SpeechDocs is a web application that aims to solve the difficulties faced by users with unsupportable conditions such as Arthritis in the hand, Parkinson's, Carpal Tunnel Syndrome, or Essential tremors, who have trouble using traditional keyboard and mouse-based software. With its fully-featured voice-controlled document editing system, SpeechDocs enables users to perform any operation such as creating, editing, opening, writing, or replacing words in a document, simply by using their voice. It uses speech-to-text converter and natural language processing technology to allow users to manage documents efficiently. The system also includes features such as facial recognition authentication, document history, and a favorites tab for easy access to frequently used documents. Although some challenges exist, such as accurately capturing speech in noisy environments, SpeechDocs has the potential to greatly improve the lives of users who have disabilities or other conditions that limit their ability to use traditional word processing software.
{"title":"SpeechDocs - A voice activated document editing software","authors":"Pandey Aakash, Pandey Hrithik, Prajapati Rahul, Janrao Prachi","doi":"10.26634/jcom.11.1.19413","DOIUrl":"https://doi.org/10.26634/jcom.11.1.19413","url":null,"abstract":"SpeechDocs is a web application that aims to solve the difficulties faced by users with unsupportable conditions such as Arthritis in the hand, Parkinson's, Carpal Tunnel Syndrome, or Essential tremors, who have trouble using traditional keyboard and mouse-based software. With its fully-featured voice-controlled document editing system, SpeechDocs enables users to perform any operation such as creating, editing, opening, writing, or replacing words in a document, simply by using their voice. It uses speech-to-text converter and natural language processing technology to allow users to manage documents efficiently. The system also includes features such as facial recognition authentication, document history, and a favorites tab for easy access to frequently used documents. Although some challenges exist, such as accurately capturing speech in noisy environments, SpeechDocs has the potential to greatly improve the lives of users who have disabilities or other conditions that limit their ability to use traditional word processing software.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"264 2 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":"133944849","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}
As countries of the world are facing unimaginable challenges from the novel coronavirus COVID-19 pandemic, the strains on governments are extreme;and these have greatly impacted people everywhere In response to the pandemic, captains of industries have implemented pandemic/communicable disease response plans, assessed and/or initiated business continuity plans, and widespread telework operations Nigeria's government has also implemented lots of guidelines and these include, social distancing, shutting or locking down of restaurants, bars, movie theaters, gyms, and even worship centres As a nation, Nigeria has been affected in many areas, for example, the educational system has been truncated, activities of manufacturing industries halted, and technologically, the nation is not moving forward As a result, this paper aims to examine the role of Information Technology (IT) in the COVID-19 pandemic recovery strategy in Nigeria The paper carried out a thorough analysis of the COVID-19 pandemic, its economic impact;and how government and private sectors can help in restoring the nation's economy by maximizing the use of InformationTechnology (IT) in Nigeria Studies were carried out on the pandemic spread with emphasis on attributes, such as, confirmed, recovered, and death cases Data was extracted from UNDP and WHO recorded data of December 12, 2020 The study shows that the growth of IT has increased steadily in the past nine (9) months with an appreciable increase in its teledensity, info density, and compudensity
{"title":"COVID-19 PANDEMIC RECOVERY STRATEGY: THE ROLE OF\u0000INFORMATION TECHNOLOGY (IT) IN NIGERIA","authors":"Olufemi Akinyede Raphael","doi":"10.26634/JCOM.8.2.17758","DOIUrl":"https://doi.org/10.26634/JCOM.8.2.17758","url":null,"abstract":"As countries of the world are facing unimaginable challenges from the novel coronavirus COVID-19 pandemic, the strains on governments are extreme;and these have greatly impacted people everywhere In response to the pandemic, captains of industries have implemented pandemic/communicable disease response plans, assessed and/or initiated business continuity plans, and widespread telework operations Nigeria's government has also implemented lots of guidelines and these include, social distancing, shutting or locking down of restaurants, bars, movie theaters, gyms, and even worship centres As a nation, Nigeria has been affected in many areas, for example, the