Pub Date : 2022-12-01DOI: 10.1109/OCIT56763.2022.00030
Md. Abu Sayeed, Fatahi Nasrin, S. Mohanty, E. Kougianos
Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment.
{"title":"eSeiz 2.0: An IoMT Framework for Accurate Low-Latency Seizure Detection using Pulse Exclusion Mechanism","authors":"Md. Abu Sayeed, Fatahi Nasrin, S. Mohanty, E. Kougianos","doi":"10.1109/OCIT56763.2022.00030","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00030","url":null,"abstract":"Epilepsy is a neurological disorder marked by recurrent seizures. At least 3 million Americans and 1% of the global population have epilepsy, requiring a low-latency seizure detection system necessary for effective epilepsy treatment. In this paper, a pulse exclusion mechanism (PEM) based novel seizure detection system has been presented in the internet of medical things (IoMT), which uses a PEM to eliminate unnecessary features or channels and allocate desired pulses in a time frame. An optimized deep neural network (DNN) algorithm is used for feature classification. The proposed approach has been evaluated using CHB-MIT Scalp database. The results of the experiments indicate that the proposed eSeiz 2.0 offers a high specificity of 100% and a low latency of 1.05 sec, which can be useful for wearable biomedical applications as well as real-world epilepsy treatment.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"46 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":"123683563","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/OCIT56763.2022.00036
Payel Patra, Tripty Singh
Diabetic Retinopathy (DR) could be a mortal eye ailment that happens in people who have the disease named diabetics which hurts mainly on retina and after a long duration, it may lead to visual lacking. Diabetic Retinopathy Detection (DRD) through the integration of state of the art Profound Proficiency styles. This research used dataset, which was obtained from Eye Foundation Hospital Bangalore and Narayana Netralaya Bangalore, In this paper authors designed the frameworks within the field of profound Convolutional Neural Networks (CNNs), which have demonstrated progressive changes in numerous areas of computer vision counting therapeutic imaging, and researchers bring their control to the conclusion of eye fundus images. This proposed outline is combination of three stages. To begin with, the fundus picture is pre-processed utilizing an intensity of normalised procedure and augmented method. 2nd, the pre-processed picture is input to distinctive foundations of the CNN architecture in arrange to extricate a point vector for the evaluating process. 3rd, a classification is utilized for DRD and decides its review (e.g., no DR, mild, severe, moderate, or Proliferative Diabetic Retinopa-thy). A trained model with Resnet50, Inception V3, VGG-19, DenseNet-121 and MobileNetV2 architectures will extricate the Indus images of the eye. The outcome is coming with amazing exactness of 93.79 percentile, which is better by 7% than earlier work, by utilizing several activation functions in the new DiabRetNet architecture.
{"title":"Diabetic Retinopathy Detection using an Improved ResNet 50-InceptionV3 and hybrid DiabRetNet Structures","authors":"Payel Patra, Tripty Singh","doi":"10.1109/OCIT56763.2022.00036","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00036","url":null,"abstract":"Diabetic Retinopathy (DR) could be a mortal eye ailment that happens in people who have the disease named diabetics which hurts mainly on retina and after a long duration, it may lead to visual lacking. Diabetic Retinopathy Detection (DRD) through the integration of state of the art Profound Proficiency styles. This research used dataset, which was obtained from Eye Foundation Hospital Bangalore and Narayana Netralaya Bangalore, In this paper authors designed the frameworks within the field of profound Convolutional Neural Networks (CNNs), which have demonstrated progressive changes in numerous areas of computer vision counting therapeutic imaging, and researchers bring their control to the conclusion of eye fundus images. This proposed outline is combination of three stages. To begin with, the fundus picture is pre-processed utilizing an intensity of normalised procedure and augmented method. 2nd, the pre-processed picture is input to distinctive foundations of the CNN architecture in arrange to extricate a point vector for the evaluating process. 3rd, a classification is utilized for DRD and decides its review (e.g., no DR, mild, severe, moderate, or Proliferative Diabetic Retinopa-thy). A trained model with Resnet50, Inception V3, VGG-19, DenseNet-121 and MobileNetV2 architectures will extricate the Indus images of the eye. The outcome is coming with amazing exactness of 93.79 percentile, which is better by 7% than earlier work, by utilizing several activation functions in the new DiabRetNet architecture.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"12 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":"123922393","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/OCIT56763.2022.00057
M. Sayeed, Deepa Gupta
Global universities are establishing institutional setups that offer a hybrid format of education. The next step of education is to maintain quality and flexibility, such as providing the option to convert online courses such as Massive Open Online Courses (MOOCS) to course credits. However, several universities are reluctant to completely transition to online-based education due to poor digital experience in educational tools. The available evaluation tools such as Multiple-choice answers (MCQ) aren't able to evaluate students holistically. In this study, research work aims for an improvised reference-based approach (utilizing student and reference answers) that evaluates descriptive answers with the Siamese architecture- Roberta bi-encoder based transformer models for Automated Short Answer Grading (ASAG). The architecture was designed considering ASAG tasks constrained to feasible compute resources. The research work presents the competitive performance of the models, further improvised with finetuning and hyperparameter optimization process on the benchmark SemEval-2013 2way task dataset.
