Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303021
Hye Jin Kim, Rhee Jung Soo
The article offers a comprehensive analysis of network coding, communications security, and coding theory, examining their applications and advancements. It evaluates the fundamental concepts and methodologies utilized in these fields while shedding light on current progress and potential future research directions. The implications of the study discussed in this article extend widely across the communication sector, with immediate practical applications across various disciplines. One of the key areas covered in the article is the development of novel error-correcting codes and coding algorithms, which contribute to enhancing communication reliability. Additionally, the integration of machine learning and artificial intelligence (AI) techniques into network communications security is explored, highlighting their potential to bolster safeguarding measures. Furthermore, the incorporation of security controls into connected devices and Internet of Things (IoT) networks is addressed, acknowledging the need to ensure security in these interconnected systems. To ensure the reliability and security of network communications and foster innovation and growth within the communication sector, the article concludes that coding theory and network communications security must continue to evolve and progress. By pushing the boundaries of these fields, researchers can address emerging challenges, improve existing systems, and pave the way for future advancements in communication technology.
{"title":"A Comprehensive Study on the Advancements of Man and Machine in Network Security and Coding Theory","authors":"Hye Jin Kim, Rhee Jung Soo","doi":"10.53759/7669/jmc202303021","DOIUrl":"https://doi.org/10.53759/7669/jmc202303021","url":null,"abstract":"The article offers a comprehensive analysis of network coding, communications security, and coding theory, examining their applications and advancements. It evaluates the fundamental concepts and methodologies utilized in these fields while shedding light on current progress and potential future research directions. The implications of the study discussed in this article extend widely across the communication sector, with immediate practical applications across various disciplines. One of the key areas covered in the article is the development of novel error-correcting codes and coding algorithms, which contribute to enhancing communication reliability. Additionally, the integration of machine learning and artificial intelligence (AI) techniques into network communications security is explored, highlighting their potential to bolster safeguarding measures. Furthermore, the incorporation of security controls into connected devices and Internet of Things (IoT) networks is addressed, acknowledging the need to ensure security in these interconnected systems. To ensure the reliability and security of network communications and foster innovation and growth within the communication sector, the article concludes that coding theory and network communications security must continue to evolve and progress. By pushing the boundaries of these fields, researchers can address emerging challenges, improve existing systems, and pave the way for future advancements in communication technology.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84019009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303018
Senthil Vadivu M, Purushotham Reddy M, Kantilal Rane, Narendra Kumar, K. A, Nitesh Behare
The agricultural sector plays a significant role in the economy of many countries, and irrigation is a critical component of successful agriculture. However, traditional irrigation methods can be time-consuming and labor-intensive, and often result in the over or under-watering of crops, which can negatively impact crop yields. To overcome these challenges, smart irrigation systems have been developed to assist farmers in managing their crops and increasing their yield. This research article presents an IoT-based smart irrigation system that uses four sensors - moisture content, temperature, humidity, and ultrasonic - to collect data from the irrigation area and transmit it to a central control system. The central control system uses the data to automatically turn the irrigation pump on and off, based on the moisture level of the soil. The system also includes a mobile application that allows farmers to monitor the system remotely and control the motor pump from their smartphones. The proposed system has several advantages, including reducing the hard work of farmers, providing essential strength to crops, and ensuring that plants receive the adequate amount of water at the required time. Additionally, the system's remote monitoring capabilities allow farmers to monitor the atmospheric temperature, humidity, and moisture content from anywhere at any time, and make adjustments as necessary. Overall, the findings of this research will help farmers to control their irrigation systems remotely, reduce labor costs, and increase crop yields. By improving the efficiency of irrigation and reducing water waste, this IoT-based smart irrigation system has the potential to significantly impact the agriculture sector and promote sustainable farming practices.
