Pub Date : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075753
Chetan Panse, Aayush Chaskar
The term “5G” refers to the latest advancements in mobile technology. The next key stage of mobile communications standards after the soon-to-be-implemented 4G standards is 5G. Due to the development of 5G technology, the bulk of high bandwidth users will change how they access their phones. With 5G pushed via a VOIP-equipped smartphone, people would experience a volume of calls and data transfer unlike anything else. [1] The word “5G” relates to the generation of wireless telecommunications, which will change multiple areas of our daily life. Due to emerging mobile technologies like virtual reality applications, HD video consumption, and cloud-based entertainment, mobile users and their usage is expanding very quickly. The rate of increase in demand and the predicted trends of new technologies, such as driverless vehicles and virtual reality, will far exceed the capacity of 4G networks in a few years Consequently, multiple attempts have been made by industry experts and research associations to make 5G networks a reality soon. Academicians and thought leaders have agreed that these new network systems would employ highly exciting, developed technologies like SDN and NFV to realize their objectives. The transmission speed of 5G is a lot greater than that of the present network Data transmission speeds up to 10Gbps, which are 10 to 100 times faster than 4G and 4G- LTE will be available with 5G. To facilitate the development of new services, 5G is anticipated to merge ultra-broadband networks and incorporate new-age technologies like the Internet of Things (IoT), blockchain, big data, artificial intelligence, machine learning and cloud computing.
{"title":"Analysing the Need for 5G Networks based on Smartphone Market Penetration","authors":"Chetan Panse, Aayush Chaskar","doi":"10.1109/ICCT56969.2023.10075753","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075753","url":null,"abstract":"The term “5G” refers to the latest advancements in mobile technology. The next key stage of mobile communications standards after the soon-to-be-implemented 4G standards is 5G. Due to the development of 5G technology, the bulk of high bandwidth users will change how they access their phones. With 5G pushed via a VOIP-equipped smartphone, people would experience a volume of calls and data transfer unlike anything else. [1] The word “5G” relates to the generation of wireless telecommunications, which will change multiple areas of our daily life. Due to emerging mobile technologies like virtual reality applications, HD video consumption, and cloud-based entertainment, mobile users and their usage is expanding very quickly. The rate of increase in demand and the predicted trends of new technologies, such as driverless vehicles and virtual reality, will far exceed the capacity of 4G networks in a few years Consequently, multiple attempts have been made by industry experts and research associations to make 5G networks a reality soon. Academicians and thought leaders have agreed that these new network systems would employ highly exciting, developed technologies like SDN and NFV to realize their objectives. The transmission speed of 5G is a lot greater than that of the present network Data transmission speeds up to 10Gbps, which are 10 to 100 times faster than 4G and 4G- LTE will be available with 5G. To facilitate the development of new services, 5G is anticipated to merge ultra-broadband networks and incorporate new-age technologies like the Internet of Things (IoT), blockchain, big data, artificial intelligence, machine learning and cloud computing.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973076","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075964
Vineela Chandra Dodda, Lakshmi Kuruguntla, Shaik Razak, A. Mandpura, S. Chinnadurai, Karthikeyan Elumalai
Seismic interpretation is essential to obtain infor-mation about the geological layers from seismic data. Manual interpretation, however, necessitates additional pre-processing stages and requires more time and effort. In recent years, Deep Learning (DL) has been applied in the geophysical domain to solve various problems such as denoising, inversion, fault estimation, horizon estimation, etc. In this paper, we propose an Attention-based Deep Convolutional Neural Network (ACNN) for seismic lithology prediction. We used Continuous Wavelet Transform (CWT) to obtain the time-frequency spectrum of seismic data which is further used to train the network. The attention module is used to scale the features from the convolutional layers thus prioritizing the prominent features in the data. We validated the results on blind wells and observed that the proposed method had shown improved accuracy when compared to the existing basic CNN.
