Pub Date : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.24
Deepika E, Pavan Kumar Reddy B
This paper centers on the use of AI calculations for anticipating spinal anomalies. Various AI approaches specifically Decision tree, Naïve Bayes, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) strategies are considered for the conclusion of spinal anomaly. The presentation of arrangement of strange and typical spinal patients is assessed as far as various variables including preparing and testing exactness, accuracy and review. Be that as it may, SVM is the most appealing as it's anything but a higher exactness esteem. Henceforth, SVM is appropriate for the order of spinal patients when applied on the most five significant highlights of spinal examples.
{"title":"Diagnosis spinal abnormalities utilizing machine learning algorithms","authors":"Deepika E, Pavan Kumar Reddy B","doi":"10.33545/27076636.2021.v2.i2a.24","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.24","url":null,"abstract":"This paper centers on the use of AI calculations for anticipating spinal anomalies. Various AI approaches specifically Decision tree, Naïve Bayes, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) strategies are considered for the conclusion of spinal anomaly. The presentation of arrangement of strange and typical spinal patients is assessed as far as various variables including preparing and testing exactness, accuracy and review. Be that as it may, SVM is the most appealing as it's anything but a higher exactness esteem. Henceforth, SVM is appropriate for the order of spinal patients when applied on the most five significant highlights of spinal examples.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127669056","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.31
Harathi, Boyella Mala Konda Reddy
By and large, distant detecting photos are taken in dim conditions like mist, snow, slim cloudiness, mud, etc, bringing about picture contrast misfortune. The Dark Channel Prior (DCP) was utilized to eliminate the dimness impact on far off detecting pictures in this examination. DE inception is conceivable in this model for both characteristic and distant detecting pictures. The initial phase in improving satellite picture properties is to decide if the picture is a characteristic picture or a far off detecting picture, and afterward recuperate it to take out dimness. Emphasis proceeds with the utilization of airlight values, trailed by the utilization of DCP to limit dust, lastly the fog is eliminated utilizing the Iterative dehazing measure for distant detecting picture (IDERS) model. The aftereffect of the Low light picture upgrade (LIME) measure is a fog free picture with expanded lucidity.
{"title":"Super pixel segmentation and classification of SAR images for brightness enhancement","authors":"Harathi, Boyella Mala Konda Reddy","doi":"10.33545/27076636.2021.v2.i2a.31","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.31","url":null,"abstract":"By and large, distant detecting photos are taken in dim conditions like mist, snow, slim cloudiness, mud, etc, bringing about picture contrast misfortune. The Dark Channel Prior (DCP) was utilized to eliminate the dimness impact on far off detecting pictures in this examination. DE inception is conceivable in this model for both characteristic and distant detecting pictures. The initial phase in improving satellite picture properties is to decide if the picture is a characteristic picture or a far off detecting picture, and afterward recuperate it to take out dimness. Emphasis proceeds with the utilization of airlight values, trailed by the utilization of DCP to limit dust, lastly the fog is eliminated utilizing the Iterative dehazing measure for distant detecting picture (IDERS) model. The aftereffect of the Low light picture upgrade (LIME) measure is a fog free picture with expanded lucidity.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116486155","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.28
Indu, Pavan Kumar Reddy B
Banknotes are financial norms used by any nation to finish cash related activities and are every country asset which every country needs it to be genuine. A couple of heretics present fake notes which look to some degree like exceptional note to make incongruities of the money in the cash related market. It is problematic for individuals to tell authentic and fake banknotes isolated especially because they have a lot of similar features. In this examination, we played out a broad relative investigation of troupe procedures, for example, boosting, packing and stacking for Banknote Authentication. During the last many years, in the space of AI and information mining, the advancement of outfit strategies has acquired a critical consideration from mainstream researchers. AI troupe strategies join different learning calculations to acquire preferable prescient execution over could be gotten from any of the constituent learning calculations alone. Outfit techniques utilize different models to improve execution. Outfit strategies have been utilized in different exploration fields like computational insight, measurements and AI. The consequences of the investigation show that troupe strategies, like packing and boosting, are powerful in further developing the forecast exactness of frail classifiers, and display palatable execution in distinguishing hazard of Banknote Authentication. A greatest increment of 7% exactness for feeble classifiers was accomplished with the assistance of troupe arrangement.