{"title":"Automate Descriptive Answer Grading using Reference based Models","authors":"M. Sayeed, Deepa Gupta","doi":"10.1109/OCIT56763.2022.00057","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00057","url":null,"abstract":"Global universities are establishing institutional setups that offer a hybrid format of education. The next step of education is to maintain quality and flexibility, such as providing the option to convert online courses such as Massive Open Online Courses (MOOCS) to course credits. However, several universities are reluctant to completely transition to online-based education due to poor digital experience in educational tools. The available evaluation tools such as Multiple-choice answers (MCQ) aren't able to evaluate students holistically. In this study, research work aims for an improvised reference-based approach (utilizing student and reference answers) that evaluates descriptive answers with the Siamese architecture- Roberta bi-encoder based transformer models for Automated Short Answer Grading (ASAG). The architecture was designed considering ASAG tasks constrained to feasible compute resources. The research work presents the competitive performance of the models, further improvised with finetuning and hyperparameter optimization process on the benchmark SemEval-2013 2way task dataset.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"45 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":"127175499","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/OCIT56763.2022.00112
Isha Padhy, P. Kanungo, S. Sahoo
A shadow in an image can disturb the actual outcome in computer vision and pattern recognition applications. The reason is that the shadow will act as an individual object resulting in the false interpretation and performance degradation of subsequent computer vision tasks. Here we propose a process to detect and remove shadows from an image using the YCbCr colour model. A small portion of the image is identified as a shadow area. The features at the pixel level and along the boundaries in the shadow area are learned. A method based on the locations of the border of the shadow is applied to remove the shadow. Experiments have been conducted on the benchmark camouflaged image dataset and the non-camouflaged image dataset to evaluate the approach. The methodology achieves promising performance in detecting and removing shadows from an image.
{"title":"A YCbCr Model Based Shadow Detection and Removal Approach On Camouflaged Images","authors":"Isha Padhy, P. Kanungo, S. Sahoo","doi":"10.1109/OCIT56763.2022.00112","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00112","url":null,"abstract":"A shadow in an image can disturb the actual outcome in computer vision and pattern recognition applications. The reason is that the shadow will act as an individual object resulting in the false interpretation and performance degradation of subsequent computer vision tasks. Here we propose a process to detect and remove shadows from an image using the YCbCr colour model. A small portion of the image is identified as a shadow area. The features at the pixel level and along the boundaries in the shadow area are learned. A method based on the locations of the border of the shadow is applied to remove the shadow. Experiments have been conducted on the benchmark camouflaged image dataset and the non-camouflaged image dataset to evaluate the approach. The methodology achieves promising performance in detecting and removing shadows from an image.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"88 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":"130670133","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/OCIT56763.2022.00025
Binon Teji, Swarup Roy
Graph representation learning recently has proven their excellent competency in understanding large graphs and their inner engineering for various downstream tasks. Link completion is an important computational task to guess missing edges in a network. The traditional methods extract local, pairwise information based on specific proximity statistics that are always ineffective in inferring missing links from a global topological perspective. Graph Convolutional Network (GCN) based em-bedding methods may be an effective alternative. In this work, we try to experimentally assess the power of GCN-based graph embedding techniques, namely Graph Auto Encoder (GAE) and its variants GraphSAGE, and Graph Attention Network (GAT) for link prediction tasks. Experimental results show that the GAE-based encoding methods are able to achieve superior results for predicting missing links in various real large-scale networks in comparison to traditional link prediction methods. Interestingly, our results reveal that the above techniques successfully recreate the original network with high true positive and negative rates. However, it has been observed that they produce many extra edges with an overall very high false positive rate.
{"title":"Missing Link Identification from Node Embeddings using Graph Auto Encoders and its Variants","authors":"Binon Teji, Swarup Roy","doi":"10.1109/OCIT56763.2022.00025","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00025","url":null,"abstract":"Graph representation learning recently has proven their excellent competency in understanding large graphs and their inner engineering for various downstream tasks. Link completion is an important computational task to guess missing edges in a network. The traditional methods extract local, pairwise information based on specific proximity statistics that are always ineffective in inferring missing links from a global topological perspective. Graph Convolutional Network (GCN) based em-bedding methods may be an effective alternative. In this work, we try to experimentally assess the power of GCN-based graph embedding techniques, namely Graph Auto Encoder (GAE) and its variants GraphSAGE, and Graph Attention Network (GAT) for link prediction tasks. Experimental results show that the GAE-based encoding methods are able to achieve superior results for predicting missing links in various real large-scale networks in comparison to traditional link prediction methods. Interestingly, our results reveal that the above techniques successfully recreate the original network with high true positive and negative rates. However, it has been observed that they produce many extra edges with an overall very high false positive rate.","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":"126877838","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}
Consumption of alcohol among students, mainly college or university students, has risen immensely over the past couple of years. It has been determined that students experiment with alcohol during their college years and around 80% of students consume alcohol in some manner or degree and 50% are involved in binge drinking. This is mainly due to students wanting to explore their newfound independence and freedom which they didn't have during their school years. In this paper, we have analyzed students belonging to two courses of a Secondary School-Maths and Portuguese Language Course. We have applied Feature Scaling along with various machine learning classification models to determine higher alcohol consumption where the Random Forest Model outperformed all other models that have been applied such as Linear, Ridge, and Lasso Regression, Decision Tree, k-NN, XG Boost, Support Vector Machine, ADA Boosting Regressor and Gradient Boosting Regressor for analysis of alcohol consumption among secondary school students.
