Pub Date : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00089
Manish Snehi, A. Bhandari
This paper aims at imparting acquaintance to the researchers an insight into the IoT metamorphosis from a security point of view. This paper presents a state-of-the-art apprehension of the IoT botnet landscape with a close analysis of Mirai. We have elucidated the characterization of the IoT-specific network behaviors such as limited endpoints, sleep time between packets, packet size, etc. that have turned out to be of substantial efficacy to contemporary learning algorithms, including neural networks. The learning algorithms have been reliable to be efficient enough for distributed denial of service (DDoS) attacks detection. We have evaluated the existing learning models and have proposed an efficient IoT-DDoS defense solution. Finally, we have concluded the research with prospective extensions.
{"title":"Apprehending Mirai Botnet Philosophy and Smart Learning Models for IoT-DDoS Detection","authors":"Manish Snehi, A. Bhandari","doi":"10.1109/INDIACom51348.2021.00089","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00089","url":null,"abstract":"This paper aims at imparting acquaintance to the researchers an insight into the IoT metamorphosis from a security point of view. This paper presents a state-of-the-art apprehension of the IoT botnet landscape with a close analysis of Mirai. We have elucidated the characterization of the IoT-specific network behaviors such as limited endpoints, sleep time between packets, packet size, etc. that have turned out to be of substantial efficacy to contemporary learning algorithms, including neural networks. The learning algorithms have been reliable to be efficient enough for distributed denial of service (DDoS) attacks detection. We have evaluated the existing learning models and have proposed an efficient IoT-DDoS defense solution. Finally, we have concluded the research with prospective extensions.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128185265","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00077
D. Piromalis, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, D. Tsaopoulos, P. Zikos, Nikos Tsotsolas, S. Pizanias, Marios Kounelis, Angelos Hliaoutakis, Eleni Koutsouraki, D. Kolovos, G. Giakas, E. Symeonaki, M. Papoutsidakis
Parkinson's disease is a progressive neurodegenerative disorder correlating with dysfunction or deprivation of brains dopaminergic neurons, lack of dopamine, and the formation of abnormal protein particles. There are several clinical tests for detection of Parkinson's disease, but nowadays a demand is rising for an objective assessment of symptoms and health-related outcomes. The rapid development of sensor-based technological devices permits conducting measurements without bias that they are able to be used in scientific research and clinical practice. This paper provides a technical overview of the available commercial wearable systems for monitoring and supporting Parkinson's disease management, taking into account their validity and reliability. The understanding of the current state-of-the-art could help patients and clinicians significantly improve Parkinson's disease management by minimizing health care costs and increasing patient's quality of life.
{"title":"Commercially Available Sensor-based Monitoring and Support Systems in Parkinson's Disease: An Overview","authors":"D. Piromalis, Christos Kokkotis, Themistoklis Tsatalas, George Bellis, D. Tsaopoulos, P. Zikos, Nikos Tsotsolas, S. Pizanias, Marios Kounelis, Angelos Hliaoutakis, Eleni Koutsouraki, D. Kolovos, G. Giakas, E. Symeonaki, M. Papoutsidakis","doi":"10.1109/INDIACom51348.2021.00077","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00077","url":null,"abstract":"Parkinson's disease is a progressive neurodegenerative disorder correlating with dysfunction or deprivation of brains dopaminergic neurons, lack of dopamine, and the formation of abnormal protein particles. There are several clinical tests for detection of Parkinson's disease, but nowadays a demand is rising for an objective assessment of symptoms and health-related outcomes. The rapid development of sensor-based technological devices permits conducting measurements without bias that they are able to be used in scientific research and clinical practice. This paper provides a technical overview of the available commercial wearable systems for monitoring and supporting Parkinson's disease management, taking into account their validity and reliability. The understanding of the current state-of-the-art could help patients and clinicians significantly improve Parkinson's disease management by minimizing health care costs and increasing patient's quality of life.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130045005","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00008
Rabindra Kumar Barik, S. Patra, Rasmita Patro, S. Mohanty, A. A. Hamad
With the speedy expansion of Internet of Spatial Things, the enormous volume of geospatial big data is produced by the IoT devices. It gives rise to the new challenges for real time geospatial data processing and storing of reliable data in cloud system. The traditional geospatial cloud computing system is not efficient enough to process large volumetric of concurrent geospatial data. Consequently, fog assisted cloud computing environment has come into picture for achieving secure geospatial big data deduplication scheme. In this paper, we introduce a novel scheme GeoBD2 which defines the geo-deduplication structure to build an efficient geospatial bigdata deduplication scheme on fog assisted cloud computing framework. It also regulates which fog node needs to be traversed to investigate duplicate geospatial data rather than to traverse all the fog nodes. This can substantially enhance the efficiency of geospatial big data deduplication in fog assisted cloud environment. It also executes the performance analysis of the proposed scheme. By the experimental results, it is found that the proposed scheme has minimum overhead cost than the existing big data deduplication scheme.
