Pub Date : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100051
Rusul Abdulridha Muttashar, R. Fyath
A nine-dimensional (9D) chaotic-based hybrid digital/optical Encryption (HDOE) scheme is proposed for a double color image. The 9D chaotic sequences are grouped into three independent sets, each responsible for the encryption of one of the RGB channels. Thus, the three color channels are encrypted separately, enhancing the security level and robustness of the encryption process. The digital encryption (DE) part uses fusion and scrambling and is interrupted by a chaotic color image controlled by three of the chaotic sequences, one from each set. The optical encryption (OE) part is implemented in the optical Fourier transform (FT) domain and assisted by two chaotic phase masks (CPMs) for phase-encoding operation. The color CPM is constructed by three chaotic sequences, one from each chaotic sequences sets. The proposed HDOE scheme yields very-high entropy (7.9988), which is very close to the ideal case (8).
{"title":"A Hybrid Digital and Optical Double Color Image Encryption Scheme Using a Nine-Dimensional Chaotic System","authors":"Rusul Abdulridha Muttashar, R. Fyath","doi":"10.1109/DeSE58274.2023.10100051","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100051","url":null,"abstract":"A nine-dimensional (9D) chaotic-based hybrid digital/optical Encryption (HDOE) scheme is proposed for a double color image. The 9D chaotic sequences are grouped into three independent sets, each responsible for the encryption of one of the RGB channels. Thus, the three color channels are encrypted separately, enhancing the security level and robustness of the encryption process. The digital encryption (DE) part uses fusion and scrambling and is interrupted by a chaotic color image controlled by three of the chaotic sequences, one from each set. The optical encryption (OE) part is implemented in the optical Fourier transform (FT) domain and assisted by two chaotic phase masks (CPMs) for phase-encoding operation. The color CPM is constructed by three chaotic sequences, one from each chaotic sequences sets. The proposed HDOE scheme yields very-high entropy (7.9988), which is very close to the ideal case (8).","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"3 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127472895","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099719
Umesha Selv, Sahar Al-Sudani
The objective of this paper is to develop a system for predicting diabetes and related conditions in patients using Machine Learning techniques with high degree of accuracy so patients can be treated at an early stage, which could provide a life-saving impact. A Backpropagation Neural Network (BPNN) with 50 nodes in hidden layer and K-Nearest Neighbour (KNN) were created to predict diabetes in patients. A Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) with 100 nodes in hidden layer were created to predict blood glucose levels and generate early warning signs for short-term diabetes complications such as hypoglycaemia, hyperglycaemia and pre-diabetic. The BPNN model achieved the best performance for predicting diabetes with an average classification accuracy of 76.7% and was compared with KNN model which achieved an average classification accuracy of 74.0%. While LSTM model achieved the best performance for predicting blood glucose levels with an average classification accuracy of 90.0%, 88.8% sensitivity, 88.0% specificity, 93.0% positive predictive value and 81.3% negative predictive value, and was compared with RNN model which achieved an average classification accuracy of 84.1%. Obtaining highly accurate predictions on future readings shows potential for the system to be used by healthcare care personnel to determine the right form of treatment at an early stage so patients can be treated in advance. The developed system is at its early stages with two fully working tools and shows promise for further development to increase its effectiveness and performance for complete professional use.
