Pub Date : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037355
B. Priya, S. Jayalakshmy
Understanding the concurrent activity of human brain is a highly crucial task in most of the brain computer interface (BCI) applications. This study exploited the potential of empirical wavelet transform and the different time frequency visualizations for interpretating the brain functionality for external visual stimuli from magnetoencephalography signals. The study examined the four types of visualizations: spectrogram, scalogram, constant Q Gabor spectrogram and Fourier synchro squeezed representation. The proficiency of the aforementioned representations of the empirical wavelet transform (EWT) decomposed modes were assessed using GoogLeNet, a prominent transfer learning architecture. The experimental results serve as an evident that mode 3 of EWT is a dominant mode and that combined with scalogram results in a promising performance with a classification accuracy of 80.79% in decoding the human brain for visual stimuli.
{"title":"CNN - Time Frequency Representation Based Brain Wave Decoding from Magnetoencephalography Signals","authors":"B. Priya, S. Jayalakshmy","doi":"10.1109/ICDDS56399.2022.10037355","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037355","url":null,"abstract":"Understanding the concurrent activity of human brain is a highly crucial task in most of the brain computer interface (BCI) applications. This study exploited the potential of empirical wavelet transform and the different time frequency visualizations for interpretating the brain functionality for external visual stimuli from magnetoencephalography signals. The study examined the four types of visualizations: spectrogram, scalogram, constant Q Gabor spectrogram and Fourier synchro squeezed representation. The proficiency of the aforementioned representations of the empirical wavelet transform (EWT) decomposed modes were assessed using GoogLeNet, a prominent transfer learning architecture. The experimental results serve as an evident that mode 3 of EWT is a dominant mode and that combined with scalogram results in a promising performance with a classification accuracy of 80.79% in decoding the human brain for visual stimuli.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131111712","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 : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037253
Samiksha Soni, N. Londhe, Rajendra S. Sonawane
Psoriasis is an inflammatory skin disease caused due to the accelerated growth of epidermal tissues giving rise to thick, red, and scaly patches on the skin. It's a lifelong condition that can only be managed with a correct diagnosis and appropriate treatment. The current method of manual assessment for disease diagnosis is tedious and unquantifiable whereas most of the existing computer-aided methods are feature dependent and are less accurate due to the challenging task of lesion segmentation from an uneven background. To overcome these challenges, we propose a fully automatic UNet-based segmentation technique that leverages the benefit of attention and EfficientNet1l as an encoder network for transfer learning. It contains efficiently connected encoders and attention-guided decoders for psoriasis lesion segmentation. The proposed work is evaluated using the Dice Coefficient (DC) and Jaccard Index (JI). The performance result is found to be improved with 0.9590 DC and 0.9215 JI over the existing state-of-the-art method.
{"title":"Improving Performance of Psoriasis Lesion Segmentation Using Attention-UNet with EfficientNet Encoder","authors":"Samiksha Soni, N. Londhe, Rajendra S. Sonawane","doi":"10.1109/ICDDS56399.2022.10037253","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037253","url":null,"abstract":"Psoriasis is an inflammatory skin disease caused due to the accelerated growth of epidermal tissues giving rise to thick, red, and scaly patches on the skin. It's a lifelong condition that can only be managed with a correct diagnosis and appropriate treatment. The current method of manual assessment for disease diagnosis is tedious and unquantifiable whereas most of the existing computer-aided methods are feature dependent and are less accurate due to the challenging task of lesion segmentation from an uneven background. To overcome these challenges, we propose a fully automatic UNet-based segmentation technique that leverages the benefit of attention and EfficientNet1l as an encoder network for transfer learning. It contains efficiently connected encoders and attention-guided decoders for psoriasis lesion segmentation. The proposed work is evaluated using the Dice Coefficient (DC) and Jaccard Index (JI). The performance result is found to be improved with 0.9590 DC and 0.9215 JI over the existing state-of-the-art method.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127495402","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 : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037409
Kranthi Sedamaki, A. Kattepur
Supply chains are vulnerable to unforeseen delays, which might adversely affect delivery performance. Quantifying the risk profiles of each supplier based on their historic delivery patterns and forecast deviations can help make superior decisions in multi-supplier scenarios. This problem has been previously approached from linear programming and qualitative assessment perspectives; however, application of machine learning and reinforcement learning-based methods are still in a nascent stage. This paper proposes a machine learning technique to classify a supplier into one of four risk indices accurately on real-world datasets from Ericsson's supply hub. A reinforcement learning agent is also trained in a custom-modeled environment to split an order among multiple suppliers while minimizing the delays. Additionally, a working web-based tool is developed to demonstrate these techniques, that may be extended to other domains.