{"title":"An IoT-Based System for Managing and Monitoring Smart Irrigation through Mobile Integration","authors":"Senthil Vadivu M, Purushotham Reddy M, Kantilal Rane, Narendra Kumar, K. A, Nitesh Behare","doi":"10.53759/7669/jmc202303018","DOIUrl":"https://doi.org/10.53759/7669/jmc202303018","url":null,"abstract":"The agricultural sector plays a significant role in the economy of many countries, and irrigation is a critical component of successful agriculture. However, traditional irrigation methods can be time-consuming and labor-intensive, and often result in the over or under-watering of crops, which can negatively impact crop yields. To overcome these challenges, smart irrigation systems have been developed to assist farmers in managing their crops and increasing their yield. This research article presents an IoT-based smart irrigation system that uses four sensors - moisture content, temperature, humidity, and ultrasonic - to collect data from the irrigation area and transmit it to a central control system. The central control system uses the data to automatically turn the irrigation pump on and off, based on the moisture level of the soil. The system also includes a mobile application that allows farmers to monitor the system remotely and control the motor pump from their smartphones. The proposed system has several advantages, including reducing the hard work of farmers, providing essential strength to crops, and ensuring that plants receive the adequate amount of water at the required time. Additionally, the system's remote monitoring capabilities allow farmers to monitor the atmospheric temperature, humidity, and moisture content from anywhere at any time, and make adjustments as necessary. Overall, the findings of this research will help farmers to control their irrigation systems remotely, reduce labor costs, and increase crop yields. By improving the efficiency of irrigation and reducing water waste, this IoT-based smart irrigation system has the potential to significantly impact the agriculture sector and promote sustainable farming practices.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76495695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303022
Soumya Haridas, Jayamalar T
One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.
{"title":"A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification","authors":"Soumya Haridas, Jayamalar T","doi":"10.53759/7669/jmc202303022","DOIUrl":"https://doi.org/10.53759/7669/jmc202303022","url":null,"abstract":"One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83128940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303019
Shirley C P, K. Rane, Kolli Himantha Rao, Bradley Bright B, Prashant Agrawal, Neelam Rawat
Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and independence. The robot is equipped with sensors, including Lidar, proximity sensors, and a Bluetooth transmitter and receiver, which enable it to sense the environment and deliver information to the user. The presence of obstacles can be detected by the robot, and the user is notified through a Bluetooth interface to their headset. The robot's machine learning algorithm is generated using Python code and is capable of processing the data collected by the sensors to make decisions about how to inform the user about their surroundings. A microcontroller is used to collect data from the sensors, and a Raspberry Pi is used to communicate the information to the system. The visually impaired user can receive instructions about their environment through a speaker, which enables them to navigate their surroundings with greater confidence and independence. Our research shows that a multi-robot system equipped with sensors and machine learning algorithms can assist visually impaired individuals in navigating their environment. The system delivers the user with real-time information about their surroundings, enabling them to make informed decisions about their movements. Additionally, the system can replace the need for a human assistant, providing greater independence and privacy for the visually impaired individual. The system can be improved further by incorporating additional sensors and refining the machine learning algorithms to enhance its functionality and usability. This technology has the possible to greatly advance the value of life for visually impaired individuals by increasing their independence and mobility. It has important implications for the design of future assistive technologies and robotics.
{"title":"Machine learning and Sensor-Based Multi-Robot System with Voice Recognition for Assisting the Visually Impaired","authors":"Shirley C P, K. Rane, Kolli Himantha Rao, Bradley Bright B, Prashant Agrawal, Neelam Rawat","doi":"10.53759/7669/jmc202303019","DOIUrl":"https://doi.org/10.53759/7669/jmc202303019","url":null,"abstract":"Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and independence. The robot is equipped with sensors, including Lidar, proximity sensors, and a Bluetooth transmitter and receiver, which enable it to sense the environment and deliver information to the user. The presence of obstacles can be detected by the robot, and the user is notified through a Bluetooth interface to their headset. The robot's machine learning algorithm is generated using Python code and is capable of processing the data collected by the sensors to make decisions about how to inform the user about their surroundings. A microcontroller is used to collect data from the sensors, and a Raspberry Pi is used to communicate the information to the system. The visually impaired user can receive instructions about their environment through a speaker, which enables them to navigate their surroundings with greater confidence and independence. Our research shows that a multi-robot system equipped with sensors and machine learning algorithms can assist visually impaired individuals in navigating their environment. The system delivers the user with real-time information about their surroundings, enabling them to make informed decisions about their movements. Additionally, the system can replace the need for a human assistant, providing greater independence and privacy for the visually impaired individual. The system can be improved further by incorporating additional sensors and refining the machine learning algorithms to enhance its functionality and usability. This technology has the possible to greatly advance the value of life for visually impaired individuals by increasing their independence and mobility. It has important implications for the design of future assistive technologies and robotics.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90703794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303030
Kalaiselvi Balaraman, Angelin Claret S.P.
Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.