{"title":"An ensemble classification approach for prediction of banknote authentication","authors":"Indu, Pavan Kumar Reddy B","doi":"10.33545/27076636.2021.v2.i2a.28","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.28","url":null,"abstract":"Banknotes are financial norms used by any nation to finish cash related activities and are every country asset which every country needs it to be genuine. A couple of heretics present fake notes which look to some degree like exceptional note to make incongruities of the money in the cash related market. It is problematic for individuals to tell authentic and fake banknotes isolated especially because they have a lot of similar features. In this examination, we played out a broad relative investigation of troupe procedures, for example, boosting, packing and stacking for Banknote Authentication. During the last many years, in the space of AI and information mining, the advancement of outfit strategies has acquired a critical consideration from mainstream researchers. AI troupe strategies join different learning calculations to acquire preferable prescient execution over could be gotten from any of the constituent learning calculations alone. Outfit techniques utilize different models to improve execution. Outfit strategies have been utilized in different exploration fields like computational insight, measurements and AI. The consequences of the investigation show that troupe strategies, like packing and boosting, are powerful in further developing the forecast exactness of frail classifiers, and display palatable execution in distinguishing hazard of Banknote Authentication. A greatest increment of 7% exactness for feeble classifiers was accomplished with the assistance of troupe arrangement.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123442772","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.26
Devi P, Boyella Mala Konda Reddy
The advancement in profound learning estimations for different PC vision issues convinces our report. For picture super-objectives, we propose a novel start to finish profound learning-based system. This design at the same time decides the convolutional highlights of low-goal (LR) and high-goal (HR) picture fixes, just as the non-direct force that maps these LR picture fix convolutional highlights to their relating HR picture fix convolutional highlights. The proposed profound learning-based picture super-objectives design is named coupled profound convolutional auto-encoder (CDCA) in this paper, and it produces cutting edge results. Super-objectives of an uproarious/curved LR picture results in loud/bended HR pictures, as the super-objectives strategy gives rise to spatial relationship in the commotion, and it can't be de-noised viably. Until super-objectives, most uproar flexible picture super-objectives methods do a de-noising gauge. Be that as it may, the de-noising technique brings about the shortfall of some high-repeat information (edges and surface nuances), and the subsequent picture's super-objectives bring about HR pictures without edges and surface information. We're likewise proposing a pristine start to finish profound learning-based design for acquiring upheaval
{"title":"Image super-resolution and noise-resilient super-resolution using end-to-end deep learning","authors":"Devi P, Boyella Mala Konda Reddy","doi":"10.33545/27076636.2021.v2.i2a.26","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.26","url":null,"abstract":"The advancement in profound learning estimations for different PC vision issues convinces our report. For picture super-objectives, we propose a novel start to finish profound learning-based system. This design at the same time decides the convolutional highlights of low-goal (LR) and high-goal (HR) picture fixes, just as the non-direct force that maps these LR picture fix convolutional highlights to their relating HR picture fix convolutional highlights. The proposed profound learning-based picture super-objectives design is named coupled profound convolutional auto-encoder (CDCA) in this paper, and it produces cutting edge results. Super-objectives of an uproarious/curved LR picture results in loud/bended HR pictures, as the super-objectives strategy gives rise to spatial relationship in the commotion, and it can't be de-noised viably. Until super-objectives, most uproar flexible picture super-objectives methods do a de-noising gauge. Be that as it may, the de-noising technique brings about the shortfall of some high-repeat information (edges and surface nuances), and the subsequent picture's super-objectives bring about HR pictures without edges and surface information. We're likewise proposing a pristine start to finish profound learning-based design for acquiring upheaval","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123681407","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.25
D. G, Boyella Mala Konda Reddy
This paper investigations choice tree calculation for Breast disease discovery. The effectiveness of choice tree calculation can be broke down dependent on their precision and the quality choice measure utilized. The paper likewise gives a thought of the trait choice measure utilized by different choice tree calculation utilizes data gain and GINI Index as the quality choice measure. In this paper, the expectation of Decision Tree characterization is evaluated using two property trait choice decision measures for Breast Cancer sickness dataset. Choice tree uses separate and vanquish framework for the fundamental learning technique. From the result examination we can reason that the execution of Decision Tree grouping relies upon the trademark quality choice decision measures. Choice Tree is significant since improvement of decision tree classifiers doesn't need any territory learning. The essential objective is to produce a capable assumption show for Breast Cancer sickness expectation returns with high precision.