{"title":"Seismic Lithology Interpretation using Attention based Convolutional Neural Networks","authors":"Vineela Chandra Dodda, Lakshmi Kuruguntla, Shaik Razak, A. Mandpura, S. Chinnadurai, Karthikeyan Elumalai","doi":"10.1109/ICCT56969.2023.10075964","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075964","url":null,"abstract":"Seismic interpretation is essential to obtain infor-mation about the geological layers from seismic data. Manual interpretation, however, necessitates additional pre-processing stages and requires more time and effort. In recent years, Deep Learning (DL) has been applied in the geophysical domain to solve various problems such as denoising, inversion, fault estimation, horizon estimation, etc. In this paper, we propose an Attention-based Deep Convolutional Neural Network (ACNN) for seismic lithology prediction. We used Continuous Wavelet Transform (CWT) to obtain the time-frequency spectrum of seismic data which is further used to train the network. The attention module is used to scale the features from the convolutional layers thus prioritizing the prominent features in the data. We validated the results on blind wells and observed that the proposed method had shown improved accuracy when compared to the existing basic CNN.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"421 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132553275","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076174
Omkar Subhash Ghongade, S. K. S. Reddy, Srilatha Tokala, K. Hajarathaiah, M. Enduri, Satish Anamalamudi
Heart disease is a major cause of death and disability across the world. Heart disease mortality and morbidity rates can be greatly decreased with early detection and treatment. Hence, the development of efficient and accurate methods for early diagnosis of heart disease has become a priority in the medical field. In this study, we did a comparative study of exiting supervised machine learning approaches for predicting heart disease diagnosis and also improved the accuracy of KNN by changing K values. We used a dataset that consists of a variety of features such as age, gender and other important indicators for heart disease diagnosis. We then explored and evaluated traditional ML algorithms such as logistic regression, decision tree, random forest and SVM for the predictive analysis. A number of criteria, including accuracy, precision, recall, and F1 Score, were used to assess the models' performance. This study provides evidence that ML algorithms can be used to forecast the diagnosis of heart disease. Healthcare providers and medical practitioners can utilize the outcomes of this study for early detection and management of cardiac disease. Further research will aim to analyse and evaluate additional machine learning algorithms to enhance precision and performance.
{"title":"A Comparison of Neural Networks and Machine Learning Methods for Prediction of Heart Disease","authors":"Omkar Subhash Ghongade, S. K. S. Reddy, Srilatha Tokala, K. Hajarathaiah, M. Enduri, Satish Anamalamudi","doi":"10.1109/ICCT56969.2023.10076174","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076174","url":null,"abstract":"Heart disease is a major cause of death and disability across the world. Heart disease mortality and morbidity rates can be greatly decreased with early detection and treatment. Hence, the development of efficient and accurate methods for early diagnosis of heart disease has become a priority in the medical field. In this study, we did a comparative study of exiting supervised machine learning approaches for predicting heart disease diagnosis and also improved the accuracy of KNN by changing K values. We used a dataset that consists of a variety of features such as age, gender and other important indicators for heart disease diagnosis. We then explored and evaluated traditional ML algorithms such as logistic regression, decision tree, random forest and SVM for the predictive analysis. A number of criteria, including accuracy, precision, recall, and F1 Score, were used to assess the models' performance. This study provides evidence that ML algorithms can be used to forecast the diagnosis of heart disease. Healthcare providers and medical practitioners can utilize the outcomes of this study for early detection and management of cardiac disease. Further research will aim to analyse and evaluate additional machine learning algorithms to enhance precision and performance.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"30 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133267266","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075658
Dyd Pradeep, Bitragunta Vivek Vardhan, Shaik Raiak, I. Muniraj, Karthikeyan Elumalai, S. Chinnadurai
As global competitiveness in the semiconductor sector intensifies, companies must continue to improve manufacturing techniques and productivity in order to sustain competitive advantages. In this research paper, we have used machine learning (ML) techniques on computational data collected from the sensors in the manufacturing unit to predict the wafer failure in the manufacturing of the semiconductors and then lower the equipment failure by enabling predictive maintenance and thereby increasing productivity. Training time has been greatly reduced through the proposed feature selection process with maintaining high accuracy. Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Tree Classifier, Extreme Gradient Boost, and Neural Networks are some of the model-building techniques that are performed in this work. Numerous case studies were undertaken to examine accuracy and precision. Random Forest Classifier surpassed all the other models with an accuracy of over 93.62%. Numerical results also show that the ML techniques can be implemented to predict wafer failure, perform predictive maintenance and increase the productivity of manufacturing the semiconductors.