{"title":"Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals","authors":"Kalaiselvi Balaraman, Angelin Claret S.P.","doi":"10.53759/7669/jmc202303030","DOIUrl":"https://doi.org/10.53759/7669/jmc202303030","url":null,"abstract":"Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85604343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303027
Deepa Devasenapathy, V. K, Anna Alphy, F. D. Shadrach, Jayaraj Velusamy, Kathirvelu M
Diabetes is the main cause for diabetic kidney disease (dkd), which affects the filtering units of kidneys slowly and stops it’s function finally. This consequence is common for both genetic based (type 1) and lifestyle based (type 2) diabetes. However, type 2 diabetes plays a significant influence in increased urine albumin excretion, decreased glomerular filtration rate (gfr), or both. These causes failure of kidneys stage by stage. Herein, the implementation of extended ensemble learning machine algorithm (eelm) with improved elephant herd optimization (ieho) algorithm helps in identifying the severity stages of kidney damage. The data preprocessing and feature extraction process extracts three vital features such as period of diabetes (in year), gfr (glomerular filtration rate), albumin (creatinine ratio) for accurate prediction of kidney damage due to diabetes. Predicted result ensures the better outcome such as an accuracy of 98.869%, 97.899 % of precision ,97.993 % of recall and f-measure of 96.432 % as a result.
{"title":"Kidney Impairment Prediction Due to Diabetes Using Extended Ensemble Learning Machine Algorithm","authors":"Deepa Devasenapathy, V. K, Anna Alphy, F. D. Shadrach, Jayaraj Velusamy, Kathirvelu M","doi":"10.53759/7669/jmc202303027","DOIUrl":"https://doi.org/10.53759/7669/jmc202303027","url":null,"abstract":"Diabetes is the main cause for diabetic kidney disease (dkd), which affects the filtering units of kidneys slowly and stops it’s function finally. This consequence is common for both genetic based (type 1) and lifestyle based (type 2) diabetes. However, type 2 diabetes plays a significant influence in increased urine albumin excretion, decreased glomerular filtration rate (gfr), or both. These causes failure of kidneys stage by stage. Herein, the implementation of extended ensemble learning machine algorithm (eelm) with improved elephant herd optimization (ieho) algorithm helps in identifying the severity stages of kidney damage. The data preprocessing and feature extraction process extracts three vital features such as period of diabetes (in year), gfr (glomerular filtration rate), albumin (creatinine ratio) for accurate prediction of kidney damage due to diabetes. Predicted result ensures the better outcome such as an accuracy of 98.869%, 97.899 % of precision ,97.993 % of recall and f-measure of 96.432 % as a result.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74407635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303026
P. P, Yogapriya J, N. L, Madanachitran R
Cancer is a major cause of death that is brought on by the body's abnormal cell proliferation, including breast cancer. It poses a significant threat to the safety and health of people globally. Several imaging methods, such as mammography, CT scans, MRI, ultrasound, and biopsies, can help detect breast cancer. A biopsy is commonly done in histopathology to examine an image and assist in diagnosing breast cancer. However, accurately identifying the appropriate Region of Interest (ROI) remains challenging due to the complex nature of pre-processing phases, feature extracting regions, segmenting process and other conventional machine learning phases. This reduces the system's efficiency and accuracy. In order to reduce the variance that exists among viewers, the aim of this work is to build superior deep-learning phases algorithms. This research introduces a classifier that can detect and classify images simultaneously, without any human involvement. It employs a transfer-driven ensemble learning approach, where the framework comprises two main phases: production and detection of pseudo-color images and segmentation based on ROI Pooling CNN, which then feeds its output to ensemble models such as Efficientnet, ResNet101, and VGG19. Before the feature extraction process, data augmentation is necessary, involving minor adjustments like random cropping, horizontal flipping, and color space augmentations. Implementing and simulating the proposed segmentation and classification algorithms for any decision-making framework suggested could decrease the frequency of incorrect diagnoses and enhance classification accuracy. This could aid pathologists in obtaining a second opinion and facilitate the early identification of diseases. With a prediction accuracy of 98.3%, the proposed method outperforms the individual pre-trained models, namely Efficientnet, ResNet101, VGG16, and VGG19, by 2.3%, 1.71%, 2.01%, and 1.47%, respectively.