{"title":"Towards Building a System for Predicting Diabetes and related conditions using Machine Learning","authors":"Umesha Selv, Sahar Al-Sudani","doi":"10.1109/DeSE58274.2023.10099719","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099719","url":null,"abstract":"The objective of this paper is to develop a system for predicting diabetes and related conditions in patients using Machine Learning techniques with high degree of accuracy so patients can be treated at an early stage, which could provide a life-saving impact. A Backpropagation Neural Network (BPNN) with 50 nodes in hidden layer and K-Nearest Neighbour (KNN) were created to predict diabetes in patients. A Long Short-Term Memory (LSTM) network and Recurrent Neural Network (RNN) with 100 nodes in hidden layer were created to predict blood glucose levels and generate early warning signs for short-term diabetes complications such as hypoglycaemia, hyperglycaemia and pre-diabetic. The BPNN model achieved the best performance for predicting diabetes with an average classification accuracy of 76.7% and was compared with KNN model which achieved an average classification accuracy of 74.0%. While LSTM model achieved the best performance for predicting blood glucose levels with an average classification accuracy of 90.0%, 88.8% sensitivity, 88.0% specificity, 93.0% positive predictive value and 81.3% negative predictive value, and was compared with RNN model which achieved an average classification accuracy of 84.1%. Obtaining highly accurate predictions on future readings shows potential for the system to be used by healthcare care personnel to determine the right form of treatment at an early stage so patients can be treated in advance. The developed system is at its early stages with two fully working tools and shows promise for further development to increase its effectiveness and performance for complete professional use.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126383406","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100194
Sandhua M N, A. Hussain, D. Al-Jumeily, Basheera M. Mahmmod, S. Abdulhussain
Amongst different types of cancer, skin cancer has shown an increasing trend over the decade. Skin cancer is mainly caused due to exposure of human skin to ultraviolet rays, due to overexposure to the sun. Early diagnosis of skin cancer can help in preventing the further spread of the deadly disease. But there is a lack of clinical services and expertise, and this situation has worsened due to the ongoing pandemic. An automated system to guide the clinicians is the need of the hour. There are a lot of AI-based systems developed using datasets that are publicly available. Especially, deep learning-based solutions are available which detect the malignancy and classify it into a particular type of malignancy. CNN is a proven technology in the diagnosis of skin cancer. Various models based on transfer learning have been developed. The various systems that have been developed are still in the early stages of clinical deployment. There are still many challenges and open issues. It is proposed to investigate the work done so far and to develop a model with matching or improved performance. HAM 10000 dataset containing dermoscopic images is used for the research work. Dataset preprocessing is done to resize the images and to augment the dataset. The class imbalance has been addressed using data augmentation. Three models have been trained and tested. CNN-based, MobileNet V2 and Resnet50 based models have been built and tested. Achieved a validation accuracy of 86% for CNN, 96% for MobileNet and 89% for ResNet50.
{"title":"Deep Learning-Based Skin Cancer Identification","authors":"Sandhua M N, A. Hussain, D. Al-Jumeily, Basheera M. Mahmmod, S. Abdulhussain","doi":"10.1109/DeSE58274.2023.10100194","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100194","url":null,"abstract":"Amongst different types of cancer, skin cancer has shown an increasing trend over the decade. Skin cancer is mainly caused due to exposure of human skin to ultraviolet rays, due to overexposure to the sun. Early diagnosis of skin cancer can help in preventing the further spread of the deadly disease. But there is a lack of clinical services and expertise, and this situation has worsened due to the ongoing pandemic. An automated system to guide the clinicians is the need of the hour. There are a lot of AI-based systems developed using datasets that are publicly available. Especially, deep learning-based solutions are available which detect the malignancy and classify it into a particular type of malignancy. CNN is a proven technology in the diagnosis of skin cancer. Various models based on transfer learning have been developed. The various systems that have been developed are still in the early stages of clinical deployment. There are still many challenges and open issues. It is proposed to investigate the work done so far and to develop a model with matching or improved performance. HAM 10000 dataset containing dermoscopic images is used for the research work. Dataset preprocessing is done to resize the images and to augment the dataset. The class imbalance has been addressed using data augmentation. Three models have been trained and tested. CNN-based, MobileNet V2 and Resnet50 based models have been built and tested. Achieved a validation accuracy of 86% for CNN, 96% for MobileNet and 89% for ResNet50.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391604","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099823
W. A. Majeed, S. Aliesawi
This paper presents a voice-controlled and low-cost design of a practical smart house system (SHS). The proposed system uses to remotely control all digital devices using voice commands, and gives safety by identifying fire and recognizes suspicious movement. It is based on group of sensors, Arduino board, GSM and Bluetooth as a wireless technology to connect system components. Some appliances and sensors are directly associated with the Arduino. These appliances can be effectively controlled by user-friendly mobile interface. The microcontroller can additionally send signals if it identifies any unusual movement. To show the reliability and viability of this framework, devices such as LED lights, temperature, PIR Motion and ultrasonic distance detection sensors are incorporated with the proposed system. The proposed system is shown to be an easily configurable system, sensible, secure, cost effective and required less power comparing with other reviewed systems. Therefore, it is an appropriate and great candidate for the SHS.
