Pub Date : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127355
P. Muthuvel, T. Jaswanth, S. Firoz, S. Sri, N. Mukhesh
In the domain of artificial intelligence, it’s becoming more crucial than ever to classify emotions from both text and speech (AI). In order to promote and enhance human-ma-chine interaction, it is essential to establish a broader frame-work for speech emotion recognition. Machines are currently unable to reliably classify human emotions, hence machine learning development models were created for this purpose. Many academics worldwide are attempting to improve the ac-curacy of emotion categorization systems. The two steps of this study’s creation of a speech emotion detection model are (I) tasked with managing and (ii) classification. The most pertinent feature subset was discovered using feature selection (FS). A wide variety of different vision -based paradigms were employed to address the growing demand for accurate emotion categorization all across the domain of ai technology, taking into account how crucial feature selection is. This study strategy for both the emotion categorization problem and the establishment of ml algorithms and deep learning methods. This same aforementioned work focuses on speech expression analysis & proposes a paradigm for bettering human-computer interaction through into the construction on prototype cognitive computing that categorizes feelings. The investigation aims to boost this same precision for eg in voice by applying methods for selecting features and now a spectrum different deep learning methodology, notably TensorFlow. A research also high-lights the contribution on component choice mostly in creation of powerful machine-learning algorithms towards feelings categorization.
{"title":"Emotion Recognition in Speech Signals using MFCC and Mel-Spectrogram Analysis","authors":"P. Muthuvel, T. Jaswanth, S. Firoz, S. Sri, N. Mukhesh","doi":"10.1109/ICNWC57852.2023.10127355","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127355","url":null,"abstract":"In the domain of artificial intelligence, it’s becoming more crucial than ever to classify emotions from both text and speech (AI). In order to promote and enhance human-ma-chine interaction, it is essential to establish a broader frame-work for speech emotion recognition. Machines are currently unable to reliably classify human emotions, hence machine learning development models were created for this purpose. Many academics worldwide are attempting to improve the ac-curacy of emotion categorization systems. The two steps of this study’s creation of a speech emotion detection model are (I) tasked with managing and (ii) classification. The most pertinent feature subset was discovered using feature selection (FS). A wide variety of different vision -based paradigms were employed to address the growing demand for accurate emotion categorization all across the domain of ai technology, taking into account how crucial feature selection is. This study strategy for both the emotion categorization problem and the establishment of ml algorithms and deep learning methods. This same aforementioned work focuses on speech expression analysis & proposes a paradigm for bettering human-computer interaction through into the construction on prototype cognitive computing that categorizes feelings. The investigation aims to boost this same precision for eg in voice by applying methods for selecting features and now a spectrum different deep learning methodology, notably TensorFlow. A research also high-lights the contribution on component choice mostly in creation of powerful machine-learning algorithms towards feelings categorization.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127785640","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}
Massive MIMO systems are being incorporated in 5G wireless networks owing to its high spectral efficiency. In order to achieve this efficiency, we require accurate Channel State Information (CSI) which is acquired by a training performing pilot transmission, CSI estimation and feedback. In this work, a novel technique for performing this task is proposed where channel estimation is performed at the base station. The work also proposes pilot compression for this system model. The base station sends compressed pilots to the user equipment in the downlink channel which amplifies and forwards the received signal and relays it back to the base station in the uplink channel. The performance analysis for this system model has been simulated using MATLAB and is expressed in terms of the NMSE values for different levels of compression.