{"title":"GeoBD2: Geospatial Big Data Deduplication Scheme in Fog Assisted Cloud Computing Environment","authors":"Rabindra Kumar Barik, S. Patra, Rasmita Patro, S. Mohanty, A. A. Hamad","doi":"10.1109/INDIACom51348.2021.00008","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00008","url":null,"abstract":"With the speedy expansion of Internet of Spatial Things, the enormous volume of geospatial big data is produced by the IoT devices. It gives rise to the new challenges for real time geospatial data processing and storing of reliable data in cloud system. The traditional geospatial cloud computing system is not efficient enough to process large volumetric of concurrent geospatial data. Consequently, fog assisted cloud computing environment has come into picture for achieving secure geospatial big data deduplication scheme. In this paper, we introduce a novel scheme GeoBD2 which defines the geo-deduplication structure to build an efficient geospatial bigdata deduplication scheme on fog assisted cloud computing framework. It also regulates which fog node needs to be traversed to investigate duplicate geospatial data rather than to traverse all the fog nodes. This can substantially enhance the efficiency of geospatial big data deduplication in fog assisted cloud environment. It also executes the performance analysis of the proposed scheme. By the experimental results, it is found that the proposed scheme has minimum overhead cost than the existing big data deduplication scheme.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130513658","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00064
Yazed Alsaawy, Adnan Ahmed Abi Sen, A. Alkhodre, N. Bahbouh, Nuha Abdulrazak Baghanim, Hajar Barrak Alharbi
The 9thand 20th centuries have given rise to great technologies and tools which are making our lives comfortable. In the field of technology and communications, we have billions of electronic services and applications. These technological developments and the unprecedented increase technology, applications and services combined, with the expansion and proliferation of the internet, have greatly increased the frequency of data transfer over the internet. Sharing data over the internet has become a de facto standard of data transfer. This has led to an increase in the vulnerability of data being hacked during its transfer from one port to another. On the other hand, the capabilities of malicious parties in breaking the methods of protection and disclosing confidential information has also advanced and increased tremendously. Thus, organizations and individuals are looking for ways to protect their data from being hacked while it is shared with third parties. Steganography is one of the most effective ways to protect information as it uses encryption algorithms and hides information in a way that prevents attention being drawn from hackers. This research presents a new algorithm in the field of Steganography to increase the level of protection. Compared with the earlier methods, it has a double effect of the Steganography process. We call it ‘Multi-Layered Steganography Algorithm’ (MLSA). We also provide the results of the implementation and testing of the MLSA, which indicate the effectiveness of MLSA according to the protection level and resistant to revealing attacks. But, in contrast, MLSA is suitable only for small sized data sets, and does not have adequate immunity to jamming or compressing data, which we shall study in future.