{"title":"Design and Implementation a Low-Cost Smart House Automation System using Bluetooth and Sensor Technology","authors":"W. A. Majeed, S. Aliesawi","doi":"10.1109/DeSE58274.2023.10099823","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099823","url":null,"abstract":"This paper presents a voice-controlled and low-cost design of a practical smart house system (SHS). The proposed system uses to remotely control all digital devices using voice commands, and gives safety by identifying fire and recognizes suspicious movement. It is based on group of sensors, Arduino board, GSM and Bluetooth as a wireless technology to connect system components. Some appliances and sensors are directly associated with the Arduino. These appliances can be effectively controlled by user-friendly mobile interface. The microcontroller can additionally send signals if it identifies any unusual movement. To show the reliability and viability of this framework, devices such as LED lights, temperature, PIR Motion and ultrasonic distance detection sensors are incorporated with the proposed system. The proposed system is shown to be an easily configurable system, sensible, secure, cost effective and required less power comparing with other reviewed systems. Therefore, it is an appropriate and great candidate for the SHS.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123180250","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099655
A. Alatrany, A. Hussain, Saad S J Alatrany, J. Mustafina, D. Al-Jumeily
Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.
{"title":"Comparison of Machine Learning Algorithms for classification of Late Onset Alzheimer's disease","authors":"A. Alatrany, A. Hussain, Saad S J Alatrany, J. Mustafina, D. Al-Jumeily","doi":"10.1109/DeSE58274.2023.10099655","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099655","url":null,"abstract":"Alzheimer's disease (AD) is neurodegenerative brain illness. It is classified as a degenerative illness since it worsens with time. A multitude of risk factors contribute to the development of Alzheimer's disease, such as demographic information, test scores, and genetics. The paper presents the comparison of machine learning algorithms to identify the highest accuracy level in classification of Late Onset Alzheimer's disease. Dataset from the Alzheimer's Disease Neuroimaging Initiative has been requested to train and test the machine learning models. The dataset included 539 normal controls and 411 Alzheimer's Disease individuals. A main dataset includes variables that are often used in clinical practice to develop the machine learning algorithms. Another dataset was created that exclusively included subjects aged 65 and up in order to assess the accuracy of algorithms used to diagnose late-onset Alzheimer's disease. According to the benchmarked findings, Linear Discriminant Analysis performed the most efficiently, achieving accuracy and an F1-score of 1.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"623 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122264274","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099634
Koe Yueh, Intan Farahana Binti Kamsin, Jerry Chong Chean Fuh
The COVID-19 pandemic has caused an acceleration unlike any other in terms of digital and technological acceleration for the entire world and also within Malaysia. The sudden and rapid need for organisations as well as businesses to shift their day- to-day operations online has changed the way people are working everywhere. And what that means is now more than ever, there is a huge increase in demand for a workforce that is ready and can pioneer this new age of rising technological needs in conjunction with the government's aim of heading towards Industrial Revolution 4.0 (IR 4.0). Micro-credential (MC) has grown in popularity in recent years and have been labelled as a new disruptor to lifelong learning and higher learning. The Malaysian workforce and job seekers now have more options in their reskilling and upskilling efforts as they seek to remain relevant in the present-day job market which has shifted towards a digital transformation. An extensive study is proposed to be done to explore the current status quo of MC in Malaysia from the viewpoint of the hiring parties in the tech-related job markets as well as how MC will be able to play a part in the continuous growth of the tech and digital ecosystem in Malaysia.
