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Machine Learning-Based Electricity Load Forecast for the Agriculture Sector 基于机器学习的农业用电负荷预测
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315735
Megha Sharma, Namita Mittal, Anukram Mishra, Arun Gupta
{"title":"Machine Learning-Based Electricity Load Forecast for the Agriculture Sector","authors":"Megha Sharma, Namita Mittal, Anukram Mishra, Arun Gupta","doi":"10.4018/ijsi.315735","DOIUrl":"https://doi.org/10.4018/ijsi.315735","url":null,"abstract":"A large section of the population has a source of income from the agriculture sector, but their share in the Indian GDP is low. Thus, there is a need to forecast energy to improve and increase productivity. The main sources of energy in agriculture are electricity, coal, and diesel. Among them, electricity plays an important role in land irrigation. Power forecasting is also essential for demand response management. Thus, any process that dissolves future consumption is favorable. This article presents a time series-based technique for forecasting medium-term load in agriculture. The aim is to find the peak periods of power consumption by months and seasons using statistical and machine learning-based techniques. The result shows that SARIMA has lower RMSE and exponential smoothing has lower RMSPE error than random forest and LSTM, which makes the statistical approach more efficient than intelligent approach for historical datasets. The season-wise peak demand occurs during the Rabi season. Finally, five-year ahead load in the agriculture sector was determined using the best models.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115232817","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}
引用次数: 5
Preferential Selection of Software Quality Models Based on a Multi-Criteria Decision-Making Approach 基于多准则决策方法的软件质量模型优选
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315739
Ankita Verma, Anushka Agarwal, Manisha Rathore, Sneha Bisht, Deepti Singh
{"title":"Preferential Selection of Software Quality Models Based on a Multi-Criteria Decision-Making Approach","authors":"Ankita Verma, Anushka Agarwal, Manisha Rathore, Sneha Bisht, Deepti Singh","doi":"10.4018/ijsi.315739","DOIUrl":"https://doi.org/10.4018/ijsi.315739","url":null,"abstract":"Software engineering mainly aims to produce software of good quality that is delivered on time and on budget. Software quality becomes an important concern for quantifying the performance of software attributes. The seminal objective of the work is to choose the appropriate software quality model according to the client's needs where the client can give more importance to specific criteria compared to others as per his/her application's requirements. The proposed approach will help to decide the best alternative suitable for the application. The work is based on selecting the most suitable software quality model taking all the parameters into consideration while making the decision using multi-criteria decision-making techniques.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117082653","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}
引用次数: 0
A Systematic Review on the Detection and Classification of Plant Diseases Using Machine Learning 基于机器学习的植物病害检测与分类研究综述
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315657
Deepkiran Munjal, Laxman Singh, Mrinal Pandey, S. Lakra
{"title":"A Systematic Review on the Detection and Classification of Plant Diseases Using Machine Learning","authors":"Deepkiran Munjal, Laxman Singh, Mrinal Pandey, S. Lakra","doi":"10.4018/ijsi.315657","DOIUrl":"https://doi.org/10.4018/ijsi.315657","url":null,"abstract":"The occurrence of disease in plants might affect the crop production at a large scale, resulting into decline of the economic growth rate of the country. The disease in plants can be detected and treated at an early stage. Machine learning (ML), deep learning (DL), and computer vision-based techniques could play a pivotal role in detecting and classifying the diseases at an early stage. These approaches have even surpassed the human performance, as well as image processing based traditional approaches in the analysis and classification of plant diseases. Over the years, numerous authors have applied various image processing ML and DL techniques for the diagnosis of different ailments in plants that gives great hope to the farmers and landlords to cure the disease at an early stage. In this study, the authors addressed and evaluated the various currently existing state of art methods and techniques based on machine and deep learning. Besides, the authors have also focused on various limitations and challenges of these approaches that can explore greater possibly of these methods about their usability for disease detection in plants.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130878203","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}
引用次数: 4
A Method for Feature Subset Selection in Software Product Lines 一种软件产品线特征子集选择方法
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315654
Nahid Hajizadeh, Peyman Jahanbazi, R. Akbari
