Pub Date : 2023-10-14DOI: 10.13052/jmm1550-4646.1969
Md. Asim Iqbal, K. Devarajan, Syed Musthak Ahmed
Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Hybrid Deep Q Neural Networks (HDQNN). Initially, the features from the speech signals are extracted by using Krill herd optimization (KHO) approach, which extracts the detailed disease specific features. Further, the optimal features are extracted by using chaotic opposition krill herd optimization (COKHO) algorithm. Then, HDQNN is used to classify the type of asthma such as normal, and stridor classes. Further, COKHO is also used to optimize the losses generated in the HDQNN model. The simulation results shows that the proposed HDQNN method resulted in superior performance as compared to state of art approaches.
{"title":"HDQNN-Net: An Optimal Asthma Disease Detection Technique for Voice Signal Using Hybrid Deep Q-Neural Networks","authors":"Md. Asim Iqbal, K. Devarajan, Syed Musthak Ahmed","doi":"10.13052/jmm1550-4646.1969","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1969","url":null,"abstract":"Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Hybrid Deep Q Neural Networks (HDQNN). Initially, the features from the speech signals are extracted by using Krill herd optimization (KHO) approach, which extracts the detailed disease specific features. Further, the optimal features are extracted by using chaotic opposition krill herd optimization (COKHO) algorithm. Then, HDQNN is used to classify the type of asthma such as normal, and stridor classes. Further, COKHO is also used to optimize the losses generated in the HDQNN model. The simulation results shows that the proposed HDQNN method resulted in superior performance as compared to state of art approaches.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766946","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1966
Jatuporn Supramongkonset, Sathaporn Promwong
Radio frequency identification (RFID) is wireless multimedia applications for bookshelves experimental system to replace the barcodes media technology. The RFID propagation channel characteristics and environment effect should be known. In this study to evaluate the RFID propagation loss with wooden and metal bookshelves based on data of measurement. Experimental study evaluation of RFID multimedia system with bookshelves is using vector network analyzer (VNA) and the microstrip patch antennas of transmitter (Tx) and receiver (Rx) antennas at a frequency range from 2.4 GHz to 2.5 GHz. The results of experiment are considering the path loss, received signal strength (RSS), and comparison the path loss differences with cumulative distribution function (CDF) to evaluated, respectively. In this research work are necessary for RFID antenna design and evaluate the RFID multimedia systems.
{"title":"Quantitative Experimental Evaluation of RFID Propagation Loss with Wooden and Metal Bookshelves","authors":"Jatuporn Supramongkonset, Sathaporn Promwong","doi":"10.13052/jmm1550-4646.1966","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1966","url":null,"abstract":"Radio frequency identification (RFID) is wireless multimedia applications for bookshelves experimental system to replace the barcodes media technology. The RFID propagation channel characteristics and environment effect should be known. In this study to evaluate the RFID propagation loss with wooden and metal bookshelves based on data of measurement. Experimental study evaluation of RFID multimedia system with bookshelves is using vector network analyzer (VNA) and the microstrip patch antennas of transmitter (Tx) and receiver (Rx) antennas at a frequency range from 2.4 GHz to 2.5 GHz. The results of experiment are considering the path loss, received signal strength (RSS), and comparison the path loss differences with cumulative distribution function (CDF) to evaluated, respectively. In this research work are necessary for RFID antenna design and evaluate the RFID multimedia systems.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766947","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}
Chiang Mai is a well-known province in Thailand, attracting a large number of tourists every year. Nonetheless, due to the large number of tourists visiting Chiang Mai, they may confront a variety of issues, including urban issues, which may create annoyance and create unpleasant experiences. In this study, we proposed the design and development of gamified mobile application which employed the concept of participatory crowdsourcing and the system architecture for management of tourism participatory of urban problem for smart city of government officer in order to motivate tourists to collaborate in providing their comments and feedback regarding urban problems encountered in Chiang Mai city. To evaluate the approach, we developed and launched our data-gathering application for tourists. The results reveal five significant types of urban problems encountered by 352 tourists in Chiang Mai. Moreover, we found that the most urban problems in Chiang Mai by tourists are communication (31.34%), vendors (23.88%) and transportation (15.67%). Furthermore, the study revealed that gamification can motivate tourists’ attention of the participatory sensing to providing feedback and complaints to the officer government to solve the problems.