2019冠状病毒病大流行在数字和技术加速方面给整个世界以及马来西亚带来了前所未有的加速。组织和企业将日常业务转移到网上的突然而迅速的需求改变了人们在任何地方的工作方式。这意味着，与以往任何时候相比，现在对劳动力的需求大幅增加，这些劳动力准备就绪，可以引领这个不断增长的技术需求的新时代，并与政府迈向工业革命4.0 (IR 4.0)的目标相结合。近年来，微证书(MC)越来越受欢迎，并被视为终身学习和高等教育的新颠覆者。马来西亚的劳动力和求职者现在在技能再培训和技能提升方面有更多的选择，因为他们寻求在当今的就业市场中保持相关性，而就业市场已经转向数字化转型。建议进行广泛的研究，从技术相关就业市场的招聘方的角度探索马来西亚MC的现状，以及MC如何能够在马来西亚技术和数字生态系统的持续增长中发挥作用。
{"title":"The Acceptance and Readiness of Micro-credentials and its Barriers in the Tech-related Job Market in Malaysia","authors":"Koe Yueh, Intan Farahana Binti Kamsin, Jerry Chong Chean Fuh","doi":"10.1109/DeSE58274.2023.10099634","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099634","url":null,"abstract":"The COVID-19 pandemic has caused an acceleration unlike any other in terms of digital and technological acceleration for the entire world and also within Malaysia. The sudden and rapid need for organisations as well as businesses to shift their day- to-day operations online has changed the way people are working everywhere. And what that means is now more than ever, there is a huge increase in demand for a workforce that is ready and can pioneer this new age of rising technological needs in conjunction with the government's aim of heading towards Industrial Revolution 4.0 (IR 4.0). Micro-credential (MC) has grown in popularity in recent years and have been labelled as a new disruptor to lifelong learning and higher learning. The Malaysian workforce and job seekers now have more options in their reskilling and upskilling efforts as they seek to remain relevant in the present-day job market which has shifted towards a digital transformation. An extensive study is proposed to be done to explore the current status quo of MC in Malaysia from the viewpoint of the hiring parties in the tech-related job markets as well as how MC will be able to play a part in the continuous growth of the tech and digital ecosystem in Malaysia.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"40 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120852726","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099799
Aditya Yadav
To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly.
{"title":"COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images","authors":"Aditya Yadav","doi":"10.1109/DeSE58274.2023.10099799","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099799","url":null,"abstract":"To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125733989","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100062
Ruqaiya D. Jalal, S. Aliesawi
Recently, underwater wireless sensor networks (UWSNs) have been emphasized due to their immense value in monitoring the underwater environment and expanding applications for target recognition and underwater information gathering. Battery power is restricted underwater, and it is also difficult to replace, which limits the power supply. As a result, studies and research seek to extend the life of the network. The proposed Threshold Sensitive Energy Efficiency Sensor Network (TEEN) protocol, along with particle swarming optimization (PSO) and BAT algorithms disclosed in this paper, attempts to improve network lifetime and power consumption via optimal node distribution and cluster header selection. The K-mean technique is used in each algorithm that separates nodes into clusters and selects for each cluster a point to be the central point from which to choose the best node to be the block head (CH). This selection is based on the node with the most energy as well as the node closest to the center point. After this stage, the proposed algorithms continue with Particle Swarm Optimization (PSO), and BAT Apply Cluster Head Update (CH), until the best map is produced. The results revealed that the proposed protocol resulted in a significant reduction in power consumption and network lifetime compared to the original protocol. The results also show that TEEN enhanced with BAT is better than TEEN enhanced with PSO.
