Pub Date : 2023-10-09DOI: 10.1007/s43926-023-00044-3
Soumili Ghosh, Mahendra Kumar Gourisaria, Biswajit Sahoo, Himansu Das
Abstract Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning.
{"title":"A pragmatic ensemble learning approach for rainfall prediction","authors":"Soumili Ghosh, Mahendra Kumar Gourisaria, Biswajit Sahoo, Himansu Das","doi":"10.1007/s43926-023-00044-3","DOIUrl":"https://doi.org/10.1007/s43926-023-00044-3","url":null,"abstract":"Abstract Heavy rainfall and precipitation play a massive role in shaping the socio-agricultural landscape of a country. Being one of the key indicators of climate change, natural disasters, and of the general topology of a region, rainfall prediction is a gift of estimation that can be used for multiple beneficial causes. Machine learning has an impressive repertoire in aiding prediction and estimation of rainfall. This paper aims to find the effect of ensemble learning, a subset of machine learning, on a rainfall prediction dataset, to increase the predictability of the models used. The classification models used in this paper were tested once individually, and then with applied ensemble techniques like bagging and boosting, on a rainfall dataset based in Australia. The objective of this paper is to demonstrate a reduction in bias and variance via ensemble learning techniques while also analyzing the increase or decrease in the aforementioned metrics. The study shows an overall reduction in bias by an average of 6% using boosting, and an average reduction in variance by 13.6%. Model performance was observed to become more generalized by lowering the false negative rate by an average of more than 20%. The techniques explored in this paper can be further utilized to improve model performance even further via hyper-parameter tuning.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094291","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 : 2023-10-09DOI: 10.1007/s43926-023-00041-6
Vishalkumar Langaliya, Jaypalsinh A. Gohil
Abstract Purpose This research introduces a decentralized method for handling real estate transactions through the utilization of private blockchain technology. The authors pinpoint the primary challenges within the prevailing transaction procedures in India and advocate for the integration of blockchain technology as a solution. Ultimately, the study concludes that the proposed system has the potential to optimize transaction processes within Indian government offices, fostering heightened efficiency, transparency, and a reduction in corrupt practices. Methods/design/methodology The current transaction process and the centralized technology are investigated using a physical observation approach at the government office. Following that, numerous parties are questioned to identify the main pain areas in the process. The outcomes of the interviews are used to create a blockchain solution that addresses the identified pain points. Following the design, interviewees are requested to validate the suggested model. Findings Some of the primary pain areas found in the real estate transaction procedure include that it is impossible to avoid single-point failure due to the present centralized transaction process, the possibility of corruption at any point, and the lack of data available at each node. Using blockchain techniques, the suggested decentralized application enhances the way transactions are processed and ensures the quality of data availability, transparency, and the elimination of single points of failure. Practical implications and simulation process A private blockchain application is created to improve the real estate transaction procedure at the Indian government office. One complex front end is created to receive information about the seller, the buyer’s property, and the payment, and a suitable database is employed to hold the sensitive data. Data is moved to the private blockchain for final execution when the smart business logic has been applied to the necessary information. One artificial utility is created that places a heavy load on the proposed system and measures the load trashing to validate it. It generates an enormous amount of sample data to verify the suggested system. Originality/value According to recent research, blockchain technology has the potential to get better efficiency, transparency, security, data accessibility, and thus trust in the transaction process. As a result, the suggested application is beneficial to the future of the Indian real estate transaction process.
{"title":"Innovative and secure decentralized approach to process real estate transactions by utilizing private blockchain","authors":"Vishalkumar Langaliya, Jaypalsinh A. Gohil","doi":"10.1007/s43926-023-00041-6","DOIUrl":"https://doi.org/10.1007/s43926-023-00041-6","url":null,"abstract":"Abstract Purpose This research introduces a decentralized method for handling real estate transactions through the utilization of private blockchain technology. The authors pinpoint the primary challenges within the prevailing transaction procedures in India and advocate for the integration of blockchain technology as a solution. Ultimately, the study concludes that the proposed system has the potential to optimize transaction processes within Indian government offices, fostering heightened efficiency, transparency, and a reduction in corrupt practices. Methods/design/methodology The current transaction process and the centralized technology are investigated using a physical observation approach at the government office. Following that, numerous parties are questioned to identify the main pain areas in the process. The outcomes of the interviews are used to create a blockchain solution that addresses the identified pain points. Following the design, interviewees are requested to validate the suggested model. Findings Some of the primary pain areas found in the real estate transaction procedure include that it is impossible to avoid single-point failure due to the present centralized transaction process, the possibility of corruption at any point, and the lack of data available at each node. Using blockchain techniques, the suggested decentralized application enhances the way transactions are processed and ensures the quality of data availability, transparency, and the elimination of single points of failure. Practical implications and simulation process A private blockchain application is created to improve the real estate transaction procedure at the Indian government office. One complex front end is created to receive information about the seller, the buyer’s property, and the payment, and a suitable database is employed to hold the sensitive data. Data is moved to the private blockchain for final execution when the smart business logic has been applied to the necessary information. One artificial utility is created that places a heavy load on the proposed system and measures the load trashing to validate it. It generates an enormous amount of sample data to verify the suggested system. Originality/value According to recent research, blockchain technology has the potential to get better efficiency, transparency, security, data accessibility, and thus trust in the transaction process. As a result, the suggested application is beneficial to the future of the Indian real estate transaction process.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135092956","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}
Abstract The dimensions of microarray datasets are very large, containing noise and redundancy. The problem with microarray datasets is the presence of more features compared to the number of samples, which adversely affects algorithm performance. In other words, the number of columns exceeds the number of rows. Therefore, to extract precise information from microarray datasets, a robust technique is required. Microarray datasets play a critical role in detecting various diseases, including cancer and tumors. This is where feature selection techniques come into play. In recent times, feature selection (FS) has gained significant importance as a data preparation method, particularly for high-dimensional data. It is preferable to address classification problems with fewer features while maintaining high accuracy, as not all features are necessary to achieve this goal. The primary objective of feature selection is to identify the optimal subset of features. In this context, we will employ the Differential Evolution (DE) algorithm. DE is a population-based stochastic search approach that has found widespread use in various scientific and technical domains to solve optimization problems in continuous spaces. In our approach, we will combine DE with three different classification algorithms: Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). Our analysis will include a comparison of the accuracy achieved by each algorithmic model on each dataset, as well as the fitness error for each model. The results indicate that when feature selection was used the results were better compared to the results where the feature selection was not used.
