{"title":"INDIAN SIGN LANGUAGE RECOGNITION\u0000 SYSTEM USING OPENPOSE","authors":"M. Pooja, K. Chirag, V. Nikhil, R. T. Hardik","doi":"10.26634/jcom.7.2.15993","DOIUrl":"https://doi.org/10.26634/jcom.7.2.15993","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130257756","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 lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.
{"title":"Secure authentication protocol for IoT applications based on blockchain technology","authors":"Choubey Siddhartha, Choubey Abha, Bajpai Shiwanee, Kumar Bajpai Prageet","doi":"10.26634/jcom.11.1.19380","DOIUrl":"https://doi.org/10.26634/jcom.11.1.19380","url":null,"abstract":"The lack of security in Internet of Things (IoT) infrastructure across different applications has attracted the attention of researchers to work on IoT security issues. The paper presents a scenario where blockchain technology is combined with IoT to provide a decentralized security mechanism. Secure authentication and Key-Agreement technique are proposed for IoT nodes and peers to ensure proper authorization before communication can take place. The proposed protocol uses public key cryptography to generate a shared symmetric key for mutual authentication and two-party conversation. The protocol was tested using Scyther and was found to be robust enough to withstand all known authentication-related attacks, including replay, and typing attacks. Hyperledger, a blockchain technology, was employed to provide a more efficient IoT-enabled infrastructure for the scalability of IoT devices on the network. The proposed technique provides a secure and scalable system for device authentication in a blockchain-enabled IoT environment.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970699","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}
P. Abarna, R. Archana, M. Kamali, K. Selvabhuvaneshwari
{"title":"A HYBRID NETWORK MODEL FOR EFFECTIVE HOUSE PRICE PREDICTION","authors":"P. Abarna, R. Archana, M. Kamali, K. Selvabhuvaneshwari","doi":"10.26634/jcom.8.4.18150","DOIUrl":"https://doi.org/10.26634/jcom.8.4.18150","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128118888","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}
S. Karthikeyan, M. Keerthivasan, A. Lalitha, R. Karan
{"title":"NETWORK INTRUSION DETECTION SYSTEM BASED ON PACKET\u0000FILTERS","authors":"S. Karthikeyan, M. Keerthivasan, A. Lalitha, R. Karan","doi":"10.26634/jcom.9.1.18174","DOIUrl":"https://doi.org/10.26634/jcom.9.1.18174","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131339083","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 is a disease or problem associated with high levels of stress seen in humans. It is uncomfortable in talking to parents, psychologists, and healthcare professionals in general. So a virtual platform is much more suitable for sharing your emotions, for example, a chatbot that provides the user with a comfort zone, acting as a friend or well-wisher. Extracting and identifying emotions from text messages to detect depressive mood is a challenging task because it involves removing natural language ambiguities. Over the past decade, researchers have proposed various state-ofthe- art methods for detecting depressive moods in text. This paper aims to analyze such methods and present a comparison based on detection accuracy. The virtual platform provides an end-user interface for communication. The system understands the meaning and context of a sentence using Natural Language Processing (NLP), word embedding, and machine learning techniques. NLP does the preprocessing and extracts the mental health-related keywords. Word embedding converts the extracted keywords into embedding vectors that can be understood by Machine learning algorithms, it can also analyze and extract users' feelings by examining and calculating levels of depression and classifying the user as depressed or not. This paper showed that the support vector machine is the preferred algorithm over other machine learning algorithms and provides higher accuracy.