{"title":"Optimal Predictive Maintenance Technique for Manufacturing Semiconductors using Machine Learning","authors":"Dyd Pradeep, Bitragunta Vivek Vardhan, Shaik Raiak, I. Muniraj, Karthikeyan Elumalai, S. Chinnadurai","doi":"10.1109/ICCT56969.2023.10075658","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075658","url":null,"abstract":"As global competitiveness in the semiconductor sector intensifies, companies must continue to improve manufacturing techniques and productivity in order to sustain competitive advantages. In this research paper, we have used machine learning (ML) techniques on computational data collected from the sensors in the manufacturing unit to predict the wafer failure in the manufacturing of the semiconductors and then lower the equipment failure by enabling predictive maintenance and thereby increasing productivity. Training time has been greatly reduced through the proposed feature selection process with maintaining high accuracy. Logistic Regression, Random Forest Classifier, Support Vector Machine, Decision Tree Classifier, Extreme Gradient Boost, and Neural Networks are some of the model-building techniques that are performed in this work. Numerous case studies were undertaken to examine accuracy and precision. Random Forest Classifier surpassed all the other models with an accuracy of over 93.62%. Numerical results also show that the ML techniques can be implemented to predict wafer failure, perform predictive maintenance and increase the productivity of manufacturing the semiconductors.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115103626","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075848
R. Sriranjani, N. Hemavathi, A. Parvathy, B. Salini, L. Nandhini
As smart grid enables two-way flow of data and electricity with Advanced Metering Infrastructure, it is prone to security vulnerabilities. Sybil attack, one such vulnerability exhibits multiple identities of same node. As a consequence, the compromised or malicious nodes present in smart grid inject false information that would cause a serious impact in a critical infrastructure i.e. smart grid. Hence, the proposal aims to detect this attack based on node's Received Signal Strength, address, energy consumption and distance using machine learning algorithm. Support vector machine outperforms other machine learning algorithms like logistic regression, K-Nearest Neighborhood, Naive Baye's, and K-Nearest Neighborhood in terms of accuracy, training time, misclassification cost, prediction speed, sensitivity or recall, specificity, F1 score, precision, and Area Under the Curve (AUC) and Receiver Operating Characteristic Curve (ROC). Further, the performance of the model is optimized using hyper parameter tuning. The proposal is implemented in MATLAB. The results exhibit 96.5% accuracy that clearly demonstrates the efficacy of the model.
{"title":"Received Signal Strength and Optimized Support Vector Machine based Sybil Attack Detection Scheme in Smart Grid","authors":"R. Sriranjani, N. Hemavathi, A. Parvathy, B. Salini, L. Nandhini","doi":"10.1109/ICCT56969.2023.10075848","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075848","url":null,"abstract":"As smart grid enables two-way flow of data and electricity with Advanced Metering Infrastructure, it is prone to security vulnerabilities. Sybil attack, one such vulnerability exhibits multiple identities of same node. As a consequence, the compromised or malicious nodes present in smart grid inject false information that would cause a serious impact in a critical infrastructure i.e. smart grid. Hence, the proposal aims to detect this attack based on node's Received Signal Strength, address, energy consumption and distance using machine learning algorithm. Support vector machine outperforms other machine learning algorithms like logistic regression, K-Nearest Neighborhood, Naive Baye's, and K-Nearest Neighborhood in terms of accuracy, training time, misclassification cost, prediction speed, sensitivity or recall, specificity, F1 score, precision, and Area Under the Curve (AUC) and Receiver Operating Characteristic Curve (ROC). Further, the performance of the model is optimized using hyper parameter tuning. The proposal is implemented in MATLAB. The results exhibit 96.5% accuracy that clearly demonstrates the efficacy of the model.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132796325","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076198
S. I. Orakwue, H. M. Al-Khafaji, Amit Rathi
This paper compares the performance of a developed web-based application named ISP-Perf with test mobile systems (TEMs) in a mobile broadband measurement environment. The quality of service (QoS) metrics such as upload and download speeds, as well as the latency of 3G MTN network, were quantified concurrently at three major urban centers in Port Harcourt, Nigeria. The measurements have been carried out at different times of the day for a specified period. ISP-Perf has been shown to have a low error margin when compared to TEMs, hence it is recommended for measuring network performance.