{"title":"Design of Gamified Crowdsourcing for Tourism Participatory of Urban Problem the Management of Smart City Initiatives","authors":"Suepphong Chernbumroong, Phimphakan Thongthip, Orasa Sirasakamol, Kanjana Jansukpum, Phichete Julrode, Kitti Puritat","doi":"10.13052/jmm1550-4646.1961","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1961","url":null,"abstract":"Chiang Mai is a well-known province in Thailand, attracting a large number of tourists every year. Nonetheless, due to the large number of tourists visiting Chiang Mai, they may confront a variety of issues, including urban issues, which may create annoyance and create unpleasant experiences. In this study, we proposed the design and development of gamified mobile application which employed the concept of participatory crowdsourcing and the system architecture for management of tourism participatory of urban problem for smart city of government officer in order to motivate tourists to collaborate in providing their comments and feedback regarding urban problems encountered in Chiang Mai city. To evaluate the approach, we developed and launched our data-gathering application for tourists. The results reveal five significant types of urban problems encountered by 352 tourists in Chiang Mai. Moreover, we found that the most urban problems in Chiang Mai by tourists are communication (31.34%), vendors (23.88%) and transportation (15.67%). Furthermore, the study revealed that gamification can motivate tourists’ attention of the participatory sensing to providing feedback and complaints to the officer government to solve the problems.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767279","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.19610
CH. Srilakshmi, E. Laxmi Lydia, N. Ramakrishnaiah
Research of skin cancer images through visual survey and manual evaluation to investigate skin threatening development has always been abnormal. This manual evaluation of skin injuries to recognize melanoma is monotonous as well as somewhat long. With movement in advancement and fast development in computational resources, different AI techniques and significant learning methods have emerged for assessment of clinical pictures most especially the skin lesion images. In late years, AI arising as an innovation equipped for tackling issues connected with horticulture, medical services, business, and soon. To diminish the endanger to human existence we can embrace AI calculations in the medical care area and can foresee the deadliest skin illnesses like dangerous melanoma in beginning phases. The point of the research is to give bits of knowledge about various classifications of skin lesions and strategies executed to arrange and foresee skin diseases and the job of dermatologists while fostering the models, at last gives a general rundown of existing work.
{"title":"Deep Learning Technique Based State-Of-The-Art in Skin Cancer Detection: A Review","authors":"CH. Srilakshmi, E. Laxmi Lydia, N. Ramakrishnaiah","doi":"10.13052/jmm1550-4646.19610","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.19610","url":null,"abstract":"Research of skin cancer images through visual survey and manual evaluation to investigate skin threatening development has always been abnormal. This manual evaluation of skin injuries to recognize melanoma is monotonous as well as somewhat long. With movement in advancement and fast development in computational resources, different AI techniques and significant learning methods have emerged for assessment of clinical pictures most especially the skin lesion images. In late years, AI arising as an innovation equipped for tackling issues connected with horticulture, medical services, business, and soon. To diminish the endanger to human existence we can embrace AI calculations in the medical care area and can foresee the deadliest skin illnesses like dangerous melanoma in beginning phases. The point of the research is to give bits of knowledge about various classifications of skin lesions and strategies executed to arrange and foresee skin diseases and the job of dermatologists while fostering the models, at last gives a general rundown of existing work.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135766950","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1964
Krishnakanth Medichalam, V. Vijayarajan, V. Vinoth Kumar, I. Manimozhi Iyer, Yaswanth Kumar Vanukuri, V. B. Surya Prasath, B. Swapna
Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications.
{"title":"Trustworthy Artificial Intelligence and Automatic Morse Code Based Communication Recognition with Eye Tracking","authors":"Krishnakanth Medichalam, V. Vijayarajan, V. Vinoth Kumar, I. Manimozhi Iyer, Yaswanth Kumar Vanukuri, V. B. Surya Prasath, B. Swapna","doi":"10.13052/jmm1550-4646.1964","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1964","url":null,"abstract":"Morse code is one of the oldest communication techniques and used in telecommunication systems. Morse code can be transmitted as a visual signal by using reflections or with the help of flashlights, but it can also be used as a non-detectable form of communication by using the tapping of fingers or even blinking of eyes. In this paper, we develop a computer vision based approach that automatically characterizes the characters conveyed wherein a person can communicate to system or another person through Morse code with eye gestures. We can decode this visual eye tracking based language with the help of our automatic computer vision driven method. Our approach uses a normal webcam to detect the gestures made by the eyes and are interpreted as dots and dashes. These dots and dashes are used to represent the Morse code-based words. Image processing techniques-based blink and pupil detectors are employed. Blink detector helps us to detect a blink and the time that took for each blink. A blink that takes 2 to 4 seconds is acknowledged as a dot whereas a blink that takes more than 4 seconds is represented as a dash. The pupil detector helps us to detect the movement of the pupils, and if pupils move towards right with respect to a person then it is acknowledged as next letter and if the pupils are moved towards left with respect to a person then it is acknowledged as next word. In this way, we decode the Morse code which will be communicated using eyes and establish a non-detectable communication between a person and an automatic system. Our experimental results on an unconstrained visual scene with preliminary greeting words indicate the promise of an automatic eye tracking based system with success rate of 98.25% that can be of use in non-verbal communications.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767291","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}
The short-range wireless multimedia is consider used a ultra wideband (UWB) technology for human body mobile and multimedia applications shows promise for wireless multi-media systems. Based on IEEE 802.15.6, wireless body area networks (BAN) require understanding the human body’s effects on channel characteristics. This paper presents how to evaluation of human body ulra-wideband (HB-UWB) transmission with line-of-sight and non-line-of-sight scenario. Our research aims to enhance HB-UWB channel propagation on the body media by employing with CLEAN algorithm to eliminate noise. This research leverage findings from previous studies to facilitate performance comparison. Furthermore, for analyze system performance using the CLEAN algorithm at different body positions. The measurement setup covers band the FCC regulated from 3.0 GHz to 11 GHz. It includes the tested with wideband antenna and vector network analyzer (VNA). HB-UWB characteristics are shows in the path loss and power delay profile are discussed as relevant parameters. This research very useful for design and evaluation of human body mobile network and wireless multimedia systems.