{"title":"Implementation of machine learning techniques for depression in text messages: a survey","authors":"Dewangan Divya, Selot Smita, Panicker Sreejit","doi":"10.26634/jcom.9.4.18549","DOIUrl":"https://doi.org/10.26634/jcom.9.4.18549","url":null,"abstract":"Depression is a disease or problem associated with high levels of stress seen in humans. It is uncomfortable in talking to parents, psychologists, and healthcare professionals in general. So a virtual platform is much more suitable for sharing your emotions, for example, a chatbot that provides the user with a comfort zone, acting as a friend or well-wisher. Extracting and identifying emotions from text messages to detect depressive mood is a challenging task because it involves removing natural language ambiguities. Over the past decade, researchers have proposed various state-ofthe- art methods for detecting depressive moods in text. This paper aims to analyze such methods and present a comparison based on detection accuracy. The virtual platform provides an end-user interface for communication. The system understands the meaning and context of a sentence using Natural Language Processing (NLP), word embedding, and machine learning techniques. NLP does the preprocessing and extracts the mental health-related keywords. Word embedding converts the extracted keywords into embedding vectors that can be understood by Machine learning algorithms, it can also analyze and extract users' feelings by examining and calculating levels of depression and classifying the user as depressed or not. This paper showed that the support vector machine is the preferred algorithm over other machine learning algorithms and provides higher accuracy.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123522559","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}
{"title":"NOVEL TECHNIQUE FOR SECURE KEYLESS CAR AUTHENTICATION USING BLOCK-CHAIN FRAMEWORK","authors":"Hussain Rashid, Khan Rabia, K. Rajesh","doi":"10.26634/jcom.8.4.18297","DOIUrl":"https://doi.org/10.26634/jcom.8.4.18297","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114517070","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}
S. Prabakeran, K. Swarnapriya, Agnes Priscilla Remina
{"title":"MULTI-DEFENSE FRAMEWORK FOR MITIGATING\u0000 MAN IN THE CLOUD ATTACK (MITC)","authors":"S. Prabakeran, K. Swarnapriya, Agnes Priscilla Remina","doi":"10.26634/jcom.7.2.15674","DOIUrl":"https://doi.org/10.26634/jcom.7.2.15674","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123843905","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 : 1900-01-01DOI: 10.26634/jcom.10.4.19341
H. Rashid, Khan Rabia, Kumar Tyagi Rajesh
Smartphone sensors produce high-dimensional feature vectors that can be utilized to recognize different human activities. However, the contribution of each vector in the identification process is different, and several strategies have been examined over time to develop a procedure that yields favorable results. This paper presents the latest Machine Learning algorithms proposed for human activity classification, which include data acquisition, data preprocessing, data segmentation, feature selection, and dataset classification into training and testing sets. The solutions are compared and thoroughly analyzed by highlighting the respective advantages and disadvantages. The results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 95%.
{"title":"Machine learning modelling based on smartphone sensor data of human activity recognition","authors":"H. Rashid, Khan Rabia, Kumar Tyagi Rajesh","doi":"10.26634/jcom.10.4.19341","DOIUrl":"https://doi.org/10.26634/jcom.10.4.19341","url":null,"abstract":"Smartphone sensors produce high-dimensional feature vectors that can be utilized to recognize different human activities. However, the contribution of each vector in the identification process is different, and several strategies have been examined over time to develop a procedure that yields favorable results. This paper presents the latest Machine Learning algorithms proposed for human activity classification, which include data acquisition, data preprocessing, data segmentation, feature selection, and dataset classification into training and testing sets. The solutions are compared and thoroughly analyzed by highlighting the respective advantages and disadvantages. The results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 95%.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129742870","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}
{"title":"SMART AGRICULTURE USING ARTIFICIAL INTELLIGENCE: A REVIEW","authors":"Subhalaxmi","doi":"10.26634/jcom.9.2.17741","DOIUrl":"https://doi.org/10.26634/jcom.9.2.17741","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"8 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127912335","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 : 1900-01-01DOI: 10.26634/jcom.10.1.18866
CH. Sekhar, P. Deekshita, Rao M. Srinivasa
An Automated examination system for evaluation and descriptive test is an online based examination system in which exams are conducted online. The primary purpose of this Smart Examination system is to build an automated examination system. There is no need of paper and pen. Here students can write exam using internet or intranet. We will mainly use Machine Learning, Natural language processing (NLP) that generates a descriptive test automatically. The main objectives are analyzing sentence and find the semantic meaning of student answer and compare with actual answer defined by instructor and then assigns final scores. Now compare the automated evaluated score with the manual evaluated score with a specific threshold if threshold is deviated then with use of machine learning algorithm can improve the automated evaluation process. It saves lot of valuable time of teachers, reduce work, and complete the evaluation on time.
{"title":"An automated examination system for evaluation of descriptive test","authors":"CH. Sekhar, P. Deekshita, Rao M. Srinivasa","doi":"10.26634/jcom.10.1.18866","DOIUrl":"https://doi.org/10.26634/jcom.10.1.18866","url":null,"abstract":"An Automated examination system for evaluation and descriptive test is an online based examination system in which exams are conducted online. The primary purpose of this Smart Examination system is to build an automated examination system. There is no need of paper and pen. Here students can write exam using internet or intranet. We will mainly use Machine Learning, Natural language processing (NLP) that generates a descriptive test automatically. The main objectives are analyzing sentence and find the semantic meaning of student answer and compare with actual answer defined by instructor and then assigns final scores. Now compare the automated evaluated score with the manual evaluated score with a specific threshold if threshold is deviated then with use of machine learning algorithm can improve the automated evaluation process. It saves lot of valuable time of teachers, reduce work, and complete the evaluation on time.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127536133","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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