{"title":"A Method for Feature Subset Selection in Software Product Lines","authors":"Nahid Hajizadeh, Peyman Jahanbazi, R. Akbari","doi":"10.4018/ijsi.315654","DOIUrl":"https://doi.org/10.4018/ijsi.315654","url":null,"abstract":"Software product line (SPL) represents methods, tools, and techniques for creating a group of related software systems. Each product is a combination of multiple features. So, the task of production can be mapped to a feature subset selection problem, which is an NP-hard problem. This issue is very significant when the number of features in a software product line is huge. This chapter is aimed to address the feature subset selection in software product lines. Furthermore, the authors aim at studying the performance of a proposed multi-objective method in solving this NP-hard problem. Here, a multi-objective method (MOBAFS) is presented for feature selection in SPLs. The MOBAFS is a an optimization algorithm, which is inspired by the foraging behavior of honeybees. This technique is evaluated on five large-scale real-world software product lines in the range of 1,244 to 6,888 features. The proposed method is compared with the SATIBEA. According to the results of three solution quality indicators and two diversity metrics, the proposed method, in most cases, surpasses the other algorithm.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"218 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133748751","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}
引用次数: 0
A Novel Approach to Parkinson's Disease Progression Evaluation Using Convolutional Neural Networks 基于卷积神经网络的帕金森病进展评估新方法
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315655
M. Zineddine
{"title":"A Novel Approach to Parkinson's Disease Progression Evaluation Using Convolutional Neural Networks","authors":"M. Zineddine","doi":"10.4018/ijsi.315655","DOIUrl":"https://doi.org/10.4018/ijsi.315655","url":null,"abstract":"Parkinson's disease (PD) is a devastating disorder with serious impacts on the health and quality of life for a wide group of patients. While the early diagnosis of PD is a critical step in managing its symptoms, measuring its progression would be the cornerstone for the development of treatment protocols suitable for each patient. This paper proposes a novel approach to digital PPMI measures and its combination with spirals drawings to increase the accuracy rate of a neural network to the maximum possible. The results show a well performing CNN model with an accuracy of 1(100%). Thus, the end-users of the proposed approach could be more confident when evaluating the progression of PD. The trained, validated, and tested model was able to classify the PD's progression as High, Medium, or Low, with high sureness.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127721757","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}
引用次数: 1
Smart Contract-Based Secure Decentralized Smart Healthcare System 基于智能合约的安全去中心化智能医疗系统
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315742
A. Raj, S. Prakash
{"title":"Smart Contract-Based Secure Decentralized Smart Healthcare System","authors":"A. Raj, S. Prakash","doi":"10.4018/ijsi.315742","DOIUrl":"https://doi.org/10.4018/ijsi.315742","url":null,"abstract":"Social distancing has been imposed to prevent substantial transmission of the COVID-19 outbreak, which is presently a global public health issue. Medical healthcare providers rely on telemedicine to monitor their patients, particularly those with chronic conditions. However, telemedicine faces many implementation-related risks, including data breaches, access restrictions within the medical community, inaccurate diagnosis, fraud, etc. The authors propose a transparent, tamper-proof, distributed, decentralized smart healthcare system (DSHS) that uses blockchain-based smart contracts. The authors use an immutable modified Merkel tree structure to hold the transaction for viewing contracts on a public blockchain, updating patient health records (PHR), and exchanging PHR to all entities. It is verified by a performance evaluation based on the Ethereum platform. The simulation results show that the proposed system outperforms existing approaches by enhancing transparency, boosting efficiency, and reducing average latency in the system. The proposed system improves the functionality of the SHS environment.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018904","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}
引用次数: 4
Optimal Placement of Multiple DG Units With Energy Storage in Radial Distribution System by Hybrid Techniques 基于混合技术的径向配电系统多机组储能优化配置
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315736
P. Munisekhar, G. Jayakrishna, N. Visali