educational system has been truncated, activities of manufacturing industries halted, and technologically, the nation is not moving forward As a result, this paper aims to examine the role of Information Technology (IT) in the COVID-19 pandemic recovery strategy in Nigeria The paper carried out a thorough analysis of the COVID-19 pandemic, its economic impact;and how government and private sectors can help in restoring the nation's economy by maximizing the use of InformationTechnology (IT) in Nigeria Studies were carried out on the pandemic spread with emphasis on attributes, such as, confirmed, recovered, and death cases Data was extracted from UNDP and WHO recorded data of December 12, 2020 The study shows that the growth of IT has increased steadily in the past nine (9) months with an appreciable increase in its teledensity, info density, and compudensity","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"31 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":"122014510","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":"DETERMINING THE EFFICIENCY OF ATTENDANCE CHECKING\u0000USING QR CODE SCANNER APPLICATION","authors":"Shiny S. Jeba","doi":"10.26634/jcom.9.3.16007","DOIUrl":"https://doi.org/10.26634/jcom.9.3.16007","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"3 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":"129353663","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":"SECURITY IMPROVEMENT FOR REALISTIC DATA\u0000 USING INTERNATIONAL DATA ENCRYPTION\u0000 CRYPTOGRAPHIC ALGORITHM","authors":"Malav Anuraj","doi":"10.26634/jcom.7.2.15465","DOIUrl":"https://doi.org/10.26634/jcom.7.2.15465","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"105 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":"128600456","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.11.1.19374
A. Bashir, Ullah Burhan, Sardar Fouzia, Junaid Hazrat, Zaman Khan Gul
This paper presents a methodology for the early detection and diagnosis of breast cancer using the Wisconsin dataset. The methodology involves four main steps, including data collection, preprocessing, feature selection, and classification. Fine needle aspiration technique is used to extract the ultrasound image features of breast cancer, and preprocessing is performed to eliminate outliers, null values, and noise. Redundant parameters are removed during the feature selection process to improve accuracy. Six machine learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, and Gaussian Naive Bayes, are employed for the classification of the breast cancer dataset. Support Vector Machine and K-Nearest Neighbor achieved the highest accuracy, with Logistic Regression, Gaussian Naive Bayes, Random Forest, and Decision Tree having lower accuracy scores. The proposed methodology could aid in the timely detection and diagnosis of breast cancer, and help doctors in selecting the optimal clinical treatment plan for their patients. Further work will be carried out to investigate the effectiveness of additional preprocessing algorithms in improving the classification accuracy of the breast cancer dataset.
{"title":"A supervised classification phenotyping approach using machine learning for patients diagnosed with primary breast cancer","authors":"A. Bashir, Ullah Burhan, Sardar Fouzia, Junaid Hazrat, Zaman Khan Gul","doi":"10.26634/jcom.11.1.19374","DOIUrl":"https://doi.org/10.26634/jcom.11.1.19374","url":null,"abstract":"This paper presents a methodology for the early detection and diagnosis of breast cancer using the Wisconsin dataset. The methodology involves four main steps, including data collection, preprocessing, feature selection, and classification. Fine needle aspiration technique is used to extract the ultrasound image features of breast cancer, and preprocessing is performed to eliminate outliers, null values, and noise. Redundant parameters are removed during the feature selection process to improve accuracy. Six machine learning algorithms, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, Decision Tree, and Gaussian Naive Bayes, are employed for the classification of the breast cancer dataset. Support Vector Machine and K-Nearest Neighbor achieved the highest accuracy, with Logistic Regression, Gaussian Naive Bayes, Random Forest, and Decision Tree having lower accuracy scores. The proposed methodology could aid in the timely detection and diagnosis of breast cancer, and help doctors in selecting the optimal clinical treatment plan for their patients. Further work will be carried out to investigate the effectiveness of additional preprocessing algorithms in improving the classification accuracy of the breast cancer dataset.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"32 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120874893","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.10.3.19161
Jain Roli, Gupta Puja, Bansal Sahil, S. Vichitra
In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.