{"title":"Performance measure of breast cancer prediction using decision tree approach","authors":"D. G, Boyella Mala Konda Reddy","doi":"10.33545/27076636.2021.v2.i2a.25","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.25","url":null,"abstract":"This paper investigations choice tree calculation for Breast disease discovery. The effectiveness of choice tree calculation can be broke down dependent on their precision and the quality choice measure utilized. The paper likewise gives a thought of the trait choice measure utilized by different choice tree calculation utilizes data gain and GINI Index as the quality choice measure. In this paper, the expectation of Decision Tree characterization is evaluated using two property trait choice decision measures for Breast Cancer sickness dataset. Choice tree uses separate and vanquish framework for the fundamental learning technique. From the result examination we can reason that the execution of Decision Tree grouping relies upon the trademark quality choice decision measures. Choice Tree is significant since improvement of decision tree classifiers doesn't need any territory learning. The essential objective is to produce a capable assumption show for Breast Cancer sickness expectation returns with high precision.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122157833","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.30
Karthik, Pavan Kumar Reddy B
The multiclass classification problem is an important topic in the field of pattern recognition. It involves the task of classifying input instances into one of multiple classes. Since the class overlapping problem exists among multiple classes in most real-world problems, the multiclass classification task is much more complicated and challenging compared to the binary class problem. Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types
{"title":"An experimental approach for prediction of multi-classification using SVM","authors":"Karthik, Pavan Kumar Reddy B","doi":"10.33545/27076636.2021.v2.i2a.30","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.30","url":null,"abstract":"The multiclass classification problem is an important topic in the field of pattern recognition. It involves the task of classifying input instances into one of multiple classes. Since the class overlapping problem exists among multiple classes in most real-world problems, the multiclass classification task is much more complicated and challenging compared to the binary class problem. Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and multi-label classification. Traditional binary and multi-class classifications are subcategories of single-label classification. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128130809","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.27
Geetanjali, Pavan Kumar Reddy B
We present another reversible (lossless) information stowing away (inserting) strategy that takes into consideration careful recuperation of the first host signal after the implanted data is separated. The information installing approach is proposed as a speculation of the notable LSB (least significant cycle) update, which includes extra working focuses the limit contortion bend. Compacting portions of the sign that are helpless against installing spillage and dispersing these packed subtleties as a feature of the inserted payload considers lossless recovery of the first. The pressure effectiveness and in this manner the lossless information implanting capacity of a forecast based restrictive entropy coder that utilizes static segments of the host as side-data improves
{"title":"Steganography algorithm for reversible data hiding using LSB and reversible image transformation","authors":"Geetanjali, Pavan Kumar Reddy B","doi":"10.33545/27076636.2021.v2.i2a.27","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.27","url":null,"abstract":"We present another reversible (lossless) information stowing away (inserting) strategy that takes into consideration careful recuperation of the first host signal after the implanted data is separated. The information installing approach is proposed as a speculation of the notable LSB (least significant cycle) update, which includes extra working focuses the limit contortion bend. Compacting portions of the sign that are helpless against installing spillage and dispersing these packed subtleties as a feature of the inserted payload considers lossless recovery of the first. The pressure effectiveness and in this manner the lossless information implanting capacity of a forecast based restrictive entropy coder that utilizes static segments of the host as side-data improves","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321526","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 : 2021-07-01DOI: 10.33545/27076636.2021.v2.i2a.29
Jyothi, Boyella Mala Konda Reddy
Breast cancer is accounted for to be the most well-known malignancy type among ladies worldwide and it is the second most elevated lady’s casualty rate among all malignant growth types. Precisely anticipating the endurance pace of bosom disease patients is a significant issue for malignancy scientists. Machine Learning (ML) has drawn in much consideration with the expectation that it could give exact outcomes, yet its displaying techniques and forecast execution stay dubious. This paper centres on the use of AI calculations for anticipating Haberman's Breast Cancer Survival analysis. Various AI approaches specifically Decision tree, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN) strategies are considered for the conclusion of Breast Cancer Survival anomaly. The presentation of arrangement of strange and typical Breast Cancer Survival patients is assessed as far as various variables including preparing and testing exactness, accuracy and review. The point of this deliberate survey is to recognize and basically assess current examinations with respect to the use of ML in foreseeing the 5-year endurance pace of bosom malignant growth. Test results on Haberman's Breast Cancer Survival dataset show the predominance of MLP proposed technique by coming to 96.7% as far as precision.