{"title":"Comparative Analysis of ISP-Perf and TEMs in Mobile Broadband QoS Metrics Measurement","authors":"S. I. Orakwue, H. M. Al-Khafaji, Amit Rathi","doi":"10.1109/ICCT56969.2023.10076198","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076198","url":null,"abstract":"This paper compares the performance of a developed web-based application named ISP-Perf with test mobile systems (TEMs) in a mobile broadband measurement environment. The quality of service (QoS) metrics such as upload and download speeds, as well as the latency of 3G MTN network, were quantified concurrently at three major urban centers in Port Harcourt, Nigeria. The measurements have been carried out at different times of the day for a specified period. ISP-Perf has been shown to have a low error margin when compared to TEMs, hence it is recommended for measuring network performance.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130691921","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075966
Gaurav Goel, A. Chaturvedi
Fog computing allows for the availability of services and resources exterior of the computing resources, closer to end devices on the network edge, and finally at regions mandated by service level agreement. Fog nodes along with deployed Cloud is a strong additional support for computation. It allows for processing at the edge while yet allowing for cloud interaction. A crucial component of fog networks is load balancing, which put off some fog nodes from getting unutilized or extra loaded. Load balancing can improve service quality (QoS) factors such latency, resource usage, throughput, response or execution time, cost incurred and energy consumed for passive nodes. The job offloading and load redistribution strategies which are used in a fog network are reviewed in detail in this study. The review is divided into two categories: single parameter optimization algorithms and multi-objective parameter optimization algorithms, both with their suggested ideas. The review is also analysed in various ways, including the proportion of articles published by publisher, methods based on optimization parameters, performance evaluation metrics, simulation evaluation tools, and upcoming research areas in the fog computing field.
{"title":"A Systematic Review of Task Offloading & Load Balancing Methods in a Fog Computing Environment: Major Highlights & Research Areas","authors":"Gaurav Goel, A. Chaturvedi","doi":"10.1109/ICCT56969.2023.10075966","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075966","url":null,"abstract":"Fog computing allows for the availability of services and resources exterior of the computing resources, closer to end devices on the network edge, and finally at regions mandated by service level agreement. Fog nodes along with deployed Cloud is a strong additional support for computation. It allows for processing at the edge while yet allowing for cloud interaction. A crucial component of fog networks is load balancing, which put off some fog nodes from getting unutilized or extra loaded. Load balancing can improve service quality (QoS) factors such latency, resource usage, throughput, response or execution time, cost incurred and energy consumed for passive nodes. The job offloading and load redistribution strategies which are used in a fog network are reviewed in detail in this study. The review is divided into two categories: single parameter optimization algorithms and multi-objective parameter optimization algorithms, both with their suggested ideas. The review is also analysed in various ways, including the proportion of articles published by publisher, methods based on optimization parameters, performance evaluation metrics, simulation evaluation tools, and upcoming research areas in the fog computing field.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121952475","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076222
S. Agrwal, Sudheer Kumar Sharma, Vibhor Kant
With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.
