Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00079
Purushottam Govind, P. S. Chatterjee
WSN's foundation is power. However, because sensor nodes are small, their batteries are also small and quickly deplete. We provide a unique type of technique to solve this issue and enable our battery to maintain extended discharge durations. For energy storage applications, electrochemical cells should have the capacity to sustain long-term self-charging. We create a battery that can be recharged without the use of outside energy sources. The redox reaction theory underlies how the battery operates. Instead of the usual ingredients, some special materials were used to produce the batteries. Utilizing anticipated data, we conducted the experiment and produced the graph. These batteries were discovered to be more effective than typical ones.
{"title":"Power Solutions for Wireless Sensor Network","authors":"Purushottam Govind, P. S. Chatterjee","doi":"10.1109/OCIT56763.2022.00079","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00079","url":null,"abstract":"WSN's foundation is power. However, because sensor nodes are small, their batteries are also small and quickly deplete. We provide a unique type of technique to solve this issue and enable our battery to maintain extended discharge durations. For energy storage applications, electrochemical cells should have the capacity to sustain long-term self-charging. We create a battery that can be recharged without the use of outside energy sources. The redox reaction theory underlies how the battery operates. Instead of the usual ingredients, some special materials were used to produce the batteries. Utilizing anticipated data, we conducted the experiment and produced the graph. These batteries were discovered to be more effective than typical ones.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131316832","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00089
Saumya Jaipuria, R. Das
In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.
{"title":"Coverage of Targets in Mobile Sensor Networks With Limited Mobility","authors":"Saumya Jaipuria, R. Das","doi":"10.1109/OCIT56763.2022.00089","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00089","url":null,"abstract":"In Mobile Sensor Networks (MSN), covering targets with minimum movement is an important issue. We consider two related problems but with a limited mobility model where no sensor can move beyond a certain distance. In the first problem, we minimize the sum of the movements of all sensors. And in the other, we minimize their maximum. We solve the first problem by relaxing the equivalent Integer Linear Program (ILP) where the maximum allowable distance is a parameter. Experimental results show that our algorithm gives the solution very close to the optimal. For the second problem, we apply binary search and repeatedly execute the relaxed LP until we find the smallest value of the maximum distance that gives a feasible solution. We could find movements of sensors that satisfy the above limit in all our experiments with different random placements of sensors and targets.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131342476","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00013
R. Krishna, K. Prema
India's most widely utilized food crop is soybean, and deep learning techniques are frequently used in forecasting and classification tasks. The minute scenario shows that the classification of the soybean crop diseases is a well-used machine learning technique with the help of images. But the proposed work, for the first time, combines soybean physic crop properties, weather properties, and deep learning techniques for classification. As a result, Random Forest and Support Vector Machine classification algorithms are utilized and the accuracy is compared with and without feature selection. Disease classification is compared using deep learning techniques like Recurrent Neural Networks, Convolutional Neural Networks, and Multi-Layer Perceptrons, along with optimization techniques like Adam, RmsProp, and AdaGrad. Results indicate that the farmers can predict soybean crop disease based on weather and the physical crop properties, hence taking preventive action.
{"title":"Optimization methods for soybean crop disease classification: A comparative study","authors":"R. Krishna, K. Prema","doi":"10.1109/OCIT56763.2022.00013","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00013","url":null,"abstract":"India's most widely utilized food crop is soybean, and deep learning techniques are frequently used in forecasting and classification tasks. The minute scenario shows that the classification of the soybean crop diseases is a well-used machine learning technique with the help of images. But the proposed work, for the first time, combines soybean physic crop properties, weather properties, and deep learning techniques for classification. As a result, Random Forest and Support Vector Machine classification algorithms are utilized and the accuracy is compared with and without feature selection. Disease classification is compared using deep learning techniques like Recurrent Neural Networks, Convolutional Neural Networks, and Multi-Layer Perceptrons, along with optimization techniques like Adam, RmsProp, and AdaGrad. Results indicate that the farmers can predict soybean crop disease based on weather and the physical crop properties, hence taking preventive action.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122316154","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00043
Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu
Evolutionary algorithms (EA) are well known algorithms and commonly used for trajectory optimization of missile. The present research work aims at comparative performance analysis of two different EAs such as genetic algorithm (GA) and differential evolution (DE) for optimization of missile gliding trajectory. The range of missile was maximized by optimizing gliding trajectory through descretization of angle of attack (AOA) as control parameter and problem solving. Evaluation of performance characteristics of GA and DE was carried out on the basis of computation time, accuracy of solution and convergence efficiency. Experimental results demonstrate the better performance of DE when compared to GA in terms of computation time, solution accuracy and convergence efficiency.