{"title":"Pilot Compression Analysis for Feedback Based Channel Estimation Model in FDD Massive MIMO","authors":"Madhumitha Jayaram, Bhagyaveni Marcharla Anjaneyulu","doi":"10.1109/ICNWC57852.2023.10127323","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127323","url":null,"abstract":"Massive MIMO systems are being incorporated in 5G wireless networks owing to its high spectral efficiency. In order to achieve this efficiency, we require accurate Channel State Information (CSI) which is acquired by a training performing pilot transmission, CSI estimation and feedback. In this work, a novel technique for performing this task is proposed where channel estimation is performed at the base station. The work also proposes pilot compression for this system model. The base station sends compressed pilots to the user equipment in the downlink channel which amplifies and forwards the received signal and relays it back to the base station in the uplink channel. The performance analysis for this system model has been simulated using MATLAB and is expressed in terms of the NMSE values for different levels of compression.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923717","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127558
D. Malathi, S. Gopika, Devina Awasthi, Dorathi Jayaseeli
The internet has emerged as one of the most crucial elements of modern life. Every single person uses the internet to access knowledge, information, and all communication tools available to them. However, many who are visually impaired find it difficult to use those features and need outside aid to complete their tasks. People with visual impairments all around the world now have a wide range of new opportunities because of the invention of computers. Screen readers, audio-based environments, and other assistive features have made it easier for blind persons to utilize the workspace. Today, email is required to send confidential information. Email is a type of technology that facilitates business correspondence and lets users transmit messages to other people. The main objective of this work is to develop a voice-based email system that will enable people who are blind or visually impaired to send and receive emails using computers. It will make advantage of modern features to create a working environment that enables persons with visual impairments to do their jobs independently.
{"title":"Voice Automation Mail System for Visually Impaired","authors":"D. Malathi, S. Gopika, Devina Awasthi, Dorathi Jayaseeli","doi":"10.1109/ICNWC57852.2023.10127558","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127558","url":null,"abstract":"The internet has emerged as one of the most crucial elements of modern life. Every single person uses the internet to access knowledge, information, and all communication tools available to them. However, many who are visually impaired find it difficult to use those features and need outside aid to complete their tasks. People with visual impairments all around the world now have a wide range of new opportunities because of the invention of computers. Screen readers, audio-based environments, and other assistive features have made it easier for blind persons to utilize the workspace. Today, email is required to send confidential information. Email is a type of technology that facilitates business correspondence and lets users transmit messages to other people. The main objective of this work is to develop a voice-based email system that will enable people who are blind or visually impaired to send and receive emails using computers. It will make advantage of modern features to create a working environment that enables persons with visual impairments to do their jobs independently.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607349","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127500
R. Deepa, R. Priscilla, A. Pandi, B. Renukadevi
Chronic kidney disease (CKD) or chronic renal disease-has become a major issue with a steady growth rate. A person can survive for a maximum of 18 days, which makes a huge demand for a kidney transplant and dialysis. It is necessary to have a good model to predict this disease at an earlier stage. It can be identified using ML models. This proposal proposes a workflow to predict CKD status based on the pre-processing steps of clinical data collection, incorporating data, handling missing values with collaborative filters, and attribute selection. This proposal used seven machine models and will compare all the models and the extra tree classifier and decision tree to ensure high accuracy and minimal bias for the attribute. This research also focuses on the real-time aspects of data collection and highlights the importance of domain knowledge when using machine learning for CKD status prediction. The evolution of the proposed model shows that the model can predict CKD with an accuracy of 98.65%.
{"title":"An Early Prediction Model for Chronic Kidney Disease Using Machine Learning","authors":"R. Deepa, R. Priscilla, A. Pandi, B. Renukadevi","doi":"10.1109/ICNWC57852.2023.10127500","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127500","url":null,"abstract":"Chronic kidney disease (CKD) or chronic renal disease-has become a major issue with a steady growth rate. A person can survive for a maximum of 18 days, which makes a huge demand for a kidney transplant and dialysis. It is necessary to have a good model to predict this disease at an earlier stage. It can be identified using ML models. This proposal proposes a workflow to predict CKD status based on the pre-processing steps of clinical data collection, incorporating data, handling missing values with collaborative filters, and attribute selection. This proposal used seven machine models and will compare all the models and the extra tree classifier and decision tree to ensure high accuracy and minimal bias for the attribute. This research also focuses on the real-time aspects of data collection and highlights the importance of domain knowledge when using machine learning for CKD status prediction. The evolution of the proposed model shows that the model can predict CKD with an accuracy of 98.65%.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485379","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127551
Nithya Jayakumar, A. K. Maheshwaran, P. S. Arvind, G. Vijayaragavan
Employee attrition, also referred to as the loss of personnel over time in a business, occurs for a variety of inescapable reasons. The attrition percentage in 2022 will be 20.3%, according to the latest statistics from India. Employee attrition is a significant problem that can cause severe losses to organizations. In recent years, machine learning has emerged as a powerful tool to address this challenge by predicting employees who may leave the organization. However, the accurate prediction of employee attrition faces various challenges, including dealing with imbalanced datasets, identifying the most critical predictors, and selecting the most appropriate machine learning algorithms. In this study, the proposed solution employs a combination of data preprocessing techniques and machine learning algorithms to predict employee attrition. Our solution includes a visual representation of employee attrition, a parser to extract information from resumes, a test to assess the suitability of potential candidates and AI candidate recommendation. Evaluate the proposed solution using the Employee Attrition dataset and achieve promising results. Our solution can serve as a useful tool for HR managers to predict and visualize employee attrition trends and hire the right candidates for upcoming vacancies.
