Recently, psychologist has experienced drastic development using statistical methods to analyze the interactions of humans. The intention of past decades of psychological studies is to model how individuals learn elements and types. The scientific validation of such studies is often based on straightforward illustrations of artificial stimuli. Recently, in activities such as recognizing items in natural pictures, strong neural networks have reached or exceeded human precision. In this paper, we present Relevance Networks (RNs) as a basic plug-and-play application with Covolutionary Neural Network (CNN) to address issues that are essentially related to reasoning. Thus our proposed network performs visual answering the questions, superhuman performance and text based answering. All of these have been accomplished by complex reasoning on diverse physical systems. Thus, by simply increasing convolutions, (Long Short Term Memory) LSTMs, and (Multi-Layer Perceptron) MLPs with RNs, we can remove the computational burden from network components that are unsuitable for handling relational reasoning, reduce the overall complexity of the network, and gain a general ability to reason about the relationships between entities and their properties.
{"title":"Enlightening and Predicting the Correlation Around Deep Neural Nets and Cognitive Perceptions","authors":"Chandra Bhim Bhan Singh","doi":"10.46300/9108.2020.14.9","DOIUrl":"https://doi.org/10.46300/9108.2020.14.9","url":null,"abstract":"Recently, psychologist has experienced drastic development using statistical methods to analyze the interactions of humans. The intention of past decades of psychological studies is to model how individuals learn elements and types. The scientific validation of such studies is often based on straightforward illustrations of artificial stimuli. Recently, in activities such as recognizing items in natural pictures, strong neural networks have reached or exceeded human precision. In this paper, we present Relevance Networks (RNs) as a basic plug-and-play application with Covolutionary Neural Network (CNN) to address issues that are essentially related to reasoning. Thus our proposed network performs visual answering the questions, superhuman performance and text based answering. All of these have been accomplished by complex reasoning on diverse physical systems. Thus, by simply increasing convolutions, (Long Short Term Memory) LSTMs, and (Multi-Layer Perceptron) MLPs with RNs, we can remove the computational burden from network components that are unsuitable for handling relational reasoning, reduce the overall complexity of the network, and gain a general ability to reason about the relationships between entities and their properties.","PeriodicalId":89779,"journal":{"name":"International journal of computers in healthcare","volume":"105 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74442750","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}
The energy consumption is becoming a constraint on all computer devices, from smartphones to supercomputers. Consequently, the focus has moved from performance to energy and power consumption. Design metrics are not only based solely on performance, as the energy performance of application executions is becoming the main aspect of architecture. Also, Design metrics depend on, the manufacturers of semiconductor chips which, have implemented multicore processors to boost the level of energy efficiency by using verified techniques for voltage and frequency scaling. To utilize the maximum potential of such architectures, we need to make the right decisions because parameters such as core type, frequency, and utilization typically affect power dissipation and performance. This paper proposes a new algorithm to achieve energy-efficient by monitoring core energy and level utilization control such as: Increasing the number of cores to execute the task, scaling voltage, and frequency. Based on the built model, we analyze the energy efficiency variations for different platform configurations providing the same level of performance. We show that trading the number and type of core with frequency and voltage level and core utilization rate can lead to substantial energy efficiency gains.
{"title":"A New Core Level Utilization Algorithm for Energy-Efficient Multicore Systems","authors":"Samar Nour, Sameh A. Salem, S. Habashy","doi":"10.46300/9108.2020.14.7","DOIUrl":"https://doi.org/10.46300/9108.2020.14.7","url":null,"abstract":"The energy consumption is becoming a constraint on all computer devices, from smartphones to supercomputers. Consequently, the focus has moved from performance to energy and power consumption. Design metrics are not only based solely on performance, as the energy performance of application executions is becoming the main aspect of architecture. Also, Design metrics depend on, the manufacturers of semiconductor chips which, have implemented multicore processors to boost the level of energy efficiency by using verified techniques for voltage and frequency scaling. To utilize the maximum potential of such architectures, we need to make the right decisions because parameters such as core type, frequency, and utilization typically affect power dissipation and performance. This paper proposes a new algorithm to achieve energy-efficient by monitoring core energy and level utilization control such as: Increasing the number of cores to execute the task, scaling voltage, and frequency. Based on the built model, we analyze the energy efficiency variations for different platform configurations providing the same level of performance. We show that trading the number and type of core with frequency and voltage level and core utilization rate can lead to substantial energy efficiency gains.","PeriodicalId":89779,"journal":{"name":"International journal of computers in healthcare","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88145055","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}
Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.
