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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md. Mortuza Nasir Uddin, Md. Kazary, Sumaya Rahman, Md. Obaidur Dhaka University of Engineering and Technology Gazipur Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798331529765

The Underwater Wireless Sensor Network (UWSN) provides an effective way of communicating in aquatic environments to overcome the constraints of applying terrestrial MAC protocols. Due to unique challenges and requirements of underwater communication, designing specialized MAC protocols for UWSNs is crucial to conserve energy. Currently, research has focused on duty-cycled receiver-initiated asynchronous MAC protocols due to their superior performance over sender-initiated protocols and their ability to overcome the challenges of synchronous MAC protocols. However, existing UWSN MAC protocols suffer from re-transmissions, energy loss, protocol overheads, and longer duty-cycle periods. This paper proposes a MAC protocol for UWSNs, termed Predictive Wake-Up Based Optimized MAC (PWO-MAC) protocol, which incorporates an additive increase and multiplicative decrease (AIMD) technique along with a predictive wake-up scheduling approach. The proposed PWO-MAC protocol dynamically adjusts transmission rates, enabling re-transmissions only upon failure and reducing multiple transmission. The protocol optimizes protocol overhead, reduces energy consumption, ensures minimum delay and enhances network throughput. © 2024 IEEE.

关键词： Energy utilization

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Privacy Preservation And Performance Improvement of HealthCare Data Prediction and Storage Using Map-Reduce Framework

Privacy Preservation And Performance Improvement of HealthCa...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Shanmugapriya, E. Ajai Krishna, T.S. Guindy Anna University College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505776

Big data is observed as the state-of-the-art-concern which is employed to deal with immense range of data in definite fields like remote sensing, medical, customer services and so on. In recent days, health care industry has been originating vast expansion to develop individual specialized techniques. After all it is mandatory to protect the confidential patient's medical record as it is stored in the untrusted cloud which in turn continual to concede with users according to their requirements. Despite, sustaining the ownership of an entire medical records is the crucial issueamong patient and the hospital thereby the primary responsibility of the hospital is to maintain the records precisely. Hence, the medical records of patients are not only accessed in any emergency issues or treatment. Furthermore, Big Data classification is typically a tiresome and extended task. The Map Reduce Framework technique offers the processing of data which is being paralleled through the distribution of data in smaller chunks over clusters. The technique of Map Reduce is being presented which is utilized for processing data items that are heterogeneous. By means of the well-known framework Map reduce issues and challengesof existing methods could be solved randomly. This research paper attempts pragmatic method of disease prediction and classification using discrete multilayer perceptron-based classification. Subsequently, the proposed technique performs preprocessing using Normalization, Adaptive Shearlet polynomial component analysis-based features extraction, optimization of extracted features using recurrent chicken swarm optimization algorithm, classification and prediction. Ultimately, the medical information is stored in cloud server byperforming encryption to the data using semantic polynomial based blowfish encryption. © 2024 IEEE.

关键词： Polynomials

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CNN Based Smart Waste Segregation and Collection System 4

CNN Based Smart Waste Segregation and Collection System

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Lavanya, V. Abikash Shivam, G. Shanmugasundaram, S. Deepalakshmi, R. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350393354

Waste management is an increasing concern in urban areas worldwide, because of population growth and environmental sustainability goals. To address this challenge, this paper presents a Convolutional Neural Network (CNN)-based waste segregation and collection system that uses the power of computer vision and machine learning. The proposed system is used to automate the process of waste segregation, optimizing the sorting of waste into bio-degradable, reusable, recyclable waste and trash in real time. Utilizing a camera for capturing the images of incoming waste materials and moving the waste at their respective collection points. The images are processed using a deep learning YOLOv8 model, which identifies and classifies the waste items into bio-degradable, recyclable and non-recyclable categories with high accuracy. This innovative waste management solution holds the potential to change the waste collection and recycling processes, making them more cost-effective, environmentally friendly, and aligned with the ability for sustainable waste management practices. © 2024 IEEE.