{"title":"A Review on Unconstrained Real-Time Rotation-Invariant Face Detection","authors":"S. Agrwal, Sudheer Kumar Sharma, Vibhor Kant","doi":"10.1109/ICCT56969.2023.10076222","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076222","url":null,"abstract":"With the amazing growth of image and video databases, there is a vast need for intelligent systems to automatically understand and look at information since doing it by hand is getting very hard. Faces are significant in social interactions because they show the feelings and identity of a person. People are not much better than machines at recognizing different faces. The automatic face detection system is a key in head pose tracking, face verification, face recognition, face tracking, face animation, face modeling, facial expression recognition, age and gender recognition, and behavior analysis in a crowd. Face detection is a way for a computer to find out the size and location of a face in an image. Face detection has been an outstanding issue in computer vision literature. This paper provides an overview of pose and rotation invariant face detection approaches with architecture designs and performance on popular benchmark datasets. The benchmark datasets used for face detection are listed as their key features. This paper also talks about different applications and challenges with face detection. Also, we set up special discussions on the practical aspects of making a face-detection system that works well. We end this paper by suggesting a few promising directions for future research.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127726590","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076193
H. M. Al-Khafaji, S. Aljunid, Amit Rathi
This paper aims to enable the two-keying approach in spectral-amplitude coding optical code-division multiple-access (SAC-OCDMA) system that employs modified double weight (MDW) code. To achieve this goal, two-keying subtraction detection (TKSD) is suggested, which also declines the impact of multiuser interference (MUI) and phase-induced intensity noise (PIIN). The results of simulation test demonstrate that the TKSD is efficient in realizing the two-keying detection feature in SAC-OCDMA system with superior bit-error rate (BER) performance, security, and transmission rate.
{"title":"An Emerging Detection Design Adopting Two-Keying Technique in SAC-OCDMA-Based MDW Code","authors":"H. M. Al-Khafaji, S. Aljunid, Amit Rathi","doi":"10.1109/ICCT56969.2023.10076193","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076193","url":null,"abstract":"This paper aims to enable the two-keying approach in spectral-amplitude coding optical code-division multiple-access (SAC-OCDMA) system that employs modified double weight (MDW) code. To achieve this goal, two-keying subtraction detection (TKSD) is suggested, which also declines the impact of multiuser interference (MUI) and phase-induced intensity noise (PIIN). The results of simulation test demonstrate that the TKSD is efficient in realizing the two-keying detection feature in SAC-OCDMA system with superior bit-error rate (BER) performance, security, and transmission rate.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121410302","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076165
Vikas Yadav, M. Kumar
With cloud computing becoming an inseparable part of human daily life and making its way in every field of technology during the last decade with smarter human generation and adoption of new data policies, data security has become a significant pillar of trust. The standard of data security ensures the quality of privacy while availing various services over the internet. To achieve a step forward against always developing exploiters various algorithms have been developed which come under the wing of cryptography. Cryptography is the branch that ensures confidentiality, integrity, authentication, privacy, and security of the data of a consumer. This paper has dis-cussed and analyzed DES, triple DES, Blowfish, AES, IDEA, RC4, RSA, and ECC cryptography algorithms based on their ability of key size, block size, encryption time, decryption time, and total time in the Node JavaScript environment. Since, JavaScript powers 80% of the internet, this paper provides a production-level real-world analysis of various algorithms at present time.
{"title":"Key Cryptographic Methods in the Cloud: A Comparative Study","authors":"Vikas Yadav, M. Kumar","doi":"10.1109/ICCT56969.2023.10076165","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076165","url":null,"abstract":"With cloud computing becoming an inseparable part of human daily life and making its way in every field of technology during the last decade with smarter human generation and adoption of new data policies, data security has become a significant pillar of trust. The standard of data security ensures the quality of privacy while availing various services over the internet. To achieve a step forward against always developing exploiters various algorithms have been developed which come under the wing of cryptography. Cryptography is the branch that ensures confidentiality, integrity, authentication, privacy, and security of the data of a consumer. This paper has dis-cussed and analyzed DES, triple DES, Blowfish, AES, IDEA, RC4, RSA, and ECC cryptography algorithms based on their ability of key size, block size, encryption time, decryption time, and total time in the Node JavaScript environment. Since, JavaScript powers 80% of the internet, this paper provides a production-level real-world analysis of various algorithms at present time.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133206920","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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