The redundancy of the data is an active research topic. While an agent works in a multi-agent system, the number of messages between them increases. This is due to the fact that the functionalities data depends on other agents in terms of functional requirements. Typically, only one agent in a multi-agent system is responsible for accessing a database instead of replicating the database on each agent. A database is stored on multiple agents rather than a single agent to avoid a single point of failure. In this approach, the system has a higher load because one agent is responsible for all agent queries and must send duplicate messages to multiple agents, resulting in redundant data. In this research, we present Multi-Agent System for Commodity Data (MASCD) framework, the multi-agent system based communication using the distributed hash system, to reduce data redundancy in multi-agent system communication. Our anticipated method demonstrated how we divided the database names and efficiently distributed data to each agent. The database splitting is based on manufacturer names or product names. We utilize a table based on prime numbers. Through the hash function, we ascertain the index of the agent granted access to the relevant data. Each agent is accountable for its data. We use a Distributed Hash Table for efficient querying that stores data as key-value pairs. Each agent maintains a Finger Table containing the next and previous nodes for agent communication purposes. Using FIPA messages, we demonstrated how an agent could interact optimally. In conclusion, we illustrate the application of the proposed approach through a case study of mobile phones and university information systems.
{"title":"An effective approach for reducing data redundancy in multi-agent system communication","authors":"Awais Qasim, Arslan Ghouri, Adeel Munawar","doi":"10.3233/mgs-230089","DOIUrl":"https://doi.org/10.3233/mgs-230089","url":null,"abstract":"The redundancy of the data is an active research topic. While an agent works in a multi-agent system, the number of messages between them increases. This is due to the fact that the functionalities data depends on other agents in terms of functional requirements. Typically, only one agent in a multi-agent system is responsible for accessing a database instead of replicating the database on each agent. A database is stored on multiple agents rather than a single agent to avoid a single point of failure. In this approach, the system has a higher load because one agent is responsible for all agent queries and must send duplicate messages to multiple agents, resulting in redundant data. In this research, we present Multi-Agent System for Commodity Data (MASCD) framework, the multi-agent system based communication using the distributed hash system, to reduce data redundancy in multi-agent system communication. Our anticipated method demonstrated how we divided the database names and efficiently distributed data to each agent. The database splitting is based on manufacturer names or product names. We utilize a table based on prime numbers. Through the hash function, we ascertain the index of the agent granted access to the relevant data. Each agent is accountable for its data. We use a Distributed Hash Table for efficient querying that stores data as key-value pairs. Each agent maintains a Finger Table containing the next and previous nodes for agent communication purposes. Using FIPA messages, we demonstrated how an agent could interact optimally. In conclusion, we illustrate the application of the proposed approach through a case study of mobile phones and university information systems.","PeriodicalId":508072,"journal":{"name":"Multiagent and Grid Systems","volume":"124 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360831","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}
Osteoporosis is a disorder, that leads to fractures and fatal problems in bones. It is believed that more than 200 million individuals are affected globally. Furthermore, osteoporosis is caused by micro-architectural degeneration of bone tissues, which increases the risk of bone fragility and fractures. Moreover, the osteoporosis categorization is essential for the medical industry, which classifies the skeleton problems of individuals caused by ageing. This work presented the prediction of femur bone volume for osteoporosis classification. Moreover, the femur bone X-ray image is utilized for the classification. The preprocessing phase is employed to neglect the noise contained in input bone images through a non-local means filter. In the image segmentation process, the SegNet is utilized to isolate the specific portion. Moreover, the template search approach based on femoral geometric estimation is carried out and the feature extraction phase is essential for a significant feature extraction process. The proposed tuna jellyfish optimization based deep batch-normalized eLU AlexNet (DbneAlexNet) is utilized in the osteoporosis classification process. Furthermore, accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR) and True Negative Rate (TNR) are the metrics to validate the model and the superior values 0.913, 0.906, 0.896, 0.923 and 0.932 are achieved.