{"title":"Breast cancer diagnosis and survival prediction using ML algorithms","authors":"Jyothi, Boyella Mala Konda Reddy","doi":"10.33545/27076636.2021.v2.i2a.29","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i2a.29","url":null,"abstract":"Breast cancer is accounted for to be the most well-known malignancy type among ladies worldwide and it is the second most elevated lady’s casualty rate among all malignant growth types. Precisely anticipating the endurance pace of bosom disease patients is a significant issue for malignancy scientists. Machine Learning (ML) has drawn in much consideration with the expectation that it could give exact outcomes, yet its displaying techniques and forecast execution stay dubious. This paper centres on the use of AI calculations for anticipating Haberman's Breast Cancer Survival analysis. Various AI approaches specifically Decision tree, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN) strategies are considered for the conclusion of Breast Cancer Survival anomaly. The presentation of arrangement of strange and typical Breast Cancer Survival patients is assessed as far as various variables including preparing and testing exactness, accuracy and review. The point of this deliberate survey is to recognize and basically assess current examinations with respect to the use of ML in foreseeing the 5-year endurance pace of bosom malignant growth. Test results on Haberman's Breast Cancer Survival dataset show the predominance of MLP proposed technique by coming to 96.7% as far as precision.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125322812","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 : 2021-01-01DOI: 10.33545/27076636.2021.v2.i1a.23
Abang Iss, Akhimie Co, Nze On, Amasiatu Is, Ukwueze Ru, Agbatah Ob
This paper presents some solutions to how Nigerian agricultural sector can become great again, recalling the glory days, when the sector was the national economic backbone. The paper explained some of the challenges that agricultural farmers faces in Nigeria and how scientific application of ICT as a means of quick dissemination of farming skills, techniques and proper resource allocation, could aid the nations farming sector to achieve it aim. The objectives of this paper are to determine an alternative means of farming that would enhance the contribution of the agricultural produce in Nigeria and also to encourage the use of modern day technologies by farmers to improve their farming techniques and produce. The concept of the growth pole technology was adopted as a means of enhancing the current farming situation and ICT is our focal tool in driving the growth pole strategy. The discussions in this study are premised on the concept of ICT, Growth Pole strategy, and the importance of agriculture as an alternative source of Income generation. The methodology aimed at achieving some of the following in the discussion that ICT-enabled service, often use multiple technologies to provide information to rural farmers on forecasts so that they can prepare for weather-related events (Balaji and Craufurd, 2011; Gunda et al , 2017). The proliferation of mobile phones across the globe has not impinged agriculture in various ways. Mobiles are being used to help raise farmers’ incomes, making agricultural marke ting more efficient, lowering information costs, reducing transport costs, and providing a platform to deliver services and innovate (Honrao, 2012; Khapayi and Celliers, 2016). Recommendation were made on Knowledge management application, where the study made suggestions to the federal ministry of information and communication technology that they should create a user friendly application that farmers could easily associate themselves with and share knowledge with each other, also the application should create room for researchers to post their own findings to aid farmers with modern ways of farming.