{"title":"Double Steganography - New Algorithm for More Security","authors":"Yazed Alsaawy, Adnan Ahmed Abi Sen, A. Alkhodre, N. Bahbouh, Nuha Abdulrazak Baghanim, Hajar Barrak Alharbi","doi":"10.1109/INDIACom51348.2021.00064","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00064","url":null,"abstract":"The 9thand 20th centuries have given rise to great technologies and tools which are making our lives comfortable. In the field of technology and communications, we have billions of electronic services and applications. These technological developments and the unprecedented increase technology, applications and services combined, with the expansion and proliferation of the internet, have greatly increased the frequency of data transfer over the internet. Sharing data over the internet has become a de facto standard of data transfer. This has led to an increase in the vulnerability of data being hacked during its transfer from one port to another. On the other hand, the capabilities of malicious parties in breaking the methods of protection and disclosing confidential information has also advanced and increased tremendously. Thus, organizations and individuals are looking for ways to protect their data from being hacked while it is shared with third parties. Steganography is one of the most effective ways to protect information as it uses encryption algorithms and hides information in a way that prevents attention being drawn from hackers. This research presents a new algorithm in the field of Steganography to increase the level of protection. Compared with the earlier methods, it has a double effect of the Steganography process. We call it ‘Multi-Layered Steganography Algorithm’ (MLSA). We also provide the results of the implementation and testing of the MLSA, which indicate the effectiveness of MLSA according to the protection level and resistant to revealing attacks. But, in contrast, MLSA is suitable only for small sized data sets, and does not have adequate immunity to jamming or compressing data, which we shall study in future.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132386382","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}
Depression and mental illness are becoming an indispensable concern, primarily among the youth. According to doctors, about 80 to 90 percent of people with depression eventually respond well to treatment. The close correspondence between social media platforms and their users helps in getting insight into the users' personal life on many levels. This project aims to analyze the tweets for self-assessed depressive features, which can make it possible for individuals, parents, caregivers, and medical professionals to combat this disorder. The project helps to identify the linguistic features of the tweets and the behavioral pattern of the Twitter users who post them, which could demonstrate symptoms of depression. This can be considered as an enhancement in the health care industry providing aid in the early detection and treatment of depression. Our proposed model works by synchronizing different machine learning algorithms to work as an ensemble model for higher efficiency and accuracy.
{"title":"An Ensemble Learning Approach for the Detection of Depression and Mental Illness over Twitter Data","authors":"Ananya Prakash, Kanika Agarwal, Shashank Shekhar, Tarun Mutreja, Partha Sarathi Chakraborty","doi":"10.1109/INDIACom51348.2021.00100","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00100","url":null,"abstract":"Depression and mental illness are becoming an indispensable concern, primarily among the youth. According to doctors, about 80 to 90 percent of people with depression eventually respond well to treatment. The close correspondence between social media platforms and their users helps in getting insight into the users' personal life on many levels. This project aims to analyze the tweets for self-assessed depressive features, which can make it possible for individuals, parents, caregivers, and medical professionals to combat this disorder. The project helps to identify the linguistic features of the tweets and the behavioral pattern of the Twitter users who post them, which could demonstrate symptoms of depression. This can be considered as an enhancement in the health care industry providing aid in the early detection and treatment of depression. Our proposed model works by synchronizing different machine learning algorithms to work as an ensemble model for higher efficiency and accuracy.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132059771","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00147
Nilay Ganatra
Medical imaging is a widely accepted technique for the early detection and diagnosis of disease within digital health. It includes different techniques such as Magnetic resonance imaging (MRI), X-ray, positron emission tomography (PET) scan. Human experts mostly perform the analysis of these images. However, recent advancement in the field of computer-assisted interventions shows the promising results for medical image analysis. With the availability of enormous data, sophisticated algorithms, and high computation power, deep neural networks are highly effective for image analysis and interpretation tasks. Medical image analysis can be performed using the object detection method, where a convolutional neural network (CNN) eliminates the need for manual feature extraction. Object detection using CNN able to extract features directly from images and provides good accuracy. This paper exhibits a detailed survey on applications of different object detection methods available for medical image analysis. This paper discusses the different techniques, state-of-the-art datasets, tools, techniques available, and performance metrics. It also presents the work carried out by various researchers for applying object detection methods for medical image analysis.