{"title":"Comparative Performance Analysis of Genetic Algorithm and Differential Evolution for Optimization of Missile Gliding Trajectory","authors":"Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu","doi":"10.1109/OCIT56763.2022.00043","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00043","url":null,"abstract":"Evolutionary algorithms (EA) are well known algorithms and commonly used for trajectory optimization of missile. The present research work aims at comparative performance analysis of two different EAs such as genetic algorithm (GA) and differential evolution (DE) for optimization of missile gliding trajectory. The range of missile was maximized by optimizing gliding trajectory through descretization of angle of attack (AOA) as control parameter and problem solving. Evaluation of performance characteristics of GA and DE was carried out on the basis of computation time, accuracy of solution and convergence efficiency. Experimental results demonstrate the better performance of DE when compared to GA in terms of computation time, solution accuracy and convergence efficiency.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126929393","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00011
P. S. Chatterjee
Serious Speech and Motor Impairment (SSMI) affects a sizeable portion of the Indian population. They are unable to communicate normally because of their physical impairment. For persons who are physically disabled, such as those who have cerebral palsy, speech disorders, or other motor neuron difficulties, the Sanyog is a tool that helps them speak and communicate. The user communicates with icons in this Iconic Communication System (ICS). The collection of chosen icons is transformed into an instantiated representation, which resembles a frame. A natural language simple sentence generator accepts this intermediate representation. The proposed work aim to creates compound sentence from the subject's input in Sanyog in Bengali language. Two simple sentences are aggregated to form compound sentence. After that we applies the rules of pronolninalization to generate pronouns which makes the compound sentence more fluent. In this paper among the different categories of pronouns we have only concentrate on anaphoric pronoun generation. Lastly the correctness of the generated sentences are checked.
{"title":"Compound sentence and pronoun generation in Sanyog: An Iconic Communication System for People with Speech and Motor Impairments","authors":"P. S. Chatterjee","doi":"10.1109/OCIT56763.2022.00011","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00011","url":null,"abstract":"Serious Speech and Motor Impairment (SSMI) affects a sizeable portion of the Indian population. They are unable to communicate normally because of their physical impairment. For persons who are physically disabled, such as those who have cerebral palsy, speech disorders, or other motor neuron difficulties, the Sanyog is a tool that helps them speak and communicate. The user communicates with icons in this Iconic Communication System (ICS). The collection of chosen icons is transformed into an instantiated representation, which resembles a frame. A natural language simple sentence generator accepts this intermediate representation. The proposed work aim to creates compound sentence from the subject's input in Sanyog in Bengali language. Two simple sentences are aggregated to form compound sentence. After that we applies the rules of pronolninalization to generate pronouns which makes the compound sentence more fluent. In this paper among the different categories of pronouns we have only concentrate on anaphoric pronoun generation. Lastly the correctness of the generated sentences are checked.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127803229","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 : 2022-12-01DOI: 10.1109/ocit56763.2022.00005
{"title":"Message from the General Chairs: OCIT 2022","authors":"","doi":"10.1109/ocit56763.2022.00005","DOIUrl":"https://doi.org/10.1109/ocit56763.2022.00005","url":null,"abstract":"","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134012467","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00046
V. G, Deepa Gupta, Vani Kanjirangat
In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach utilizes the BERT model with dependency parsing for relation extraction. The proposed model, focuses on identifying five inter subdomain relations viz., Soil_Location, Soil_Crop, Disease_Pathogen, Pathogen_Crop, and Chemical_Crop. We created a corpus of 30,000 sentences extracted from recognised agriculture sites to evaluate the model. The labeled relations were then manually checked to evaluate the prediction accuracy. We used a test corpus with 700 sentences that included 3500 triplets for the evaluation. The proposed approach presents an average macro F -Score of 86.4 %, which is quite promising for semi-supervised domain specific relation extraction systems. Experimental results show the efficacy of the proposed approach in classifying relational phrases in a semi-supervised set-up for the agricultural domain.
{"title":"Semi Supervised Approach for Relation Extraction in Agriculture Documents","authors":"V. G, Deepa Gupta, Vani Kanjirangat","doi":"10.1109/OCIT56763.2022.00046","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00046","url":null,"abstract":"In this work, we propose a semi-supervised boot-strapping approach for relation extraction in domain specific texts, specifically focusing on agricultural domain. Our approach utilizes the BERT model with dependency parsing for relation extraction. The proposed model, focuses on identifying five inter subdomain relations viz., Soil_Location, Soil_Crop, Disease_Pathogen, Pathogen_Crop, and Chemical_Crop. We created a corpus of 30,000 sentences extracted from recognised agriculture sites to evaluate the model. The labeled relations were then manually checked to evaluate the prediction accuracy. We used a test corpus with 700 sentences that included 3500 triplets for the evaluation. The proposed approach presents an average macro F -Score of 86.4 %, which is quite promising for semi-supervised domain specific relation extraction systems. Experimental results show the efficacy of the proposed approach in classifying relational phrases in a semi-supervised set-up for the agricultural domain.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131887959","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00119
Sukrutha L. T. Vangipuram, S. Mohanty, E. Kougianos
This paper discusses how agriculture has become one of the prime reasons for the wastage of energy and water during food production. In order to control the use of resources in farming, we introduce a novel concept called IncentiveChain. The application idea is to distribute crypto ether as a reward to the farmers because they play key roles in keeping a check on resource usage and can benefit through these schemes economically. We provide a state-of-the-art architecture and design, which includes participation from national agricultural departments and local regional utility companies to embed various technologies and data together to make the IncentiveChain application practical. We have successfully implemented IncentiveChain to show the transfer of ether from utility company accounts to farmer accounts and the currency being collected by the farmer in a more secure way using the blockchain, removing third-party vulnerabilities.