关键词： Waste management

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IoT Based Weather Forecasting for Rainfall Prediction Using Intensified Long Short-Term Memory Artificial Neural Network

IoT Based Weather Forecasting for Rainfall Prediction Using ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Vigneshwaran, V. Avinash, B. Titus Casimir, A. Vijay, D. Jayakumar, D. IFET College of Engineering Department of Computer Science Engineering Tamil Nadu Villupuram India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The purpose of this study was to evaluate the possible uses of artificial neural networks (ANNs) and intensified long short-term memory (LSTM) for rainfall forecasting. The effectiveness of these weather forecasting techniques was evaluated and assigned a score. Materials and Techniques: For the sample test analysis, intensified LSTM and ANN with pre-test power (G-power) of 80% and alpha error rate (Err) of 0.05 were used. The results of the MATLAB simulations show that the ANN has an accuracy of 95.68% and the Intensified LSTM has an accuracy of 87.99%. We discover that the achieved accuracy ratio is 0.034 (p 0.05) using the statistical application SPSS. According to this study, when it comes to advanced weather forecasting, the ANN algorithm outperforms the Intensified LSTM technique in terms of rainfall prediction. © 2024 IEEE.

关键词： Accuracy Rain Error analysis Weather forecasting Signal processing algorithms Signal processing Prediction algorithms

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Cartoonify real-time images using machine learning

Cartoonify real-time images using machine learning

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Umarani, V. Jagadeesh Raj, M. Garshan Kumar, K. Vasanth Reddy, Somu Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

The image process is a technique for applying certain operations to a photograph in order to produce an enhanced image or extract some useful information from it. It’s a type of signal processing where the input is a picture and the output could be either an image or features that are associated with that image. Tools for processing images include Numpy, Scikit Image, and OpenCV. Generative modeling involves mechanically identifying and then mastering the variations or styles on input traces in a way similar to how the model may be used to induce or affair new exemplifications that credibly might be drawn from the first dataset. It is associated with nursing unattended literacy tasks in machine literacy. © 2024, CRC Press/Balkema. All rights reserved.

关键词： K-means clustering

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Optimal Operation of Dedicated Urban Charging Stations in Modern Power Distribution Networks with High Electric Public Transport Vehicle Penetration 19

Optimal Operation of Dedicated Urban Charging Stations in Mo...

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19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Zare, Peyman Mohammadian, Amir Seyedshenava, Seyedjalal Davoudkhani, Iraj Faraji University of Mohaghegh Ardabili Faculty of Engineering Department of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798331507565

The rapid integration and expanding use of Electric Public Transport Vehicles (EPTVs) have become pivotal in the evolution of Modern Urban Power Distribution Networks (MUPDNs). As urban centers transition towards electrified public transportation, MUPDNs face both significant challenges and promising opportunities. The widespread deployment of EPTVs, coupled with the high penetration of renewable energy sources (RESs), including Photovoltaic Systems (PVs) and Wind Turbine Generators (WTGs), introduces complexities due to the variability and uncertainty of energy production. This impacts the delicate balance between load and generation. This study investigates the strategic integration of EPTV Dedicated Urban Charging Stations (EPTVDUCSs) alongside RESs, such as PVs and WTGs, with the objective of mitigating these challenges and enhancing the technical performance of MUPDNs. Reconfiguring MUPDNs is identified as a cost-effective strategy for optimizing network parameters and fully leveraging the potential of these emerging technologies. Accordingly, the reorganization of MUPDNs and the optimal timing of the EPTV charging process, along with the maximum utilization of RESs, are the primary objectives of this article. These challenges are formulated as a probabilistic exploitation problem, structured as a multi-objective optimization challenge. The focus is on minimizing operational costs while maximizing power quality. To address this complex issue, a Pareto front-based optimization framework is employed, utilizing the Polar Fox Optimization Algorithm (PFA), an advanced meta-heuristic method. The IEEE 33-bus distribution system serves as a case study to evaluate the proposed probabilistic operation model. The results demonstrate the effectiveness of this approach in improving MUPDN performance amidst increasing EPTV integration and significant RES penetration. © 2024 IEEE.

关键词： Multiobjective optimization

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Machine Learning Enhanced Smart Sensors for Hand Gesture Recognition 3

Machine Learning Enhanced Smart Sensors for Hand Gesture Rec...