{"title":"On-Demand Job-Based Recruitment For Organisations Using Artificial Intelligence","authors":"Nithya Jayakumar, A. K. Maheshwaran, P. S. Arvind, G. Vijayaragavan","doi":"10.1109/ICNWC57852.2023.10127551","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127551","url":null,"abstract":"Employee attrition, also referred to as the loss of personnel over time in a business, occurs for a variety of inescapable reasons. The attrition percentage in 2022 will be 20.3%, according to the latest statistics from India. Employee attrition is a significant problem that can cause severe losses to organizations. In recent years, machine learning has emerged as a powerful tool to address this challenge by predicting employees who may leave the organization. However, the accurate prediction of employee attrition faces various challenges, including dealing with imbalanced datasets, identifying the most critical predictors, and selecting the most appropriate machine learning algorithms. In this study, the proposed solution employs a combination of data preprocessing techniques and machine learning algorithms to predict employee attrition. Our solution includes a visual representation of employee attrition, a parser to extract information from resumes, a test to assess the suitability of potential candidates and AI candidate recommendation. Evaluate the proposed solution using the Employee Attrition dataset and achieve promising results. Our solution can serve as a useful tool for HR managers to predict and visualize employee attrition trends and hire the right candidates for upcoming vacancies.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133384991","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127400
K. Jeyavarshini, H. Jeevapriya, B. Kaaviya, Sivamani Kanimozhi, S. Ramesh
Numerous antenna properties are improved by microwave components with frequency-selective architectures, which are becoming more and more common in many techniques. This paper presents a single-resonant antenna with a Y-shaped structure in the patch. The designed antenna illustrates the parameters such as reflection coefficient (S11), gain (dB), and directivity (dBi) for the corresponding frequency. Since the antenna has been verified for impedance matching and has a low return loss, it is ideal for integration with other microwave equipment. A Microstrip antenna’s characteristics of lightweight and low profile have led to its widespread use in applications such as WLAN and 5G mobile communication. This study describes the design of a Y-shaped antenna in the frequency range of 4.2 to 6. 2GHz for 5G applications. A 3-Dimensional Electromagnetic simulation tool utilizing a finite difference time domain is used. The FR-4 epoxy substrate of height 40mm is used, which has a dielectric permittivity of 4.3. A proximity-coupled feed with 50$Omega$ impedance power this antenna.
{"title":"Design Of Compact Y-Shape Antenna For 5g Smartphones","authors":"K. Jeyavarshini, H. Jeevapriya, B. Kaaviya, Sivamani Kanimozhi, S. Ramesh","doi":"10.1109/ICNWC57852.2023.10127400","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127400","url":null,"abstract":"Numerous antenna properties are improved by microwave components with frequency-selective architectures, which are becoming more and more common in many techniques. This paper presents a single-resonant antenna with a Y-shaped structure in the patch. The designed antenna illustrates the parameters such as reflection coefficient (S11), gain (dB), and directivity (dBi) for the corresponding frequency. Since the antenna has been verified for impedance matching and has a low return loss, it is ideal for integration with other microwave equipment. A Microstrip antenna’s characteristics of lightweight and low profile have led to its widespread use in applications such as WLAN and 5G mobile communication. This study describes the design of a Y-shaped antenna in the frequency range of 4.2 to 6. 2GHz for 5G applications. A 3-Dimensional Electromagnetic simulation tool utilizing a finite difference time domain is used. The FR-4 epoxy substrate of height 40mm is used, which has a dielectric permittivity of 4.3. A proximity-coupled feed with 50$Omega$ impedance power this antenna.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681607","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127349
V. Natarajan, M. S. Kumar
Wireless sensor network (WSN) research is now extremely stimulated due to its potential applications in a range of disciplines, including area monitoring, healthcare, environmental observation, and industrial monitoring. The Quality of Service has become one of the main problems in WSN applications due to the increasing demand for WSN. Due to several limitations imposed by the applications using this network, guaranteed QoS in WSN is challenging to establish. Traditional QoS metrics concentrate on network-level metrics including packet reception ratio (PRR), jitter, end-to-end delay, and throughput. A high QoS environment is characterized by low packet delivery latency, high packet reception ratios, and maximum network throughput. The QoS can be assessed at the network or application level. In order to improve QoS in the network, this study focuses on creating and implementing a better path selection approach for WSN routing based on PRR predictions. Regression algorithms are used to forecast the PRR of a specific path, and the path with the best PRR value is selected to improve network quality of service. The strength of the received signal denoted as RSS, link quality indicator, noise floor over the specific multi-hop path, transmission and reception rate in the MAC layer, and routing path length are used to make the forecast. The results of the predictions and the estimated PRR are compared with the actual packet reception ratio collected from various WSN at an industrial environment.
