1. The document discusses using convolutional deep belief networks to simulate the human brain and identify brain diseases. It compares the predictive accuracy of artificial neural networks, machine learning, and deep learning for diseases like Alzheimer's and Parkinson's, finding that convolutional deep belief networks performed best.
2. It provides background on computational neuroscience and outlines models of neurons, neural systems, and brain function at different scales that are used to test theories about the brain.
3. The summary briefly describes key parts of the human brain like the cerebral cortex, cerebellum, thalamus, hypothalamus, and hippocampus and their functions in areas like motor control, memory, learning, emotion, and decision making.
The purpose of this book is to provide an overview of medical image processing and related health care services in variant diseases like Alzheimer’s disease, glaucoma, mild cognitive impairment, dementia, and other neurodegenerative syndromes. It is anticipated that it will be useful for research scientists to capture recent developments and to spark innovative ideas within the medical image processing domain. With an emphasis on both the basic and advanced applications of medical imaging, this book covers several unique concepts like optimized image fusion, ophthalmic hashing, and linear bootstrap aggregating that have been graphically represented to improve readability, such as the optimized image fusion introduced in chapter 1, linear bootstrap aggregating discussed in Chapter 2 and ophthalmic hashing that is proposed in Chapter 4. The remainder of the book emphases on the area application-orientated image fusions, which cover the numerous expanses of medical image processing and its applications.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
The purpose of this book is to provide an overview of medical image processing and related health care services in variant diseases like Alzheimer’s disease, glaucoma, mild cognitive impairment, dementia, and other neurodegenerative syndromes. It is anticipated that it will be useful for research scientists to capture recent developments and to spark innovative ideas within the medical image processing domain. With an emphasis on both the basic and advanced applications of medical imaging, this book covers several unique concepts like optimized image fusion, ophthalmic hashing, and linear bootstrap aggregating that have been graphically represented to improve readability, such as the optimized image fusion introduced in chapter 1, linear bootstrap aggregating discussed in Chapter 2 and ophthalmic hashing that is proposed in Chapter 4. The remainder of the book emphases on the area application-orientated image fusions, which cover the numerous expanses of medical image processing and its applications.
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Sleep quality prediction from wearable data using deep learningLuis Fernandez Luque
Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR Mhealth Uhealth 2016;4(4):e125. http://doi.org/10.2196/mhealth.6562
The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.
A transfer learning with deep neural network approach for diabetic retinopath...IJECEIAES
Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
An overview on Advanced Research Works on Brain-Computer InterfaceWaqas Tariq
A brain–computer interface (BCI) is a proficient result in the research field of human- computer synergy, where direct articulation between brain and an external device occurs resulting in augmenting, assisting and repairing human cognitive. Advanced works like generating brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments or developing brain-computer interface by Fuzzy logic Systems or by implementing wavelet theory to drive its efficacies are still going on and some useful results has also been found out. The requirements to develop this brain machine interface is also growing day by day i.e. like neuropsychological rehabilitation, emotion control, etc. An overview on the control theory and some advanced works on the field of brain machine interface are shown in this paper.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
Automated fundus image quality assessment and segmentation of optic disc usin...IJECEIAES
An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIMONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Tumor Detection and Classification of MRI Brain Images using SVM and DNNijtsrd
The brain is one of the most complex organ in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Brain tumors are classified as benign or low grade Grade 1 and 2 and malignant tumors or high grade Grade 3 and 4 . The proposed methodology aims to differentiate between normal brain and tumor brain Benign or Melign . The proposed method in this paper is automated framework for differentiate between normal brain and tumor brain. Then our method is used to predict the diseases accurately. Then these methods are used to predict the disease is affected or not by using a comparison method. These methodology are validated by a comprehensive set of comparison against competing and well established image registration methods, by using real medical data sets and classic measures typically employed as a benchmark by the medical imaging community our proposed method is mostly used in medical field. It is used to easily detect the diseases. We demonstrate the accuracy and effectiveness of the preset framework throughout a comprehensive set of qualitative comparisons against several influential state of the art methods on various brain image databases. Sanmathi. R | Sujitha. K | Susmitha. G | Gnanasekaran. S ""Tumor Detection and Classification of MRI Brain Images using SVM and DNN"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30192.pdf
Paper Url : https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30192/tumor-detection-and-classification-of-mri-brain-images-using-svm-and-dnn/sanmathi-r
Sleep quality prediction from wearable data using deep learningLuis Fernandez Luque
Sathyanarayana A, Joty S, Fernandez-Luque L, Ofli F, Srivastava J, Elmagarmid A, Arora T, Taheri S. Sleep Quality Prediction From Wearable Data Using Deep Learning. JMIR Mhealth Uhealth 2016;4(4):e125. http://doi.org/10.2196/mhealth.6562
The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.