{"title":"Enhancing TEEN Protocol using the Particle Swarm Optimization and BAT Algorithms in Underwater Wireless Sensor Network","authors":"Ruqaiya D. Jalal, S. Aliesawi","doi":"10.1109/DeSE58274.2023.10100062","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100062","url":null,"abstract":"Recently, underwater wireless sensor networks (UWSNs) have been emphasized due to their immense value in monitoring the underwater environment and expanding applications for target recognition and underwater information gathering. Battery power is restricted underwater, and it is also difficult to replace, which limits the power supply. As a result, studies and research seek to extend the life of the network. The proposed Threshold Sensitive Energy Efficiency Sensor Network (TEEN) protocol, along with particle swarming optimization (PSO) and BAT algorithms disclosed in this paper, attempts to improve network lifetime and power consumption via optimal node distribution and cluster header selection. The K-mean technique is used in each algorithm that separates nodes into clusters and selects for each cluster a point to be the central point from which to choose the best node to be the block head (CH). This selection is based on the node with the most energy as well as the node closest to the center point. After this stage, the proposed algorithms continue with Particle Swarm Optimization (PSO), and BAT Apply Cluster Head Update (CH), until the best map is produced. The results revealed that the proposed protocol resulted in a significant reduction in power consumption and network lifetime compared to the original protocol. The results also show that TEEN enhanced with BAT is better than TEEN enhanced with PSO.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191644","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10100072
Bilel Najeh, Aicha Idriss Hentati, M. Fourati, L. Chaari, A. Alanezi
During this last decade, Unmanned Aerial Vehicles (UAVs) are being useful in complex missions and critical sce-narios. In this paper, we propose novel architecture for data gathering and storage in which data is collected from IoT devices using cooperative UAVs. The main purpose of our scheme is to ensure secure data acquisition and storage using the BlockChain (BC) technology. The performance of the proposed scheme is analyzed via experimental evaluation.
{"title":"BlockChain-based Cooperative UAVs for Secure Data Acquisition and Storage","authors":"Bilel Najeh, Aicha Idriss Hentati, M. Fourati, L. Chaari, A. Alanezi","doi":"10.1109/DeSE58274.2023.10100072","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10100072","url":null,"abstract":"During this last decade, Unmanned Aerial Vehicles (UAVs) are being useful in complex missions and critical sce-narios. In this paper, we propose novel architecture for data gathering and storage in which data is collected from IoT devices using cooperative UAVs. The main purpose of our scheme is to ensure secure data acquisition and storage using the BlockChain (BC) technology. The performance of the proposed scheme is analyzed via experimental evaluation.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132138353","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 : 2023-01-09DOI: 10.1109/DeSE58274.2023.10099854
S. A. Jerjees, Hala Jassim Mohammed, Hayder S. Radeaf, Basheera M. Mahmmod, S. Abdulhussain
Machine learning, a part of artificial intelligence, is recently used in speech enhancement algorithms (SE). The primary focus of SE is finding the original speech signal from the distorted one. Specifically, deep learning is used in SE because it handles nonlinear mapping problems for complicated features. In this paper, Charlier polynomials-based discrete transform, simply discrete Charlier transform (DCHT), has been used to get the spectra of the noisy signal using a fully connected neural network. Deep learning effectively acquires the context information of speech signal and gets enhanced speech with good quality and intelligibility properties. The proposed algorithm is tested experimentally through self-comparison to obtain the best speech enhancement models corresponding to the DCHT parameter. The experiment is performed with different values of the DCHT parameter. In addition, the well-known TIMIT database is used for evaluation purposes. Different speech measures are used in the experiment. The realized results show the ability of the trained model based on DCHT to enhance the speech signal and provide good results on specific conditions.
{"title":"Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform","authors":"S. A. Jerjees, Hala Jassim Mohammed, Hayder S. Radeaf, Basheera M. Mahmmod, S. Abdulhussain","doi":"10.1109/DeSE58274.2023.10099854","DOIUrl":"https://doi.org/10.1109/DeSE58274.2023.10099854","url":null,"abstract":"Machine learning, a part of artificial intelligence, is recently used in speech enhancement algorithms (SE). The primary focus of SE is finding the original speech signal from the distorted one. Specifically, deep learning is used in SE because it handles nonlinear mapping problems for complicated features. In this paper, Charlier polynomials-based discrete transform, simply discrete Charlier transform (DCHT), has been used to get the spectra of the noisy signal using a fully connected neural network. Deep learning effectively acquires the context information of speech signal and gets enhanced speech with good quality and intelligibility properties. The proposed algorithm is tested experimentally through self-comparison to obtain the best speech enhancement models corresponding to the DCHT parameter. The experiment is performed with different values of the DCHT parameter. In addition, the well-known TIMIT database is used for evaluation purposes. Different speech measures are used in the experiment. The realized results show the ability of the trained model based on DCHT to enhance the speech signal and provide good results on specific conditions.","PeriodicalId":346847,"journal":{"name":"2023 15th International Conference on Developments in eSystems Engineering (DeSE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129819690","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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