Like the market fluctuations of traditional stocks, cryptocurrencies fluctuations in price are extremely volatile and non stationary. Traders and academics alike have given increased focus to predictions of bitcoin prices due to the asset class' rising popularity. The use of deep learning in projecting the price of cryptocurrencies has become increasingly important. This research presented a new algorithm for predicting cryptocurrency prices called Hybrid Learning for Crypto Prediction (HLCP), and results from these models demonstrated that the proposed HLCP algorithm outperformed all other methods in this area. The HLCP model offered encouraging and precise predictions for all cryptocurrencies;hence it can be considered the most successful algorithm. The model was used to forecast the 6 month ending prices of cryptocurrency. To measure how well predictions match targets during training and testing, we used the HLCP correlation metric. The proposed method efficiency is evaluated with respect to the following metrics such as training and testing accuracy levels, training and testing loss ratio as well as different cryptocurrencies (ETH, BitCoin and BTT) prediction and actual price comparisons. These metrics clearly shows the effici

关键词： Cryptocurrency Deep Learning HLCP Hybrid Learning for Crypto Prediction Price Prediction Trading

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Prediction of Kidney Disease Utilizing a Hybrid Deep Learning Methodology 2

Prediction of Kidney Disease Utilizing a Hybrid Deep Learnin...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Nallarasan, V. Ponnusamy, Vijayakumar Lakshminarayanan, R. Sona, K. Vigneshwari, S. Vinoth, R. Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chengalpattu Kattankulathur India Srm Institute of Science and Technology Department of Electronics and Communications Chengalpattu Kattankulathur India Sri Ramakrishna Engineering College Department of Computer Science and Engineering India V.S.B College of Engineering Technical Campus India Gojan School of Business and Technology Department of Information Technology RedhillsTamilnadu Chennai600052 India

ISBN: (纸本)9798350387933

The kidney is an essential organ inside the human body, playing a crucial role in several physiological processes. One of its primary roles is the filtration of waste products and surplus fluids from the bloodstream, facilitating their subsequent elimination via urine production. In order to mitigate potential life-threatening complications, it is essential to identify and investigate chronic renal illnesses. The suggested method takes into account the limitations of early prediction and robust decision-making oscillations due to the presence of False Positive Rates in Kidney CT Images. The suggested system incorporates a hybrid method that combines machine learning and deep learning techniques. A prognostic method is developed by using the kidney dataset collected from TCIA, which involves the analysis of patients' past health information. The suggested system incorporates a hybrid approach of random forest regression and multinomial regression algorithms to identify the Explorer data analysis technique for the chronic kidney disease dataset. The secondary approach involves using a computer tomography image dataset obtained from the TCIA public data set. This dataset is processed using a deep convolutional neural network (DCNN) architecture that has many layers of deep convolution filters. The purpose of this architecture is to accurately detect and classify kidney abnormalities within the images. The integration of main and secondary outcomes is used to determine the presence or absence of renal disease in a patient. In this study, we examine a unique methodology known as Hybrid Deep K-Net (H-DKN). The accuracy reached by HDKN is reported to be 97%. Furthermore, we compare this approach with many state-of-the-art methods that have been established on the current platform. © 2024 IEEE.

关键词： Decision making

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