{"title":"Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis","authors":"P. P, Yogapriya J, N. L, Madanachitran R","doi":"10.53759/7669/jmc202303026","DOIUrl":"https://doi.org/10.53759/7669/jmc202303026","url":null,"abstract":"Cancer is a major cause of death that is brought on by the body's abnormal cell proliferation, including breast cancer. It poses a significant threat to the safety and health of people globally. Several imaging methods, such as mammography, CT scans, MRI, ultrasound, and biopsies, can help detect breast cancer. A biopsy is commonly done in histopathology to examine an image and assist in diagnosing breast cancer. However, accurately identifying the appropriate Region of Interest (ROI) remains challenging due to the complex nature of pre-processing phases, feature extracting regions, segmenting process and other conventional machine learning phases. This reduces the system's efficiency and accuracy. In order to reduce the variance that exists among viewers, the aim of this work is to build superior deep-learning phases algorithms. This research introduces a classifier that can detect and classify images simultaneously, without any human involvement. It employs a transfer-driven ensemble learning approach, where the framework comprises two main phases: production and detection of pseudo-color images and segmentation based on ROI Pooling CNN, which then feeds its output to ensemble models such as Efficientnet, ResNet101, and VGG19. Before the feature extraction process, data augmentation is necessary, involving minor adjustments like random cropping, horizontal flipping, and color space augmentations. Implementing and simulating the proposed segmentation and classification algorithms for any decision-making framework suggested could decrease the frequency of incorrect diagnoses and enhance classification accuracy. This could aid pathologists in obtaining a second opinion and facilitate the early identification of diseases. With a prediction accuracy of 98.3%, the proposed method outperforms the individual pre-trained models, namely Efficientnet, ResNet101, VGG16, and VGG19, by 2.3%, 1.71%, 2.01%, and 1.47%, respectively.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80256368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303017
Anandaraj A, Alphonse P J A
Prediction of epileptic seizures in accurate manner and on time prediction can help in improving the lifestyle of the affected people. Many computational intelligence methods have been developed for EEG signal analysis. Since they can only handle the algorithm's complexity, new strategies have been developed to obtain the desired outcome. The goal of this work is to create an innovative method that provides the highest classification performance with the least computational expenses. This work concentrates on analyzing various deep learning models and machine learning classifiers like decision tree (C4.5), Naïve Bayes (NB), Support Vector Machine (SVM), logistic regression (LR), k-nearest neighbour (k-NN) and adaboosting model. By considering the results obtained from various classifiers, it is noted that C4.5 works well compared to other approaches. By examining the results obtained from various classifiers, this research provides valuable insights into the ensemble machine learning approaches for enhancing the accuracy and efficiency of epileptic seizure prediction from EEG signals.
{"title":"Enhancing Epileptic Seizure Prediction with Machine Learning and EEG Analysis","authors":"Anandaraj A, Alphonse P J A","doi":"10.53759/7669/jmc202303017","DOIUrl":"https://doi.org/10.53759/7669/jmc202303017","url":null,"abstract":"Prediction of epileptic seizures in accurate manner and on time prediction can help in improving the lifestyle of the affected people. Many computational intelligence methods have been developed for EEG signal analysis. Since they can only handle the algorithm's complexity, new strategies have been developed to obtain the desired outcome. The goal of this work is to create an innovative method that provides the highest classification performance with the least computational expenses. This work concentrates on analyzing various deep learning models and machine learning classifiers like decision tree (C4.5), Naïve Bayes (NB), Support Vector Machine (SVM), logistic regression (LR), k-nearest neighbour (k-NN) and adaboosting model. By considering the results obtained from various classifiers, it is noted that C4.5 works well compared to other approaches. By examining the results obtained from various classifiers, this research provides valuable insights into the ensemble machine learning approaches for enhancing the accuracy and efficiency of epileptic seizure prediction from EEG signals.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"2016 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87780294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303024
R. B, K. Kumar