{"title":"Lossless Compression of Medical Images based on the Differential Probability of Images","authors":"","doi":"10.46300/9108.2020.14.1","DOIUrl":"https://doi.org/10.46300/9108.2020.14.1","url":null,"abstract":"Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.","PeriodicalId":89779,"journal":{"name":"International journal of computers in healthcare","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83172163","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 : 2018-01-01DOI: 10.1504/IJCIH.2018.10015645
B. S. Mahanand, G. S. Babu
{"title":"Computer Aided Detection of Imaging Biomarkers for Alzheimers Disease","authors":"B. S. Mahanand, G. S. Babu","doi":"10.1504/IJCIH.2018.10015645","DOIUrl":"https://doi.org/10.1504/IJCIH.2018.10015645","url":null,"abstract":"","PeriodicalId":89779,"journal":{"name":"International journal of computers in healthcare","volume":"2 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66720813","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 : 2012-01-01DOI: 10.1504/IJCIH.2012.046995
Asit K Saha, Sean S Kohles
Physiologic regulation of extracellular matrix (ECM) in articular cartilage tissue is controlled by cellular and molecular mechanisms which are not fully understood. It has been observed that the synthesis of the ECM structural molecules, glycosaminoglycan and collagen are promoted by growth factors such as IGF-1 and TGF-β. Concomitant ECM degradation is promoted by a variety of cytokines such as IL-1. The clinical need for reparative therapies of articular cartilage is linked with its poor intrinsic healing capacity. The following modelling approach was applied to engineered cartilage as a platform for exploring cartilage biology and to introduce a predictive tool as a bioinformatic support system supporting regenerative therapies. Systems biology was adapted through a mathematical framework producing a computational intelligence paradigm to explore a controlled phasic regulatory influence of the inhibition and production of ECM biomolecules. Model outcomes describe a steady synthesis of ECM as a dependence on a cyclic influence of the catabolic action of proteases and anabolic action of growth factors. This relationship is shown quantitatively in a governing harmonic equation representing the simplified biological mechanisms of biomolecule homeostasis.
{"title":"A cell-matrix model of anabolic and catabolic dynamics during cartilage biomolecule regulation.","authors":"Asit K Saha, Sean S Kohles","doi":"10.1504/IJCIH.2012.046995","DOIUrl":"https://doi.org/10.1504/IJCIH.2012.046995","url":null,"abstract":"<p><p>Physiologic regulation of extracellular matrix (ECM) in articular cartilage tissue is controlled by cellular and molecular mechanisms which are not fully understood. It has been observed that the synthesis of the ECM structural molecules, glycosaminoglycan and collagen are promoted by growth factors such as IGF-1 and TGF-β. Concomitant ECM degradation is promoted by a variety of cytokines such as IL-1. The clinical need for reparative therapies of articular cartilage is linked with its poor intrinsic healing capacity. The following modelling approach was applied to engineered cartilage as a platform for exploring cartilage biology and to introduce a predictive tool as a bioinformatic support system supporting regenerative therapies. Systems biology was adapted through a mathematical framework producing a computational intelligence paradigm to explore a controlled phasic regulatory influence of the inhibition and production of ECM biomolecules. Model outcomes describe a steady synthesis of ECM as a dependence on a cyclic influence of the catabolic action of proteases and anabolic action of growth factors. This relationship is shown quantitatively in a governing harmonic equation representing the simplified biological mechanisms of biomolecule homeostasis.</p>","PeriodicalId":89779,"journal":{"name":"International journal of computers in healthcare","volume":"1 3","pages":"214-228"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJCIH.2012.046995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31528421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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