A transfer learning with deep neural network approach for diabetic retinopath...IJECEIAES
Diabetic retinopathy is an eye disease caused by high blood sugar and pressure which damages the blood vessels in the eye. Diabetic retinopathy is the root cause of more than 1% of the blindness worldwide. Early detection of this disease is crucial as it prevents it from progressing to a more severe level. However, the current machine learning-based approaches for detecting the severity level of diabetic retinopathy are either, i) rely on manually extracting features which makes an approach unpractical, or ii) trained on small dataset thus cannot be generalized. In this study, we propose a transfer learning-based approach for detecting the severity level of the diabetic retinopathy with high accuracy. Our model is a deep learning model based on global average pooling (GAP) technique with various pre-trained convolutional neural net- work (CNN) models. The experimental results of our approach, in which our best model achieved 82.4% quadratic weighted kappa (QWK), corroborate the ability of our model to detect the severity level of diabetic retinopathy efficiently.
An overview on Advanced Research Works on Brain-Computer InterfaceWaqas Tariq
A brain–computer interface (BCI) is a proficient result in the research field of human- computer synergy, where direct articulation between brain and an external device occurs resulting in augmenting, assisting and repairing human cognitive. Advanced works like generating brain-computer interface switch technologies for intermittent (or asynchronous) control in natural environments or developing brain-computer interface by Fuzzy logic Systems or by implementing wavelet theory to drive its efficacies are still going on and some useful results has also been found out. The requirements to develop this brain machine interface is also growing day by day i.e. like neuropsychological rehabilitation, emotion control, etc. An overview on the control theory and some advanced works on the field of brain machine interface are shown in this paper.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
An Improved Self Organizing Feature Map Classifier for Multimodal Biometric R...ijtsrd
Multimodal biometric system is a system that is viable in authentication and capable of carrying the robustness of the system. Most existing biometric systems ear fingerprint and face ear suffer varying challenges such as large variability, high dimensionality, small sample size and average recognition time. These lead to the degrading performance and accuracy of the system. Sequel to this, multimodal biometric system was developed to overcome those challenges. The system was implemented in MATLAB environment. Am improved self organizing feature map was used to classify the fused features into known and unknown. The performance of the developed multimodal was evaluated based on sensitivity, recognition accuracy and time. Olabode, A. O | Amusan, D. G | Ajao, T. A "An Improved Self Organizing Feature Map Classifier for Multimodal Biometric Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26458.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26458/an-improved-self-organizing-feature-map-classifier-for-multimodal-biometric-recognition-system/olabode-a-o
Pattern Recognition using Artificial Neural NetworkEditor IJCATR
An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the
distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES acijjournal
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world -- for example, in
the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships. We will look at recent trends in functional magnetic resonance imaging (fMRI), with a special focus on the questions that have been addressed. This focus is
particularly important for functional neuroimaging, whose contributions will be measured by the depth of the questions asked. The ever-increasing understanding of the brain and behavior at work in the real world, the development of theoretical underpinnings, and the relentless spread of facilitative technology in the West and abroad are inexorably broadening the substrates for this interdisciplinary area of
research and practice. Neuroergonomics blends neuroscience and ergonomics to the mutual benefit of both fields, and extends the study of brain structure and function beyond the contrived laboratory settings often used in neuropsychological, psychophysical, cognitive science, and other neurosciencerelated fields. Neuroergonomics is providing rich observations of the brain and behavior at work, at home, in
transportation, and in other everyday environments in human operators who see, hear, feel, attend, remember, decide, plan, act, move, or manipulate objects among other people and technology in diverse, real-world settings. The neuroergonomics approach is allowing researchers to ask different questions