{"title":"Improving QoS in Wireless Sensor Network routing using Machine Learning Techniques","authors":"V. Natarajan, M. S. Kumar","doi":"10.1109/ICNWC57852.2023.10127349","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127349","url":null,"abstract":"Wireless sensor network (WSN) research is now extremely stimulated due to its potential applications in a range of disciplines, including area monitoring, healthcare, environmental observation, and industrial monitoring. The Quality of Service has become one of the main problems in WSN applications due to the increasing demand for WSN. Due to several limitations imposed by the applications using this network, guaranteed QoS in WSN is challenging to establish. Traditional QoS metrics concentrate on network-level metrics including packet reception ratio (PRR), jitter, end-to-end delay, and throughput. A high QoS environment is characterized by low packet delivery latency, high packet reception ratios, and maximum network throughput. The QoS can be assessed at the network or application level. In order to improve QoS in the network, this study focuses on creating and implementing a better path selection approach for WSN routing based on PRR predictions. Regression algorithms are used to forecast the PRR of a specific path, and the path with the best PRR value is selected to improve network quality of service. The strength of the received signal denoted as RSS, link quality indicator, noise floor over the specific multi-hop path, transmission and reception rate in the MAC layer, and routing path length are used to make the forecast. The results of the predictions and the estimated PRR are compared with the actual packet reception ratio collected from various WSN at an industrial environment.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121131606","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127371
M. Hema, Kanaga Suba Raja, K. Valarmathi, D. Hema Ruba, Sv Abishyna, Kiruthiga Manivel
One of the famous examples in the study of route optimization in the field of computers is Travelling Saleman Problem (TSP). Researches throughout the years various algorithms have been developed attempting to solve the TSP yet there is always doubt in producing the best solution. TSP applies in transportation pathways, delivery services, flight routes, travellers and many more which means there is a need for a pre-planned route schedule to ensure an optimized travelling has been performed. The aim of this paper is to solve the optimal path problem in vehicle routing. The goal is to choose the best route that maximises the likelihood of rapidly reaching the target.. In an attempt to include an optimal path, an optimal path set I’d generated. Using the path set the optimal path can be easily found. TSP is formulated using a modified optimization algorithm for handling complicated and vast environmental constraints. TSP generates routes in complicated and vast environmental constraints. The TSP is an effective technique for providing short and safe routes under dynamic restrictions and its efficiency has been experimented.