{"title":"An AI-Based Online Exam Proctoring Framework","authors":"Jain Roli, Gupta Puja, Bansal Sahil, S. Vichitra","doi":"10.26634/jcom.10.3.19161","DOIUrl":"https://doi.org/10.26634/jcom.10.3.19161","url":null,"abstract":"In recent years, there has been an increase in online education. However, an adequate technique was not developed for online examinations. Some educational facilities have compiled assignments that students may copy and paste from the web, while others use remote proctoring, where a human proctor monitors what the students are doing online. Cheating in online tests remains common despite all the growth that has taken place in this field. This paper proposes an Artificial Intelligence (AI)-based system that can assist in the automatic detection of fraud in online tests. This method is both efficient and trustworthy. This system outperformed previous systems in experiments.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"1 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":"124351745","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.10.1.18876
R. Thirumalaiselvi, D. Gomathi
Nutritional options and early life consuming behavior has marked as foremost dietary status of girl little ones. The goal of the look at turned into to discover consuming behavior of 10 – 20 age of girl children the usage of lady toddler consuming conduct Questionnaire. Matched questionnaires describing pronounced snack intake, consuming motivations and wholesome eating regimen were finished by way of one hundred girl infant. By way of being a high-quality position model for your developing girl infant, you may set an instance of wholesome dietary conduct. This examines targeted on standards of ingesting behavior of woman baby and to explain and analyze the content of food intake information's through Google paperwork. Series and evaluation of information have been furnished thru analyzing the content of paperwork approximately eating behavior, particularly fresh fruit, seafood, milk, vegetables, and proteins & calories. Using Google paperwork, a complete of one hundred precise forms had been retrieved and analyzed by making use of data mining strategies and cluster analysis. The findings of this look at found out that girl infant of their answers tended to explain eating conditions, and food habits, as well as to express emotions towards food items. This looks at additionally provides idea towards the food regimen and health-associated behavior trade for preventing the healthy consuming behavior for girl infant in puberty time in addition to enhancing the health circumstance. The outcomes of this look at may be used as a recommendation in the direction of the measurement of ingesting behavior coping with health of the woman child the use of Two step cluster, k means cluster and hierarchical clustering evaluation in IBM SPSS (Statistical Package for the Social Sciences) Statistics 21.0.
{"title":"Healthy eating behaviors of girl children using clustering techniques: A questionnaire study","authors":"R. Thirumalaiselvi, D. Gomathi","doi":"10.26634/jcom.10.1.18876","DOIUrl":"https://doi.org/10.26634/jcom.10.1.18876","url":null,"abstract":"Nutritional options and early life consuming behavior has marked as foremost dietary status of girl little ones. The goal of the look at turned into to discover consuming behavior of 10 – 20 age of girl children the usage of lady toddler consuming conduct Questionnaire. Matched questionnaires describing pronounced snack intake, consuming motivations and wholesome eating regimen were finished by way of one hundred girl infant. By way of being a high-quality position model for your developing girl infant, you may set an instance of wholesome dietary conduct. This examines targeted on standards of ingesting behavior of woman baby and to explain and analyze the content of food intake information's through Google paperwork. Series and evaluation of information have been furnished thru analyzing the content of paperwork approximately eating behavior, particularly fresh fruit, seafood, milk, vegetables, and proteins & calories. Using Google paperwork, a complete of one hundred precise forms had been retrieved and analyzed by making use of data mining strategies and cluster analysis. The findings of this look at found out that girl infant of their answers tended to explain eating conditions, and food habits, as well as to express emotions towards food items. This looks at additionally provides idea towards the food regimen and health-associated behavior trade for preventing the healthy consuming behavior for girl infant in puberty time in addition to enhancing the health circumstance. The outcomes of this look at may be used as a recommendation in the direction of the measurement of ingesting behavior coping with health of the woman child the use of Two step cluster, k means cluster and hierarchical clustering evaluation in IBM SPSS (Statistical Package for the Social Sciences) Statistics 21.0.","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"540 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":"124536213","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. MOHAMMED ISMAIL, R. K. Vijayraajesh, V. Dhanakoti, J. Chandru
{"title":"DETECTION OF PHISHING WEBSITE USING MACHINE LEARNING ALGORITHMS AND DEPLOYMENT USING FAST API","authors":"P. MOHAMMED ISMAIL, R. K. Vijayraajesh, V. Dhanakoti, J. Chandru","doi":"10.26634/jcom.8.4.18143","DOIUrl":"https://doi.org/10.26634/jcom.8.4.18143","url":null,"abstract":"","PeriodicalId":130578,"journal":{"name":"i-manager's Journal on Computer Science","volume":"47 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":"127537382","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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