and develop new explanatory frameworks about humans at work in the real world and in relation to modern automated systems and machines, drawing from principles of neuropsychology, psychophysics, neurophysiology, and anatomy at neuronal and systems levels. The neuroergonomics approach allows researchers to ask different questions and develop new explanatory frameworks about humans at work in
the real world and in relation to modern automated systems and machines. Better understanding of brain function can, for example, provide important guidelines and constraints for theories of information presentation and task design, optimization of alerting and warning signals, development of neural prostheses, and the design of robots. As an interdisciplinary endeavor, neuroergonomics will continue to
benefit from and grow alongside developments in neuroscience, psychology,
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
A Parallel Framework For Multilayer Perceptron For Human Face RecognitionCSCJournals
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Study on Different Human Emotions Using Back Propagation Methodijiert bestjournal
With fast evolving technology,Cognitive Science plays a vital role in our day-to-day life. Cognitive science is summed up as the study of mind based on scientific methods. It is al l about the sum of all interdisciplinary like philosophy,psychology,linguistics,artificial intelligence,robot ics,and neuroscience. In this paper,I focused on the facial expressions or emotions of human being as it has an impor tant role in interpersonal relations. Without verb communication,one can imagine the mood of a person by expressions. In this method,we use back propagation neural network for implementation. It is an information proce ssing system that has been developed as a generalization of the mathematical model of human recognition.
July 2022: Top 10 Read Articles in Signal & Image Processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
April 2023: Top 10 Read Articles in Signal & Image Processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
June 2022: Top 10 Read Articles in Signal & Image Processingsipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
Classifier Model using Artificial Neural NetworkAI Publications
When it comes to AI and ML, precision in categorization is of the utmost importance. In this research, the use of supervised instance selection (SIS) to improve the performance of artificial neural networks (ANNs) in classification is investigated. The goal of SIS is to enhance the accuracy of future classification tasks by identifying and selecting a subset of examples from the original dataset. The purpose of this research is to provide light on how useful SIS is as a preprocessing tool for artificial neural network-based classification. The work aims to improve the input dataset to ANNs by using SIS, which may help with problems caused by noisy or redundant data. The ultimate goal is to improve ANNs' ability to identify data points properly across a wide range of application areas.
Model for autism disorder detection using deep learningIAESIJAI
Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE).
An optimized approach for extensive segmentation and classification of brain ...IJECEIAES
With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/ background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme.
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2. PSJ Kumar et al. IJIMR 1(2) 180-191
specialisation in CSE from the Annamalai University, Chidambaram, India. He
achieved his MS in Computer Engineering from the New Jersey Institute of
Technology, Newark, and the USA in 2006 and his Doctorate from University
of Cambridge, the United Kingdom in 2013. He completed his postdoctoral
fellowship in the project titled "Bucolic and Farming Region Taxonomy Using
Neural Networks for Remote Sensing Images" at Dartmouth College, Hanover,
the USA in the Department of Earth Sciences and Remote Sensing (EARS).
Yanmin Yaun is working as Professor in the Department of Bioengineering,
Harvard University, and Cambridge, United States from 2016. She completed
her Ph.D from Tokyo University in the year 2009 and served Nanyang
Technological University, Singapore from 2011 to 2015. She is at present
betrothed with the “Deep Robotics and Human Brain” project funded by
Harvard University, Cambridge, United States. Her major research interest
includes Bioengineering, Biomedical Engineering, Medical Engineering,
Robotics and Computational Neuroscience. She is one of the renowned
scientists in the field of Biomedical Engineering and Bioengineering.
Yang Yung is currently working as a Professor and Research Chair in
Biomedical Engineering Research Centre (BMERC), Nanyang Technological
University, Singapore. He has 20 plus years of experience in research and
development. He completed his Bachelor‟s degree in Biomedical Engineering
from the University of Malaya, Malaysia in 1989. He obtained his Master‟s in
Biomedical Engineering from Monash University Australia in 1996. He
attained his first Doctorate from Monash University Malaysia in Biomedical
Engineering in 2006. He received his second Doctorate from the University of
Malaya, Malaysia in Medical Image Processing in 2011.