本文提出了一些解决方案，尼日利亚农业部门如何能够再次变得伟大，回忆起辉煌的日子，当该部门是国家经济支柱。这篇论文解释了尼日利亚农民面临的一些挑战，以及如何科学地应用ICT作为一种快速传播农业技能、技术和适当资源分配的手段，可以帮助该国的农业部门实现其目标。本文的目的是确定一种可替代的耕作方式，以提高尼日利亚农产品的贡献，并鼓励农民使用现代技术来改进他们的耕作技术和生产。增长极技术的概念被采纳为改善当前农业状况的一种手段，信息通信技术是我们推动增长极战略的重点工具。本研究的讨论以信息通信技术、增长极战略和农业作为另一种创收来源的重要性为前提。该方法旨在实现以下讨论中的一些目标:ict服务通常使用多种技术向农村农民提供预报信息，使他们能够为与天气有关的事件做好准备(Balaji和crawford, 2011;Gunda et al, 2017)。移动电话在全球的普及并没有以各种方式影响农业。手机被用来帮助农民提高收入，提高农业营销效率，降低信息成本，减少运输成本，并提供一个提供服务和创新的平台(Honrao, 2012;Khapayi and cellers, 2016)。对知识管理应用程序提出了建议，该研究向联邦信息通信技术部建议，他们应该创建一个用户友好的应用程序，使农民能够轻松地联系和分享知识，并且该应用程序应该为研究人员提供自己的发现，以帮助农民采用现代耕作方式。
{"title":"ICT for agricultural management: A tool for regional competitiveness in Nigeria","authors":"Abang Iss, Akhimie Co, Nze On, Amasiatu Is, Ukwueze Ru, Agbatah Ob","doi":"10.33545/27076636.2021.v2.i1a.23","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i1a.23","url":null,"abstract":"This paper presents some solutions to how Nigerian agricultural sector can become great again, recalling the glory days, when the sector was the national economic backbone. The paper explained some of the challenges that agricultural farmers faces in Nigeria and how scientific application of ICT as a means of quick dissemination of farming skills, techniques and proper resource allocation, could aid the nations farming sector to achieve it aim. The objectives of this paper are to determine an alternative means of farming that would enhance the contribution of the agricultural produce in Nigeria and also to encourage the use of modern day technologies by farmers to improve their farming techniques and produce. The concept of the growth pole technology was adopted as a means of enhancing the current farming situation and ICT is our focal tool in driving the growth pole strategy. The discussions in this study are premised on the concept of ICT, Growth Pole strategy, and the importance of agriculture as an alternative source of Income generation. The methodology aimed at achieving some of the following in the discussion that ICT-enabled service, often use multiple technologies to provide information to rural farmers on forecasts so that they can prepare for weather-related events (Balaji and Craufurd, 2011; Gunda et al , 2017). The proliferation of mobile phones across the globe has not impinged agriculture in various ways. Mobiles are being used to help raise farmers’ incomes, making agricultural marke ting more efficient, lowering information costs, reducing transport costs, and providing a platform to deliver services and innovate (Honrao, 2012; Khapayi and Celliers, 2016). Recommendation were made on Knowledge management application, where the study made suggestions to the federal ministry of information and communication technology that they should create a user friendly application that farmers could easily associate themselves with and share knowledge with each other, also the application should create room for researchers to post their own findings to aid farmers with modern ways of farming.","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124930079","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 : 2021-01-01DOI: 10.33545/27076636.2021.v2.i1a.20
Narmanov Ulugbek Abdugapparovich
In the article, the impact of the digital economy on the nature and development of the modern labor market is associated with a pressing problem. The article provides an overview of various methodological approaches to the concept of digital economy.The author's approach to the concept in which the digital economy is studied on the basis of the impact on the labor market is presented. The consequences of the transition of society to the digital economy are comprehensive, as well as the skills that digital economy personnel should possess, the professions that will be needed in the near future are analyzed
{"title":"Role and prospects of the digital economy in the labor market","authors":"Narmanov Ulugbek Abdugapparovich","doi":"10.33545/27076636.2021.v2.i1a.20","DOIUrl":"https://doi.org/10.33545/27076636.2021.v2.i1a.20","url":null,"abstract":"In the article, the impact of the digital economy on the nature and development of the modern labor market is associated with a pressing problem. The article provides an overview of various methodological approaches to the concept of digital economy.The author's approach to the concept in which the digital economy is studied on the basis of the impact on the labor market is presented. The consequences of the transition of society to the digital economy are comprehensive, as well as the skills that digital economy personnel should possess, the professions that will be needed in the near future are analyzed","PeriodicalId":127185,"journal":{"name":"International Journal of Computing, Programming and Database Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114279519","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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