{"title":"Alcohol Consumption Rate Prediction using Machine Learning Algorithms","authors":"Advait Singh, Vinay Singh, Mahendra Kumar Gourisaria, Ashish Sharma","doi":"10.1109/OCIT56763.2022.00026","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00026","url":null,"abstract":"Consumption of alcohol among students, mainly college or university students, has risen immensely over the past couple of years. It has been determined that students experiment with alcohol during their college years and around 80% of students consume alcohol in some manner or degree and 50% are involved in binge drinking. This is mainly due to students wanting to explore their newfound independence and freedom which they didn't have during their school years. In this paper, we have analyzed students belonging to two courses of a Secondary School-Maths and Portuguese Language Course. We have applied Feature Scaling along with various machine learning classification models to determine higher alcohol consumption where the Random Forest Model outperformed all other models that have been applied such as Linear, Ridge, and Lasso Regression, Decision Tree, k-NN, XG Boost, Support Vector Machine, ADA Boosting Regressor and Gradient Boosting Regressor for analysis of alcohol consumption among secondary school students.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"23 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":"115785050","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/OCIT56763.2022.00080
Anusha Vaishnav, Amulya Ratna Swain, M. R. Lenka
As the world is moving forward to the Fifth Generation (5G) of wireless technology, the demand for efficient communication techniques has also increased. 5G provides a far higher level of performance than previous generations of wireless communication in terms of low latency, increased throughput, and increased spectral efficiency. In 5G, some companion technologies have been added to strengthen the communication efficiency among the users. Device-to-Device(D2D) communication is one of these technologies to be used for modern cellular networks like 5G. D2D technology allows devices to communicate with each other without the assistance of a base station. The primary benefits of D2D communication include increased spectrum, energy efficiency, reduced transmission delay, and improved system throughput. Along with these benefits, several technical challenges include device discovery, resource allocation, mode selection, interference management, privacy, and security. In this paper, we discuss one of the challenges and the primary aspect of D2D communication, i.e., Device Discovery. The device discovery process starts when the devices transmit a discovery signal to an intermediate device to enhance the communication process by connecting with that device. Finding a potential intermediate device that will not disrupt the communication channel can sometimes become challenging. The device discovery process cannot be overlooked as it is an important step that is required before the establishment of D2D communication as well as during the communication process. In other words, device discovery is one of the key building blocks of D2D-based networks. This paper thoroughly reviews most of the important device discovery techniques for D2D communication.
{"title":"Device Discovery Approaches in D2D Communication: A Survey","authors":"Anusha Vaishnav, Amulya Ratna Swain, M. R. Lenka","doi":"10.1109/OCIT56763.2022.00080","DOIUrl":"https://doi.org/10.1109/OCIT56763.2022.00080","url":null,"abstract":"As the world is moving forward to the Fifth Generation (5G) of wireless technology, the demand for efficient communication techniques has also increased. 5G provides a far higher level of performance than previous generations of wireless communication in terms of low latency, increased throughput, and increased spectral efficiency. In 5G, some companion technologies have been added to strengthen the communication efficiency among the users. Device-to-Device(D2D) communication is one of these technologies to be used for modern cellular networks like 5G. D2D technology allows devices to communicate with each other without the assistance of a base station. The primary benefits of D2D communication include increased spectrum, energy efficiency, reduced transmission delay, and improved system throughput. Along with these benefits, several technical challenges include device discovery, resource allocation, mode selection, interference management, privacy, and security. In this paper, we discuss one of the challenges and the primary aspect of D2D communication, i.e., Device Discovery. The device discovery process starts when the devices transmit a discovery signal to an intermediate device to enhance the communication process by connecting with that device. Finding a potential intermediate device that will not disrupt the communication channel can sometimes become challenging. The device discovery process cannot be overlooked as it is an important step that is required before the establishment of D2D communication as well as during the communication process. In other words, device discovery is one of the key building blocks of D2D-based networks. This paper thoroughly reviews most of the important device discovery techniques for D2D communication.","PeriodicalId":425541,"journal":{"name":"2022 OITS International Conference on Information Technology (OCIT)","volume":"44 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":"131753912","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/OCIT56763.2022.00079
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/OCIT56763.2022.00119
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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：