Mingmin Pan is currently working as Associate Professor in Biomedical
Engineering Research Centre (BMERC), Nanyang Technological University,
Singapore. She received her Bachelor‟s degree in Biomedical Engineering from
the University of Malaya, Malaysia in 2003. She obtained her Master‟s in
Biomedical Engineering from University of Malaya, Malaysia in 2007. In
2015, she achieved her Doctorate from University of Malaya, Malaysia in
Biomedical Engineering. She has more than 200 publications in conferences
and journals. Six of her conference papers have been awarded as the best
papers in various conferences.
Wenli Hu is Professor in Biomedical Engineering Research Centre, Nanyang
Technological University, Singapore. He has 15 plus years of experience in
academics and research. He received his doctoral degree from Beijing
University, China in 2012. He is associated with Nanyang Technological
University since 2012 in different cadre. He has published more than 100
publications in reputed journals and conferences. His foremost research
interest includes Biomedical Engineering, Medical Engineering, Neuroscience
and Ophthalmology. He received the young scientist award from Perth
University, Australia for his impact in the field of medicine.
3. Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks
1 Introduction
Deep learning algorithms are general non-linear models which can gain specifically from
data, settling on them an astounding decision for such robotic automation applications.
Notwithstanding, care must be taken to outline deep learning techniques and supporting
frameworks fitting for the job that needs to be done (P.S.Jagadeesh Kumar, 2018). Deep
learning is the exploration of preparing expansive artificial neural networks. Deep neural
networks can have countless parameters, permitting them to show complex capacities, for
example, nonlinear elements. They shape smaller portrayals of state from crude, high-
dimensional, multimodal sensor information regularly found in robotic applications, and
dissimilar to numerous machine learning techniques, they don't require a human master to
hand-build highlight vectors from sensor information at design time. Deep neural
networks can, be that as it may, exhibit specific difficulties in physical robotic
frameworks, where producing and preparing information is by and large costly, and
imperfect execution in preparing represents a threat in a few applications (A. Prieto et al.,
2016). Nevertheless, in spite of several difficulties, roboticists are finding innovative
choices, for example, utilizing and preparing information by means of advanced control,
and utilizing several deep neural networks to enhance execution and decrease training
time. Applying deep learning, figures out how to apply automation (N. B. Anders
Sandberg, 2008). All the more as of late, deep learning approaches have demonstrated
noteworthy execution over an extensive variety of areas, including computer vision,
audio and speech processing, natural language processing, and others. These calculations
depend on neural systems, exceptionally parameterized models which utilize numerous
layers of portrayal to change information into an errand particular portrayal. By utilizing
unsupervised elements, learning calculations, deep learning approaches can pre-introduce
these systems with helpful highlights, staying away from the over fitting issues regularly
observed when neural systems are prepared without this instatement (D. Ravi et al.,
2017). These properties settle on deep networks systems a superb decision for robotic
applications.
2 Computational Neuroscience
Computational neuroscience is particular from mental connectionism and hypotheses of
gaining from controls, for example, machine learning, neural systems and factual learning
hypothesis in that it underlines portrayals of useful and naturally sensible neurons and
their physiology and elements (S. J. Russell and P. Norvig., 2009). These models catch
the fundamental highlights of the organic framework at voluminous spatial-worldly
scales, from layer streams, substance connecting to arrange motions, learning and
memory. These computational models are utilized to test theories that can be specifically
checked by present or future natural inquiries. Computational neuroscience is unique
from psychological connectionism and machine learning in that it accentuates portrayals
of practical and naturally sensible neurons and neural frameworks alongside their
physiology and elements (Goodman, D., and Brette, R., 2008). These models catch the
vital highlights of the natural framework at various spatial-worldly scales, from the film
streams, concoction link to organize motions, columnar and topographic causing,
learning and memory. These computational models are utilized to outline speculations in
4. PSJ Kumar et al. IJIMR 1(2) 180-191
psychological tests. The human mind might be the most intricate and exceptional organ in
presence, containing more than one hundred billion nerve cells (Gamez, D et al., 2016).