骨质疏松症是一种疾病，会导致骨折和致命的骨骼问题。据信，全球有超过 2 亿人受到影响。此外，骨质疏松症是由骨组织的微结构退化引起的，这增加了骨脆性和骨折的风险。此外，骨质疏松症的分类对于医疗行业来说至关重要，它可以对衰老导致的个人骨骼问题进行分类。这项工作介绍了用于骨质疏松症分类的股骨骨量预测。此外，分类还利用了股骨 X 光图像。预处理阶段通过非局部均值滤波器忽略输入骨骼图像中的噪声。在图像分割过程中，利用 SegNet 分离出特定部分。此外，还采用了基于股骨几何估算的模板搜索方法，而特征提取阶段对于重要的特征提取过程至关重要。在骨质疏松症分类过程中，使用了所提出的基于金枪鱼水母优化的深度批量归一化 eLU AlexNet（DbneAlexNet）。此外，准确率、阳性预测值（PPV）、阴性预测值（NPV）、真阳性率（TPR）和真阴性率（TNR）是验证该模型的指标，其优越值分别为 0.913、0.906、0.896、0.923 和 0.932。
{"title":"Femur bone volumetric estimation for osteoporosis classification based on deep learning with tuna jellyfish optimization using X-ray images","authors":"Halesh T G, Sathish P.","doi":"10.3233/mgs-230123","DOIUrl":"https://doi.org/10.3233/mgs-230123","url":null,"abstract":"Osteoporosis is a disorder, that leads to fractures and fatal problems in bones. It is believed that more than 200 million individuals are affected globally. Furthermore, osteoporosis is caused by micro-architectural degeneration of bone tissues, which increases the risk of bone fragility and fractures. Moreover, the osteoporosis categorization is essential for the medical industry, which classifies the skeleton problems of individuals caused by ageing. This work presented the prediction of femur bone volume for osteoporosis classification. Moreover, the femur bone X-ray image is utilized for the classification. The preprocessing phase is employed to neglect the noise contained in input bone images through a non-local means filter. In the image segmentation process, the SegNet is utilized to isolate the specific portion. Moreover, the template search approach based on femoral geometric estimation is carried out and the feature extraction phase is essential for a significant feature extraction process. The proposed tuna jellyfish optimization based deep batch-normalized eLU AlexNet (DbneAlexNet) is utilized in the osteoporosis classification process. Furthermore, accuracy, Positive Predictive Value (PPV), Negative Predictive Value (NPV), True Positive Rate (TPR) and True Negative Rate (TNR) are the metrics to validate the model and the superior values 0.913, 0.906, 0.896, 0.923 and 0.932 are achieved.","PeriodicalId":508072,"journal":{"name":"Multiagent and Grid Systems","volume":"33 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355450","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}
Mohammed Alweshah, Mustafa Alessa, Saleh Alkhalaileh, Sofian Kassaymeh, Bilal Abu-Salih
The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β-hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively.
{"title":"Hybrid Aquila optimizer for efficient classification with probabilistic neural networks","authors":"Mohammed Alweshah, Mustafa Alessa, Saleh Alkhalaileh, Sofian Kassaymeh, Bilal Abu-Salih","doi":"10.3233/mgs-230065","DOIUrl":"https://doi.org/10.3233/mgs-230065","url":null,"abstract":"The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer based probabilistic neural network (AO-PNN), which uses both local and global search capabilities of AO, and hybrid Aquila optimizer and simulated annealing based probabilistic neural network (AOS-PNN), which integrates the global search abilities of AO with the local search mechanism of simulated annealing (SA). Our experimental results indicate that both AO-PNN and AOS-PNN perform better than the PNN model in terms of accuracy across all datasets. This suggests that they have the potential to generate more precise results when utilized to improve PNN parameters. Moreover, our hybridization technique, AOS-PNN, is more effective than AO-PNN, as evidenced by classification experiments accuracy, data distribution, convergence speed, and significance. We have also compared our suggested approaches with three different methodologies, namely Coronavirus herd immunity optimizer based probabilistic neural network (CHIO-PNN), African buffalo algorithm based probabilistic neural network (ABO-PNN), and β-hill climbing. We have found that AO-PNN and AOS-PNN have achieved significantly higher classification accuracy rates of 90.68 and 93.95, respectively.","PeriodicalId":508072,"journal":{"name":"Multiagent and Grid Systems","volume":"33 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358905","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}
Nilesh Uke, Pravin Futane, Neeta Deshpande, Shailaja N. Uke
A deep learning algorithm tracks an object’s movement during object tracking and the main challenge in the tracking of objects is to estimate or forecast the locations and other pertinent details of moving objects in a video. Typically, object tracking entails the process of object detection. In computer vision applications the detection, classification, and tracking of objects play a vital role, and gaining information about the various techniques available also provides significance. In this research, a systematic literature review of the object detection techniques is performed by analyzing, summarizing, and examining the existing works available. Various state of art works are collected from standard journals and the methods available, cons, and pros along with challenges are determined based on this the research questions are also formulated. Overall, around 50 research articles are collected, and the evaluation based on various metrics shows that most of the literary works used Deep convolutional neural networks (Deep CNN), and while tracking the objects object detection helps in enhancing the performance of these networks. The important issues that need to be resolved are also discussed in this research, which helps in leveling up the object-tracking techniques.
{"title":"A review on deep learning-based object tracking methods","authors":"Nilesh Uke, Pravin Futane, Neeta Deshpande, Shailaja N. Uke","doi":"10.3233/mgs-230126","DOIUrl":"https://doi.org/10.3233/mgs-230126","url":null,"abstract":"A deep learning algorithm tracks an object’s movement during object tracking and the main challenge in the tracking of objects is to estimate or forecast the locations and other pertinent details of moving objects in a video. Typically, object tracking entails the process of object detection. In computer vision applications the detection, classification, and tracking of objects play a vital role, and gaining information about the various techniques available also provides significance. In this research, a systematic literature review of the object detection techniques is performed by analyzing, summarizing, and examining the existing works available. Various state of art works are collected from standard journals and the methods available, cons, and pros along with challenges are determined based on this the research questions are also formulated. Overall, around 50 research articles are collected, and the evaluation based on various metrics shows that most of the literary works used Deep convolutional neural networks (Deep CNN), and while tracking the objects object detection helps in enhancing the performance of these networks. The important issues that need to be resolved are also discussed in this research, which helps in leveling up the object-tracking techniques.","PeriodicalId":508072,"journal":{"name":"Multiagent and Grid Systems","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141357037","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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