{"title":"A Comprehensive Study of Applying Object Detection Methods for Medical Image Analysis","authors":"Nilay Ganatra","doi":"10.1109/INDIACom51348.2021.00147","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00147","url":null,"abstract":"Medical imaging is a widely accepted technique for the early detection and diagnosis of disease within digital health. It includes different techniques such as Magnetic resonance imaging (MRI), X-ray, positron emission tomography (PET) scan. Human experts mostly perform the analysis of these images. However, recent advancement in the field of computer-assisted interventions shows the promising results for medical image analysis. With the availability of enormous data, sophisticated algorithms, and high computation power, deep neural networks are highly effective for image analysis and interpretation tasks. Medical image analysis can be performed using the object detection method, where a convolutional neural network (CNN) eliminates the need for manual feature extraction. Object detection using CNN able to extract features directly from images and provides good accuracy. This paper exhibits a detailed survey on applications of different object detection methods available for medical image analysis. This paper discusses the different techniques, state-of-the-art datasets, tools, techniques available, and performance metrics. It also presents the work carried out by various researchers for applying object detection methods for medical image analysis.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126012155","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00141
A. Patidar, U. Suman
A mobile application (i.e., mobile app) is a small software program, which is mainly developed for mobile phones. Mobile apps have some peculiar characteristics, which are concerned with particular aspects in the form of requirements, pertaining to hardware, software and network connectivity. Designing an appropriate mobile app according to users' perspective depends on the mobile app characteristics. In this paper, we present a review of various available mobile app characteristics concerning to present mobile apps. It is observed that the user experience is one of the most exciting and evolving feature that attracts the majority of mobile app users. In order to deal with user experience, we have further explored and found some essential factors that affect it. Furthermore, mobile app paradigms play an important role for mobile app development. Mobile app characteristics help to select a suitable paradigm for mobile apps development. We have analyzed that hybrid mobile app paradigm fits in most of the mobile app development situations.
{"title":"Towards Analyzing Mobile App Characteristics for Mobile Software Development","authors":"A. Patidar, U. Suman","doi":"10.1109/INDIACom51348.2021.00141","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00141","url":null,"abstract":"A mobile application (i.e., mobile app) is a small software program, which is mainly developed for mobile phones. Mobile apps have some peculiar characteristics, which are concerned with particular aspects in the form of requirements, pertaining to hardware, software and network connectivity. Designing an appropriate mobile app according to users' perspective depends on the mobile app characteristics. In this paper, we present a review of various available mobile app characteristics concerning to present mobile apps. It is observed that the user experience is one of the most exciting and evolving feature that attracts the majority of mobile app users. In order to deal with user experience, we have further explored and found some essential factors that affect it. Furthermore, mobile app paradigms play an important role for mobile app development. Mobile app characteristics help to select a suitable paradigm for mobile apps development. We have analyzed that hybrid mobile app paradigm fits in most of the mobile app development situations.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131546462","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00005
Swapna Subudhiray, H. Palo, N. Das, S. Mohanty
This paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of successful capabilities separated from the Local Binary Pattern (LBP) and Histograms of Oriented Gradients (HOG) for the said task. An examination of the strategies has been made dependent on the normal acknowledgment precision of the classifiers utilizing the calculation unpredictability as a compromise. The component extraction methods have been approved for their discriminative force under various preparations for testing information division proportions, Kappa Coefficient, and order time. The LBP has outperformed the HOG include extraction strategy with a normal precision of 79.6% yet remains computationally costly. On the contrary, the HOG method has furnished a lower characterization time with a normal precision of 59.3 % as uncovered from our outcomes.