{"title":"Supply Chain Delay Mitigation via Supplier Risk Index Assessment and Reinforcement Learning","authors":"Kranthi Sedamaki, A. Kattepur","doi":"10.1109/ICDDS56399.2022.10037409","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037409","url":null,"abstract":"Supply chains are vulnerable to unforeseen delays, which might adversely affect delivery performance. Quantifying the risk profiles of each supplier based on their historic delivery patterns and forecast deviations can help make superior decisions in multi-supplier scenarios. This problem has been previously approached from linear programming and qualitative assessment perspectives; however, application of machine learning and reinforcement learning-based methods are still in a nascent stage. This paper proposes a machine learning technique to classify a supplier into one of four risk indices accurately on real-world datasets from Ericsson's supply hub. A reinforcement learning agent is also trained in a custom-modeled environment to split an order among multiple suppliers while minimizing the delays. Additionally, a working web-based tool is developed to demonstrate these techniques, that may be extended to other domains.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126016664","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 : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037275
M. Gopika, G. R. Bindu, M. Ponmalar, K. Usha, T. Haridas
Generating continuous and smooth paths with collision avoidance that avoid sharp turns is a significant challenge for autonomous mobile robot navigation. Sampling-based motion planners do widely use in robotics due to their computing efficiency, flexibility, and simplicity. One of the sampling-based planners, Probabilistic Roadmap(PRM), starts with a random sampling of the points in the free space. Although this sampling-based planner is generally very efficient, it can occasionally become computationally expensive when it runs dangerously close to an obstacle. In addition, the computed path can contain sharp turns challenging for the differential drive robot to navigate. Also, the path is not optimal and can be longer than necessary. The idea presented in this paper is to demonstrate how to use the gradient descent approach to find an optimal (smoother) path even though PRM provides a longer path with abrupt turns. PRM and Smoothened PRM were both run on the given operational environment and compared the performance in simulation and hardware. The simulation result shows that the algorithm can shorten the length of the searched path. The smoothness of the path has significantly improved even if the PRM offers a path with abrupt turns. Moreover, the proposed algorithm runs well on Turtlebot3 waffle pi, performing real-time obstacle avoidance.
{"title":"Smooth PRM Implementation for Autonomous Ground Vehicle","authors":"M. Gopika, G. R. Bindu, M. Ponmalar, K. Usha, T. Haridas","doi":"10.1109/ICDDS56399.2022.10037275","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037275","url":null,"abstract":"Generating continuous and smooth paths with collision avoidance that avoid sharp turns is a significant challenge for autonomous mobile robot navigation. Sampling-based motion planners do widely use in robotics due to their computing efficiency, flexibility, and simplicity. One of the sampling-based planners, Probabilistic Roadmap(PRM), starts with a random sampling of the points in the free space. Although this sampling-based planner is generally very efficient, it can occasionally become computationally expensive when it runs dangerously close to an obstacle. In addition, the computed path can contain sharp turns challenging for the differential drive robot to navigate. Also, the path is not optimal and can be longer than necessary. The idea presented in this paper is to demonstrate how to use the gradient descent approach to find an optimal (smoother) path even though PRM provides a longer path with abrupt turns. PRM and Smoothened PRM were both run on the given operational environment and compared the performance in simulation and hardware. The simulation result shows that the algorithm can shorten the length of the searched path. The smoothness of the path has significantly improved even if the PRM offers a path with abrupt turns. Moreover, the proposed algorithm runs well on Turtlebot3 waffle pi, performing real-time obstacle avoidance.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124860875","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 : 2022-12-02DOI: 10.1109/ICDDS56399.2022.10037287
Shubhangi K. Gawali, Neena Goveas