In 5G networks, the demand for IoT devices is increasing due to their applications. With the development and widespread adoption of 5G networks, the Internet of Things (IoT) coverage issue will collide with the issue of enormous nodes. In this paper, a parallell y implemented Hybridised Mayfly and Rat Swarm Optimizer algorithm utilising Hadoop is proposed for optimising the IoT coverage and node redundancy in IoT with massive nodes, which automatically lengthens the IoT's lifecycle. Initially, parallel operation d ivides the IoT coverage problem involving massive nodes into numerous smaller problems in order to reduce the problem's scope, which are then solved using parallel Hadoop. Using the flight behaviour and mating process of mayflies, we optimise the coverage problem here. Rats' pursuing and attacking behaviours are employed to optimise the redundancy problem. Then, select the non critical nodes from the critical nodes in an optimal manner. Lastly, parallel operation effectively resolves the IoT's coverage issu e through massive nodes by strategically extending the IoT's lifespan. Using the NS2 application, the proposed method is simulated. Computation Time, Energy efficiency, Lifespan, Lifetime, and Remaining Nodes are analysed as performance metrics. The propos ed MOP Hyb MFRS IoT 5GN method achieves lower computation times of 98.38%, 92.34%, and 97.45%, higher lifetime of 89.34%, 83.12%, and 88.96%, and lower remaining time as 91.25%, 79.90%, and 92.88% compared with existing methods such as parallel genetic alg orithm spread the lifespan of internet of things on 5G networks (MPGA IoT 5GN)
在5G网络中，由于其应用，对物联网设备的需求正在增加。随着5G网络的发展和广泛采用，物联网(IoT)覆盖问题将与巨大节点问题发生碰撞。本文提出了一种利用Hadoop并行实现的Hybridised Mayfly and Rat Swarm Optimizer算法，用于优化大节点物联网中的物联网覆盖和节点冗余，从而自动延长物联网的生命周期。最初，并行操作d将涉及大量节点的物联网覆盖问题划分为许多较小的问题，以减小问题的范围，然后使用并行Hadoop解决问题。利用蜉蝣的飞行行为和交配过程，对覆盖问题进行了优化。利用老鼠的追逐和攻击行为来优化冗余问题。然后，以最优方式从关键节点中选择非关键节点。最后，并行运行通过战略性地延长物联网的生命周期，有效地解决了物联网通过大规模节点的覆盖问题。利用NS2应用程序对该方法进行了仿真。计算时间、能效、寿命、生存期和剩余节点作为性能指标进行分析。与并行遗传算法等现有方法相比，本文提出的MOP Hyb MFRS IoT 5GN方法的计算次数分别为98.38%、92.34%和97.45%，寿命分别为89.34%、83.12%和88.96%，剩余时间分别为91.25%、79.90%和92.88%，延长了5G网络(MPGA IoT 5GN)的物联网寿命。
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Pub Date : 2023-07-05DOI: 10.53759/7669/jmc202303029
D. Kim
The fields of power electronics and fuel cells have emerged as key players in the development of sustainable power sources. The prevailing demand for fuel cells is projected to increase as they become the principal source of energy for portable electronics. A high-efficiency converter is a crucial component of the whole system and an absolute must for this specific use case. This is because the converter has a huge impact on the portability of the system as a whole in terms of size, efficiency, cost, and reliability. Choosing appropriate converter architecture is a key and important aspect of increasing the network of fuel cells for embedded systems since the converters alone accomplishes such as significant role in determining the overall efficiency of the system in this study, we take a look at the many topologies configurations of AC inverters and DC converters that are employed in the installation of fuel cells for autonomous and portable. The techniques of switching used in fuel cell energy conditioning are also analyzed in this research. The current issue with DC converters and AC inverters is also discussed at the end of this paper.
{"title":"Artificial Intelligence and Agent based Modeling for Power System Engineering","authors":"D. Kim","doi":"10.53759/7669/jmc202303029","DOIUrl":"https://doi.org/10.53759/7669/jmc202303029","url":null,"abstract":"The fields of power electronics and fuel cells have emerged as key players in the development of sustainable power sources. The prevailing demand for fuel cells is projected to increase as they become the principal source of energy for portable electronics. A high-efficiency converter is a crucial component of the whole system and an absolute must for this specific use case. This is because the converter has a huge impact on the portability of the system as a whole in terms of size, efficiency, cost, and reliability. Choosing appropriate converter architecture is a key and important aspect of increasing the network of fuel cells for embedded systems since the converters alone accomplishes such as significant role in determining the overall efficiency of the system in this study, we take a look at the many topologies configurations of AC inverters and DC converters that are employed in the installation of fuel cells for autonomous and portable. The techniques of switching used in fuel cell energy conditioning are also analyzed in this research. The current issue with DC converters and AC inverters is also discussed at the end of this paper.","PeriodicalId":91709,"journal":{"name":"International journal of machine learning and computing","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82971837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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