{"title":"Comparative Analysis of Histograms of Oriented Gradients and Local Binary Pattern Coefficients for Facial Emotion Recognition","authors":"Swapna Subudhiray, H. Palo, N. Das, S. Mohanty","doi":"10.1109/INDIACom51348.2021.00005","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00005","url":null,"abstract":"This paper examines the human expressive states dependent on facial pictures utilizing a few viable component extraction methods. It reproduces the K-Nearest Neighbor (k-NN) classifier to approve the adequacy of successful capabilities separated from the Local Binary Pattern (LBP) and Histograms of Oriented Gradients (HOG) for the said task. An examination of the strategies has been made dependent on the normal acknowledgment precision of the classifiers utilizing the calculation unpredictability as a compromise. The component extraction methods have been approved for their discriminative force under various preparations for testing information division proportions, Kappa Coefficient, and order time. The LBP has outperformed the HOG include extraction strategy with a normal precision of 79.6% yet remains computationally costly. On the contrary, the HOG method has furnished a lower characterization time with a normal precision of 59.3 % as uncovered from our outcomes.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657437","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00116
G. Gupta, D. Sharma
Depression is a group of mental disorders associated with certain factors which can affect the mood, feelings, negativity, losing interest, and sadness in human participants. To maintain the quality of life, people tend to experience fewer mental health issues. Today social media is a major part of our daily life and these social media sites offer an important platform to share their emotions, feelings in day-to-day routine and life events. In recent years, automatic depression detection on social media-related studies has improved. The objective of this paper is to identify the different machine learning algorithm methods, techniques, and approaches used by various studies related to depression detection on social media platforms by conducting a comprehensive review. Various studies of from year 2013 to 2020 are reviewed to explore the research gaps and future directions.
{"title":"Depression Detection on Social Media with the Aid of Machine Learning Platform: A Comprehensive Survey","authors":"G. Gupta, D. Sharma","doi":"10.1109/INDIACom51348.2021.00116","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00116","url":null,"abstract":"Depression is a group of mental disorders associated with certain factors which can affect the mood, feelings, negativity, losing interest, and sadness in human participants. To maintain the quality of life, people tend to experience fewer mental health issues. Today social media is a major part of our daily life and these social media sites offer an important platform to share their emotions, feelings in day-to-day routine and life events. In recent years, automatic depression detection on social media-related studies has improved. The objective of this paper is to identify the different machine learning algorithm methods, techniques, and approaches used by various studies related to depression detection on social media platforms by conducting a comprehensive review. Various studies of from year 2013 to 2020 are reviewed to explore the research gaps and future directions.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133315015","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 : 2021-03-17DOI: 10.1109/INDIACom51348.2021.00108
Anand Madasamy
Wireless Sensor Networks (WSNs) are spread broadly due to their commonsense use in various applications and zones; this prompted pervasiveness remote sensor networks all over the place. Vitality utilization is considered as the greatest test to decide the wSNs lifetime, because of the restricted force source in the batteries that are coordinated into these sensor hubs. This paper proposes steering convention dependent on DFS calculation. Reenactment results show that the proposed convention is effective as far as decreasing vitality utilization and increment the wSNs life expectancy and accomplishes preferred execution over notable conventions as far as transmission postponement, throughput, and parcel conveyance proportion. In future, update with another artificial intelligence convention will be done.
{"title":"Depth First Search Approach with Multi-Variable Heuristic Function for Enhancing Routing Protocol in 802.11 Sensor Network","authors":"Anand Madasamy","doi":"10.1109/INDIACom51348.2021.00108","DOIUrl":"https://doi.org/10.1109/INDIACom51348.2021.00108","url":null,"abstract":"Wireless Sensor Networks (WSNs) are spread broadly due to their commonsense use in various applications and zones; this prompted pervasiveness remote sensor networks all over the place. Vitality utilization is considered as the greatest test to decide the wSNs lifetime, because of the restricted force source in the batteries that are coordinated into these sensor hubs. This paper proposes steering convention dependent on DFS calculation. Reenactment results show that the proposed convention is effective as far as decreasing vitality utilization and increment the wSNs life expectancy and accomplishes preferred execution over notable conventions as far as transmission postponement, throughput, and parcel conveyance proportion. In future, update with another artificial intelligence convention will be done.","PeriodicalId":415594,"journal":{"name":"2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133427991","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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