Theevolution in modern technologies like artificial intelligence, machine learning, cloud computing, edge computing, data science, etc, focuses on user perspectives like accuracy, response-time, and timeliness but at the same time consumes heavy energy due to large and fast data processing. From the system perspective, resource utilization and energy consumption are also significant design considerations. This work proposes a task migration policy for optimal core utilization and energy savings. The time taken by data analytical tasks to process the data varies, due to variations in the amount of data it analyzes in unit time. This creates variation in the core utilization due to which there exist small inactive intervals in the schedule, consuming energy. If the inactive state is known to continue for a longer duration, the core can be put into a shutdown state which effectively reduces overall energy consumption. Dynamic Procrastination (DP) is a commonly used technique to increase the inactive duration by postponing the tasks whenever possible. To further increase the inactive duration to qualify for shutting down the core, in a homogeneous multi-core (HMC) system, the jobs can be migrated to other cores. This effectively improves core utilization and reduces overall system energy without negatively affecting performance. Combining the DP and migration techniques introduces challenges like meeting deadlines, deciding upon push/pull migration, finding the number of tasks and suitable core for migration, and computation of energy consumption parameters. This paper proposes P3 (Push-Procrastinate-Pull) migration policy for the HMC system. The experimental evaluation with synthetically generated benchmark program suites shows that on an average P3reduces the overall energy by 1.2% and reduces the shutdown duration over the idle period by 2.22% over DP without migration.
{"title":"P3: A task migration policy for optimal resource utilization and energy consumption","authors":"Shubhangi K. Gawali, Neena Goveas","doi":"10.1109/ICDDS56399.2022.10037287","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037287","url":null,"abstract":"Theevolution in modern technologies like artificial intelligence, machine learning, cloud computing, edge computing, data science, etc, focuses on user perspectives like accuracy, response-time, and timeliness but at the same time consumes heavy energy due to large and fast data processing. From the system perspective, resource utilization and energy consumption are also significant design considerations. This work proposes a task migration policy for optimal core utilization and energy savings. The time taken by data analytical tasks to process the data varies, due to variations in the amount of data it analyzes in unit time. This creates variation in the core utilization due to which there exist small inactive intervals in the schedule, consuming energy. If the inactive state is known to continue for a longer duration, the core can be put into a shutdown state which effectively reduces overall energy consumption. Dynamic Procrastination (DP) is a commonly used technique to increase the inactive duration by postponing the tasks whenever possible. To further increase the inactive duration to qualify for shutting down the core, in a homogeneous multi-core (HMC) system, the jobs can be migrated to other cores. This effectively improves core utilization and reduces overall system energy without negatively affecting performance. Combining the DP and migration techniques introduces challenges like meeting deadlines, deciding upon push/pull migration, finding the number of tasks and suitable core for migration, and computation of energy consumption parameters. This paper proposes P3 (Push-Procrastinate-Pull) migration policy for the HMC system. The experimental evaluation with synthetically generated benchmark program suites shows that on an average P3reduces the overall energy by 1.2% and reduces the shutdown duration over the idle period by 2.22% over DP without migration.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130409480","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 : 2022-09-26DOI: 10.1109/ICDDS56399.2022.10037527
Soumita Das, A. Biswas
Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely Topological Variance (TV) is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.
{"title":"Towards Direct Comparison of Community Structures in Social Networks","authors":"Soumita Das, A. Biswas","doi":"10.1109/ICDDS56399.2022.10037527","DOIUrl":"https://doi.org/10.1109/ICDDS56399.2022.10037527","url":null,"abstract":"Community detection algorithms are in general evaluated by comparing evaluation metric values for the communities obtained with different algorithms. The evaluation metrics that are used for measuring quality of the communities incorporate the topological information of entities like connectivity of the nodes within or outside the communities. However, while comparing the metric values it loses direct involvement of topological information of the communities in the comparison process. In this paper, a direct comparison approach is proposed where topological information of the communities obtained with two algorithms are compared directly. A quality measure namely Topological Variance (TV) is designed based on direct comparison of topological information of the communities. Considering the newly designed quality measure, two ranking schemes are developed. The efficacy of proposed quality metric as well as the ranking scheme is studied with eight widely used real-world datasets and six community detection algorithms.","PeriodicalId":344311,"journal":{"name":"2022 IEEE 1st International Conference on Data, Decision and Systems (ICDDS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116065275","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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