{"title":"IncentiveChain: Blockchain Crypto-Incentive for Effective Usage of Power and Water in Smart Farming","authors":"Sukrutha L. T. Vangipuram, S. Mohanty, E. Kougianos","doi":"10.1109/OCIT56763.2022.00119","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00119","url":null,"abstract":"This paper discusses how agriculture has become one of the prime reasons for the wastage of energy and water during food production. In order to control the use of resources in farming, we introduce a novel concept called IncentiveChain. The application idea is to distribute crypto ether as a reward to the farmers because they play key roles in keeping a check on resource usage and can benefit through these schemes economically. We provide a state-of-the-art architecture and design, which includes participation from national agricultural departments and local regional utility companies to embed various technologies and data together to make the IncentiveChain application practical. We have successfully implemented IncentiveChain to show the transfer of ether from utility company accounts to farmer accounts and the currency being collected by the farmer in a more secure way using the blockchain, removing third-party vulnerabilities.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133324052","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00044
Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu
Swarm intelligence algorithms were widely employed for trajectory optimization problem. The current study presents a comparative performance analysis of two well known swarm intelligence algorithms such as particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm for optimization of missile gliding trajectory. The gliding range was maximized through trajectory optimization of missile by descretizing the angle of attack (AOA) as control parameter and solving control problem. Performance characteristics of PSO and ABC were evaluated based on computational efficiency, accuracy of solution and convergence ability. The obtained results reveal the superior performance of PSO with regard to accuracy of solution, computational efficacy and convergence ability in comparison to ABC.
{"title":"Comparative Performance Analysis of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Optimization of Missile Gliding Trajectory","authors":"Shubhashree Sahoo, R. Dalei, S. Rath, U. Sahu","doi":"10.1109/OCIT56763.2022.00044","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00044","url":null,"abstract":"Swarm intelligence algorithms were widely employed for trajectory optimization problem. The current study presents a comparative performance analysis of two well known swarm intelligence algorithms such as particle swarm optimization (PSO) and artificial bee colony (ABC) algorithm for optimization of missile gliding trajectory. The gliding range was maximized through trajectory optimization of missile by descretizing the angle of attack (AOA) as control parameter and solving control problem. Performance characteristics of PSO and ABC were evaluated based on computational efficiency, accuracy of solution and convergence ability. The obtained results reveal the superior performance of PSO with regard to accuracy of solution, computational efficacy and convergence ability in comparison to ABC.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131850653","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 : 2022-12-01DOI: 10.1109/OCIT56763.2022.00035
Remya Sivan, Tripty Singh, P. Pati
Ancient manuscripts like palm leaves, available in museum libraries, are a rich source of knowledge. Digitization helps store this knowledge protected for the future & enables its global access. Varying writing styles, presence of currently discarded & rare characters, quality of imaging, and palm leaves are some of the challenges to be handled while building an offline handwritten recognition system for these manuscripts. This paper focuses on recognizing Malayalam characters available in palm leaves using deep learning techniques. With the help of the histogram and contour method, lines are segmented from palm leaves first. Subsequently, individual characters are extracted from the lines. A customized Convolution Neural Network (CNN) is employed to recognize these segmented characters. This trained CNN recognizes forty-eight classes of segmented characters with 86% accuracy. Additionally, this paper compares the results with other standard CNN models.
{"title":"Malayalam Character Recognition from Palm Leaves Using Deep-Learning","authors":"Remya Sivan, Tripty Singh, P. Pati","doi":"10.1109/OCIT56763.2022.00035","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00035","url":null,"abstract":"Ancient manuscripts like palm leaves, available in museum libraries, are a rich source of knowledge. Digitization helps store this knowledge protected for the future & enables its global access. Varying writing styles, presence of currently discarded & rare characters, quality of imaging, and palm leaves are some of the challenges to be handled while building an offline handwritten recognition system for these manuscripts. This paper focuses on recognizing Malayalam characters available in palm leaves using deep learning techniques. With the help of the histogram and contour method, lines are segmented from palm leaves first. Subsequently, individual characters are extracted from the lines. A customized Convolution Neural Network (CNN) is employed to recognize these segmented characters. This trained CNN recognizes forty-eight classes of segmented characters with 86% accuracy. Additionally, this paper compares the results with other standard CNN models.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129038037","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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