{"title":"Measurement and Investigation of HB-UWB Transmission Link for BAN System","authors":"Chanidaphar Sanguanpuak, Sathaporn Promwong, Chanin Bunlaksananusorn","doi":"10.13052/jmm1550-4646.1965","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1965","url":null,"abstract":"The short-range wireless multimedia is consider used a ultra wideband (UWB) technology for human body mobile and multimedia applications shows promise for wireless multi-media systems. Based on IEEE 802.15.6, wireless body area networks (BAN) require understanding the human body’s effects on channel characteristics. This paper presents how to evaluation of human body ulra-wideband (HB-UWB) transmission with line-of-sight and non-line-of-sight scenario. Our research aims to enhance HB-UWB channel propagation on the body media by employing with CLEAN algorithm to eliminate noise. This research leverage findings from previous studies to facilitate performance comparison. Furthermore, for analyze system performance using the CLEAN algorithm at different body positions. The measurement setup covers band the FCC regulated from 3.0 GHz to 11 GHz. It includes the tested with wideband antenna and vector network analyzer (VNA). HB-UWB characteristics are shows in the path loss and power delay profile are discussed as relevant parameters. This research very useful for design and evaluation of human body mobile network and wireless multimedia systems.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804213","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1967
Md. Asim Iqbal, K. Devarajan, Syed Musthak Ahmed
Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Multi-Feature Extraction, Selection with Hybrid Classifiers (MFESHC). Initially, speech signals are preprocessed by using Maximum likelihood estimation based spread spectrum analysis (MLE-SSA) method. Then, Improved prefix Beam Search (IPBS) based natural language processing (NLP) method is used to extract and select the best features from the preprocessed speech signals. Then, hybrid dense convolutional neural networks (HDCNN) are used to classify the type of asthma such as normal, stridor, wheezes and rattle classes. Further, Modified Crow Search (MCS) is used to optimize the losses generated in the HDCNN model. The simulation results shows that the proposed MFESHC method resulted in superior performance as compared to state of art approaches because the MCS effectively reduced the losses in the model.
{"title":"Real Time Asthma Disease Detection and Identification Technique from Speech Signals Using Hybrid Dense Convolutional Neural Network","authors":"Md. Asim Iqbal, K. Devarajan, Syed Musthak Ahmed","doi":"10.13052/jmm1550-4646.1967","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1967","url":null,"abstract":"Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Multi-Feature Extraction, Selection with Hybrid Classifiers (MFESHC). Initially, speech signals are preprocessed by using Maximum likelihood estimation based spread spectrum analysis (MLE-SSA) method. Then, Improved prefix Beam Search (IPBS) based natural language processing (NLP) method is used to extract and select the best features from the preprocessed speech signals. Then, hybrid dense convolutional neural networks (HDCNN) are used to classify the type of asthma such as normal, stridor, wheezes and rattle classes. Further, Modified Crow Search (MCS) is used to optimize the losses generated in the HDCNN model. The simulation results shows that the proposed MFESHC method resulted in superior performance as compared to state of art approaches because the MCS effectively reduced the losses in the model.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767286","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1962
Anindita Khade, Amarsinh V. Vidhate, Deepali Vidhate
Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.