{"title":"Life Saving Express-Discovery The Shortest Distance In Vehicle Routing","authors":"M. Hema, Kanaga Suba Raja, K. Valarmathi, D. Hema Ruba, Sv Abishyna, Kiruthiga Manivel","doi":"10.1109/ICNWC57852.2023.10127371","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127371","url":null,"abstract":"One of the famous examples in the study of route optimization in the field of computers is Travelling Saleman Problem (TSP). Researches throughout the years various algorithms have been developed attempting to solve the TSP yet there is always doubt in producing the best solution. TSP applies in transportation pathways, delivery services, flight routes, travellers and many more which means there is a need for a pre-planned route schedule to ensure an optimized travelling has been performed. The aim of this paper is to solve the optimal path problem in vehicle routing. The goal is to choose the best route that maximises the likelihood of rapidly reaching the target.. In an attempt to include an optimal path, an optimal path set I’d generated. Using the path set the optimal path can be easily found. TSP is formulated using a modified optimization algorithm for handling complicated and vast environmental constraints. TSP generates routes in complicated and vast environmental constraints. The TSP is an effective technique for providing short and safe routes under dynamic restrictions and its efficiency has been experimented.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117025015","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127363
Munnaza Ramzan, G. M. Rather
The study of a new unknown phenomenon/system begins with an experimental/ observational study. Statistical and regression analysis of the recorded random data is carried out to examine the characteristic features and behavior of the new phenomenon/system. The recorded data and observed statistical features are used to develop a mathematical model which closely represents the system. This helps in duplicating the new systems through simulation studies. To observe the behavior of dependent output response vis-à-vis independent input to the system under observation, curve fitting techniques are used. Most commonly used being least square based linear regression and non-linear regression techniques. These techniques have their own merits and demerits. In this paper a new polynomial based regression technique is presented. The technique performs exceptionally well within the given range of the independent variable and perfectly maps the observed points to the curve. It helps in predicting the values of the dependent variable with good accuracy in close proximity of the considered independent variable range.
{"title":"A Polynomial Curve Mapping Technique for Random Data","authors":"Munnaza Ramzan, G. M. Rather","doi":"10.1109/ICNWC57852.2023.10127363","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127363","url":null,"abstract":"The study of a new unknown phenomenon/system begins with an experimental/ observational study. Statistical and regression analysis of the recorded random data is carried out to examine the characteristic features and behavior of the new phenomenon/system. The recorded data and observed statistical features are used to develop a mathematical model which closely represents the system. This helps in duplicating the new systems through simulation studies. To observe the behavior of dependent output response vis-à-vis independent input to the system under observation, curve fitting techniques are used. Most commonly used being least square based linear regression and non-linear regression techniques. These techniques have their own merits and demerits. In this paper a new polynomial based regression technique is presented. The technique performs exceptionally well within the given range of the independent variable and perfectly maps the observed points to the curve. It helps in predicting the values of the dependent variable with good accuracy in close proximity of the considered independent variable range.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116052686","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 : 2023-04-05DOI: 10.1109/ICNWC57852.2023.10127236
K. Anand, M. Priyadharshini, K. Priyadharshini
This paper proposes the Gzip algorithm for image and document compression and decompression. Gzip is a hybrid algorithm that combines Lz77 and Huffman. In document management and communication systems, picture and document compression and decompression are crucial. Image and document compression technologies are used to lower the amount of data required to represent the file. Image compression has proven to be the most advantageous and practical method in the field of digital image processing. The goal is to reduce the images’ and documents’ redundancy so that data may be stored or sent efficiently. In order to reduce data redundancy and conserve more hardware space and transmission bandwidth, the theory of data compression and decompression is therefore becoming more and more important. Compression is beneficial because it makes use of less expensive resources like hard disc space and transmission bandwidth. When we evaluate the image quality, decompression is beneficial. In the proposed system, there is no reduction in data, but there is a decrease in data size without loss of quality.
{"title":"Compression And Decompression Of Files Without Loss Of Quality","authors":"K. Anand, M. Priyadharshini, K. Priyadharshini","doi":"10.1109/ICNWC57852.2023.10127236","DOIUrl":"https://doi.org/10.1109/ICNWC57852.2023.10127236","url":null,"abstract":"This paper proposes the Gzip algorithm for image and document compression and decompression. Gzip is a hybrid algorithm that combines Lz77 and Huffman. In document management and communication systems, picture and document compression and decompression are crucial. Image and document compression technologies are used to lower the amount of data required to represent the file. Image compression has proven to be the most advantageous and practical method in the field of digital image processing. The goal is to reduce the images’ and documents’ redundancy so that data may be stored or sent efficiently. In order to reduce data redundancy and conserve more hardware space and transmission bandwidth, the theory of data compression and decompression is therefore becoming more and more important. Compression is beneficial because it makes use of less expensive resources like hard disc space and transmission bandwidth. When we evaluate the image quality, decompression is beneficial. In the proposed system, there is no reduction in data, but there is a decrease in data size without loss of quality.","PeriodicalId":197525,"journal":{"name":"2023 International Conference on Networking and Communications (ICNWC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115045290","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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