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3rd International Symposium on Sensor Technology and Control, ISSTC 2024

作者： Feng, Haoen National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798331541637

Against the backdrop of highly developed smart sensor technology, machine learning, and multimodal data analysis, hand gesture recognition (HGR) has become an important field in promoting human-computer interaction, health monitoring, and rehabilitation medicine. This paper proposes a smart sensor system based on machine learning enhancement, which combines hardware devices such as Flex sensor, JY901S, camera and smart gloves, as well as machine learning models such as support vector machine (SVM) and long short-term memory network (LSTM), using For processing and analysis of gesture data. By using multi-modal data in static and dynamic gesture classification tasks, experimental results show that collaborative inference of multi-modal data significantly improves the model's classification accuracy compared to single or bi-modal data. © 2024 IEEE.

关键词： Adversarial machine learning

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Pomegranate Leaf Fruit Disease Prediction Using Machine Learning 4th

Pomegranate Leaf Fruit Disease Prediction Using Machine Lear...

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4th International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, MARC 2023

作者： Mokal, Atul Bhimrao Avhad, Vaishnavi Jadhav, Om Khamkar, Vishal Japtap, Mansi Ingle, Sumedh S. Department of Computer Engineering Sanjivani College of Engineering Kopargaon A. Nagar Kopargaon India

ISBN: (纸本)9789819752300

The impact of agricultural plant diseases on farmers’ economic losses is significant. These diseases can harm various parts of the plant, including the roots, fruits, leaves, and stems. Early detection of these diseases is crucial for improving agricultural productivity. In traditional agricultural systems, disease recognition relies on the expertise of agricultural professionals and experienced farmers, but this often results in lower accuracy, leading to losses for farmers. To address this issue, numerous researchers have been exploring the use of soft computing and expert systems for more accurate plant disease recognition. Visual identification of plant diseases is less reliable, as some diseases may not exhibit visible symptoms, or their symptoms may only become apparent late in the growing season, near the time of harvest. Fortunately, modern technology has the potential to significantly enhance agricultural production and sustainability. This paper offers a comprehensive review of techniques for detecting diseases in pomegranate plants. The study covers various stages of the disease detection process, including preprocessing, segmentation, feature extraction, and classification. Additionally, the paper provides a comparison of existing methods and highlights their limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Expert systems

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Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2399-2412页

作者： G.Rajasekaran J.Raja Sekar Department of information technology mepco schlenk engineering collegesivakasi626005india Department of computer science and engineering mepco schlenk engineering collegesivakasi626005india

Abnormal behavior detection is challenging and one of the growing research areas in computer *** main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain *** this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the *** stage,OPLKT-EMEHOs algorithm is used to generate the opticalﬂow from *** stage,the MIIs opticalﬂow is applied as input to 3 layer CNN for detect the abnormal crowd *** of Minnesota(UMN)dataset is used to evaluate the proposed *** experi-mental result shows that the proposed method provides better classiﬁcation accu-racy by comparing with the existing *** method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.

关键词： Crowd behavior analysis anomaly detection Motion Information

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Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM Approach on the UNSW-NB15 Dataset 21

Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM...

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21st International Conference on Electrical engineering, Computing Science and Automatic Control, CCE 2024

作者： Pear, Zareen Tasnim Kibria, Hafsa Binte Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350377545

In the era of rapid technological advancements, data and information have become significant assets that require continuous protection from unauthorized access and malicious attacks. Network Intrusion Detection Systems (NIDS) can effectively manage the increasing data demands assuring robust security. However, traditional or manual methods are often inadequate to cope with the sophisticated and evolving nature of cyber threats due to their inability to quickly process large volumes of data. Traditional machine learning models are widely used for this purpose. Recently deep learning approaches have become popular in classification tasks showing promising results. This paper introduces a hybrid deep-learning approach for detecting various network intrusions using binary and multi-class (ten classes) classification on the well-known UNSW-NB15 dataset. The proposed hybrid CNN-LSTM model demonstrates significant improvements in intrusion detection, achieving an accuracy of 97.19% for binary classification and 87.70% for multi-class classification, particularly in handling complex attack patterns and reducing false positives. Evaluated using metrics such as precision, recall and F1-score, the model consistently performs robustly across nine distinct attack categories. These results highlight the model's effectiveness in establishing enhanced network security. © 2024 IEEE.

关键词： Network intrusion

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