{"title":"Design of an Optimized Self-Acclimation Graded Boolean PSO with Back Propagation Model and Cuckoo Search Heuristics for Automatic Prediction of Chronic Kidney Disease","authors":"Anindita Khade, Amarsinh V. Vidhate, Deepali Vidhate","doi":"10.13052/jmm1550-4646.1962","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1962","url":null,"abstract":"Objectives: A kind of Artificial Neural Network (ANN) known as a Back Propagation Neural Network (BPNN) has been extensively applied in a variety of sectors, including medical diagnosis, optical character recognition, stock market forecasting, and others. Many studies have employed BPNN to create decision-support tools for doctors to use while making clinical diagnoses. Chronic Kidney Disease (CKD) is one such kind of disease which has been receiving due importance from the past decades due to lack of symptoms in its early stages. The goal of this work is to demonstrate the performance of Artificial Intelligent (AI) algorithms in the early detection of CKD. Method: We received 800 patients’ real-time data from DY Patil Hospitals for this investigation. Self-Acclimation Graded Boolean PSO (SAG-BPSO), a modified version of Particle Swarm Optimization (PSO), has been proposed and used in this study to accomplish feature selection. Cuckoo Search Algorithm (CSA) has been used to optimise the weights and biases of the BPNN. Finally, this hybrid model is combined with BPNN for final predictions. Finally, a comparison is made between few state of art algorithms and the proposed approach. Results: The accuracy noted on applying BPNN on the dataset was approximately 91.45%. The combined model of BPNN+SAGBPSO provided an accuracy of about 92.25%. The accuracy achieved for the hybrid model of BPNN+SAGBPSO+CSA was approximately near to 98.07%. Conclusions: This research used SAGBPSO for feature selection and CSA for finalizing the weights and biases of BPNN. The research implemented BPNN, BPNN+SAGBPSO and BPNN+SAGBPSO+CSA on our real time dataset. The proposed hybrid model BPNN+SAGBPSO+CSA outperformed all the state of art deep learning algorithms in terms of performance metrics.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767284","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1963
M. Rajesh, K. Vengatesan, R. Sitharthan, Shanmuga Sundar Dhanabalan, Mahendra Bhatu Gawali
The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches.
{"title":"Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia","authors":"M. Rajesh, K. Vengatesan, R. Sitharthan, Shanmuga Sundar Dhanabalan, Mahendra Bhatu Gawali","doi":"10.13052/jmm1550-4646.1963","DOIUrl":"https://doi.org/10.13052/jmm1550-4646.1963","url":null,"abstract":"The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches.","PeriodicalId":38898,"journal":{"name":"Journal of Mobile Multimedia","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135767287","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 : 2023-10-14DOI: 10.13052/jmm1550-4646.1968
Chunmei Wang
Acute and critical care nursing is a subject with high requirements covering common emergency and critical care theories, common first aid, and clinical practice in various clinical departments. It involves theoretical knowledge of various specialties and a number of nursing operations. With the continuous updating of equipment and knowledge related to acute and critical care nursing, this discipline has also developed rapidly. However, due to the increasing shortage of medical teaching resources, it cannot effectively meet the teaching needs. In order to alleviate this clinical teaching pressure, this paper introduces the wise information technology of med, Sim Man 3G, a high-simulation simulator, into the teaching of acute and critical care nursing. Using Sim Man 3G can not only adjust the difficulty of treatment according to the needs of teaching, but also break through the clinical limitations, so the operation is very practical. The study found that the use of Sim Man 3G high simulation manikin in acute and critical care nursing could effectively improve the accuracy of case analysis and practical operation, which also improved the efficiency of teaching. In acute and critical care nursing, the highest teaching efficiency of Sim Man 3G was 96%, and the average teaching efficiency was 93.2%. The highest general teaching efficiency was 92%, and the average teaching efficiency was 88.6%. The teaching efficiency of the experimental group was 4.6% higher than that of the common group on average, and the overall quality was also improved. This showed that it is relatively successful to use Sim Man 3G high-fidelity simulator in acute and critical care nursing.
急危护理是一门要求较高的学科，涉及急危护理的常用理论、常用急救以及临床各科室的临床实践。它涉及各个专业的理论知识和一些护理操作。随着急危护理相关设备和知识的不断更新，该学科也得到了迅速发展。然而，由于医学教学资源日益短缺，不能有效满足教学需求。为了缓解临床教学压力，本文将医学智慧信息技术Sim Man 3G高仿真模拟器引入急危护理教学中。使用Sim Man 3G不仅可以根据教学需要调整治疗难度，而且可以突破临床局限性，操作非常实用。研究发现，在急危重症护理中使用Sim Man 3G高仿真人体模型，可以有效提高病例分析和实际操作的准确性，提高教学效率。在急危护理中，Sim Man 3G的最高教学效率为96%，平均教学效率为93.2%。综合教学效率最高为92%，平均教学效率为88.6%。实验组的教学效率比普通组平均高出4.6%，整体教学质量也有所提高。这说明Sim Man 3G高保真模拟器在急危重症护理中应用是比较成功的。
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