{"title":"Feature selection using differential evolution for microarray data classification","authors":"Sanjay Prajapati, Himansu Das, Mahendra Kumar Gourisaria","doi":"10.1007/s43926-023-00042-5","DOIUrl":"https://doi.org/10.1007/s43926-023-00042-5","url":null,"abstract":"Abstract The dimensions of microarray datasets are very large, containing noise and redundancy. The problem with microarray datasets is the presence of more features compared to the number of samples, which adversely affects algorithm performance. In other words, the number of columns exceeds the number of rows. Therefore, to extract precise information from microarray datasets, a robust technique is required. Microarray datasets play a critical role in detecting various diseases, including cancer and tumors. This is where feature selection techniques come into play. In recent times, feature selection (FS) has gained significant importance as a data preparation method, particularly for high-dimensional data. It is preferable to address classification problems with fewer features while maintaining high accuracy, as not all features are necessary to achieve this goal. The primary objective of feature selection is to identify the optimal subset of features. In this context, we will employ the Differential Evolution (DE) algorithm. DE is a population-based stochastic search approach that has found widespread use in various scientific and technical domains to solve optimization problems in continuous spaces. In our approach, we will combine DE with three different classification algorithms: Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). Our analysis will include a comparison of the accuracy achieved by each algorithmic model on each dataset, as well as the fitness error for each model. The results indicate that when feature selection was used the results were better compared to the results where the feature selection was not used.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975450","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}
Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.
{"title":"Deep edge intelligence-based solution for heart failure prediction in ambient assisted living","authors":"Md. Ishan Arefin Hossain, Anika Tabassum, Zia Ush Shamszaman","doi":"10.1007/s43926-023-00043-4","DOIUrl":"https://doi.org/10.1007/s43926-023-00043-4","url":null,"abstract":"Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135830224","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 : 2023-09-28DOI: 10.1007/s43926-023-00040-7
Zheng Xu, Deepak Kumar Jain, S. Neelakandan, Jemal Abawajy
Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.
{"title":"Hunger games search optimization with deep learning model for sustainable supply chain management","authors":"Zheng Xu, Deepak Kumar Jain, S. Neelakandan, Jemal Abawajy","doi":"10.1007/s43926-023-00040-7","DOIUrl":"https://doi.org/10.1007/s43926-023-00040-7","url":null,"abstract":"Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386846","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 : 2023-09-07DOI: 10.1007/s43926-023-00039-0
Meduri Saketh, Neha Nandal, Rohit Tanwar, B. P. Reddy
{"title":"Intelligent surveillance support system","authors":"Meduri Saketh, Neha Nandal, Rohit Tanwar, B. P. Reddy","doi":"10.1007/s43926-023-00039-0","DOIUrl":"https://doi.org/10.1007/s43926-023-00039-0","url":null,"abstract":"","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48409601","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":"Use of Internet of Things in the context of execution of smart city applications: a review","authors":"Hari Mohan Rai, Atik-Ur-Rehman, Aditya Pal, Sandeep Mishra, Kaustubh Kumar Shukla","doi":"10.1007/s43926-023-00037-2","DOIUrl":"https://doi.org/10.1007/s43926-023-00037-2","url":null,"abstract":"","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45691174","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 : 2023-07-17DOI: 10.1007/s43926-023-00036-3
Faria Sajjad, M. Rashid, A. Zafar, Kainat Zafar, Benish Fida, Ali Arshad, Saman Riaz, A. Dutta, Joel J. P. C. Rodrigues
{"title":"An efficient hybrid approach for optimization using simulated annealing and grasshopper algorithm for IoT applications","authors":"Faria Sajjad, M. Rashid, A. Zafar, Kainat Zafar, Benish Fida, Ali Arshad, Saman Riaz, A. Dutta, Joel J. P. C. Rodrigues","doi":"10.1007/s43926-023-00036-3","DOIUrl":"https://doi.org/10.1007/s43926-023-00036-3","url":null,"abstract":"","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46066165","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 : 2023-06-05DOI: 10.1007/s43926-023-00035-4
Shrivastava, Tewari, Gupta, M. S. Singh
{"title":"IoT and radio telemetry based wireless engine control and real-time position tracking system for an agricultural tractor","authors":"Shrivastava, Tewari, Gupta, M. S. Singh","doi":"10.1007/s43926-023-00035-4","DOIUrl":"https://doi.org/10.1007/s43926-023-00035-4","url":null,"abstract":"","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45999223","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 : 2023-05-30DOI: 10.1007/s43926-023-00034-5
Mansi Bhavsar, K. Roy, John Kelly, Odeyomi Olusola
{"title":"Anomaly-based intrusion detection system for IoT application","authors":"Mansi Bhavsar, K. Roy, John Kelly, Odeyomi Olusola","doi":"10.1007/s43926-023-00034-5","DOIUrl":"https://doi.org/10.1007/s43926-023-00034-5","url":null,"abstract":"","PeriodicalId":34751,"journal":{"name":"Discover Internet of Things","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48879066","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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