{"title":"Optimal Placement of Multiple DG Units With Energy Storage in Radial Distribution System by Hybrid Techniques","authors":"P. Munisekhar, G. Jayakrishna, N. Visali","doi":"10.4018/ijsi.315736","DOIUrl":"https://doi.org/10.4018/ijsi.315736","url":null,"abstract":"In recent years, distributed generations (DGs) are extremely fast in detecting their location, which helps to satisfy the ever-increasing power demands. The placement of energy storage systems (ESSs) could be a substantial opportunity to enhance the presentation of radial distribution system (RDS). The major part of DG units in RDS deals with the detection of ideal placement and size of the DGs, which efficiently balance the power loss and voltage stability. The ideal location and size of ESSs are examined in standard IEEE-33 and 69 bus systems, which is important to reduce power losses. Nowadays, several algorithms or techniques are modified for the development of hybrid algorithms to improve the quality of DG allocation. In this research, a hybrid shuffled frog leap algorithm (SFLA) with ant lion optimizer (SFLA-ALO) is proposed for the optimal placement and size of the DG and ESS in the RDS to reduce power losses and maintain the stability of voltage. The performance of the proposed SFLA-ALO technique is compared with the implemented BPSO-SFLA technique.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124055976","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}
引用次数: 0
Fuzzy Set-Based Reliability Estimation 基于模糊集的可靠性估计
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315733
Sampa ChauPattnaik, M. Ray, M. M. Nayak
{"title":"Fuzzy Set-Based Reliability Estimation","authors":"Sampa ChauPattnaik, M. Ray, M. M. Nayak","doi":"10.4018/ijsi.315733","DOIUrl":"https://doi.org/10.4018/ijsi.315733","url":null,"abstract":"The rapid advancement of computer technology motivates software developers to use commercial off-the-shelf software components for system growth. For particular architectural elements (for instance, components), the reliability criteria associated with testing-based conventional procedures are unknown. In the traditional reliability estimation, the probabilistic method is applied. The source data problem, which depends on a number of factors that may or may not correspond to the real working conditions of the system, is this technique's major shortcoming. The component-based software reliability estimation is based on a number of parameters, including the individual component reliability, transition probability, failure rate, etc. Fuzzy logic converts fuzzy data into useful information, making it easier to develop creative solutions for vague and uncertain concepts based on various factors that influence reliability. To assess the reliability of component-based systems, the authors provide a fuzzy logic technique, which has the ability to improve the question of uncertainty.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507068","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}
引用次数: 0
Collaborative Filtering Recommender System for Timely Arrival Problem in Road Transport Networks Using Viterbi and the Hidden Markov Algorithms 基于Viterbi和隐马尔可夫算法的道路交通网络准时到达协同过滤推荐系统
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315660
O. A. Ofem, M. Agana, Elemue Oromena Felix
{"title":"Collaborative Filtering Recommender System for Timely Arrival Problem in Road Transport Networks Using Viterbi and the Hidden Markov Algorithms","authors":"O. A. Ofem, M. Agana, Elemue Oromena Felix","doi":"10.4018/ijsi.315660","DOIUrl":"https://doi.org/10.4018/ijsi.315660","url":null,"abstract":"In this study, a timely arrival recommender system (TARS) using Viterbi and hidden Markov Model (HMM) was developed. Ratings from current road users were used as inputs and trained to provide recommendations to prospective road users on the best routes to follow to get to their destinations from any source in time. The system was deployed on Android devices and iPhones with Google map. Real time data on current road conditions were collected from twenty-one (21) bolt drivers in Calabar Metropolis traversing various routes from Unical to Watt Market. The system could calculate arrival time in km/h, generate nearest nodes on each route, detect routes with free or congested traffic flow, and then recommend the best route in real time to users for timely arrival. The application, if adopted, can aid road users to save time, cost, and reduce stress on both humans and the vehicles used.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130756676","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}
引用次数: 0
Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm 基于内容的图像检索，基于混合Densenet121-Bilstm和Harris Hawks优化算法
Int. J. Softw. Innov. Pub Date : 2023-01-01 DOI: 10.4018/ijsi.315661
K. Sanjeevaiah, T. S. Reddy, S. Karthik, Mahesh Kumar, D. Vivek
{"title":"Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm","authors":"K. Sanjeevaiah, T. S. Reddy, S. Karthik, Mahesh Kumar, D. Vivek","doi":"10.4018/ijsi.315661","DOIUrl":"https://doi.org/10.4018/ijsi.315661","url":null,"abstract":"In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.","PeriodicalId":396598,"journal":{"name":"Int. J. Softw. Innov.","volume":"41 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114110098","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}
引用次数: 3
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