Each and every neuron has the handling intensity of a mini computer, accepting
contribution from a huge number of different neurons and computing which data to pass
on. Computational neuroscience allows researchers to make better inquiries and to
achieve advance bits of knowledge into the workings of the human mind. Its decisive
objective is the disclosure of the crucial hypothesis of mind, to discover the essential laws
which oversee the activities of neurons, neural systems and the whole brain
(P.S.Jagadeesh Kumar, Yanmin Yuan and Yang Yung, 2018). Revealing these laws will
empower to repair injured brains, to create new artificial intelligence, and at last will give
an intriguing window into how the activity of neuronal systems offers ascend to
cognizant, believing being.
3 Human Brain Behavior
A neuron is a nerve cell that is the fundamental building ingredients of the sensory
system. Neurons are like different cells in the human body in various routes, yet there is
one key distinction amongst neurons and different cells. Neurons are specific to transmit
data all through the body. There are three essential parts of a neuron: the dendrites, the
cell body, and the axon. Be that as it may, all neurons fluctuate to some degree in size,
shape, and qualities relying upon the capacity and part of the neuron. A few neurons have
couple of dendritic branches, while others are profoundly fanned with a specific end goal
to get a lot of data (R. C. O'Reilly, Munakata, 2000). A few neurons have short axons,
while others can be very long. With the end goal for neurons to impart, they have to
transmit data both inside the neuron and starting with one neuron then onto the next. This
procedure uses both electrical flags and in addition synthetic dispatchers. The dendrites
of neurons get data from tactile receptors or different neurons. This data is then passed
down to the cell body and on to the axon. Once the data has landed at the axon, it goes
down the length of the axon as an electrical flag known as an activity potential (Dario
Floreano, Auke J Ijspeert, and Stefan Schaal, 2014). Once an electrical drive has
achieved the finish of an axon, the data must be transmitted over the synaptic hole to the
dendrites of the abutting neuron. Now and again, the electrical flag can quickly overcome
any issues between the neurons and proceed with its technique. In different cases,
neurotransmitters are expected to send the data starting with one neuron then onto the
next. Neurotransmitters are compound emissaries that are discharged from the axon
terminals to cross the synaptic hole and achieve the receptor destinations of different
neurons (P.S.Jagadeesh Kumar, J.Lepika, J.Tisa, J.Nedumaan, 2017). In a process known
as reuptake, these neurotransmitters join to the receptor site and are reabsorbed by the
neuron to be reused.
Neurons make up just a little piece of the human body's mind boggling correspondence
outline. The sensory system is made out of two principle parts the central nervous system
and the peripheral nervous system. Also, the endocrine framework assumes an imperative
part in correspondence (Y. LeCun, Y. Bengio, and G. Hinton, 2015). The human
cerebrum isn't just a single of the most vital organs in the human body; it is moreover the
most intricate. The cerebral cortex is the piece of the mind that capacities to make
individuals one of a kind. Particularly human attributes together with higher idea, dialect
5. Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks
and human awareness and in addition the capacity to think, reason and envision all
instigates in the cerebral cortex. Cerebellum authorizes control to stance, adjust, and the
coordination of intentional developments (E. Horvitz., 2014). This permits distinctive
muscle bunches in the body to act together and deliver composed, smooth motion.
Nevertheless, assuming a central part in motor control, the cerebellum is moreover vital
in certain psychological capacities including discourse. Situated over the brainstem, the
thalamus customs and transmits progress and tangible data. It is fundamentally a hand-off
station, taking in tangible data and afterward transmitting it on to the cerebral cortex. The
cerebral cortex likewise sends information to the thalamus, which at that point sends this
information to different frameworks (M. Wooldridge and N. R. Jennings, 1995). The
hypothalamus associates with numerous different areas of the mind and is in charge of
controlling enthusiasm, thirst, feelings, body temperature, and circadian rhythms. The
hypothalamus also controls the pituitary organ by discharging hormones, which gives the
hypothalamus a lot of control over many body capacities. The hippocampus is imperative
in memory and learning, while the limbic framework itself is principal in the control of
passionate reactions.
4 Mathematical Modeling of Human Brain
This segment deliberates about the mathematical modeling of human brain along with its
capability to predict behavior and the associated neural activities in the next segment.
Developing these sorts of models is troublesome in light of the fact that the model needs
to anticipate every single conceivable choice at any given point all the while, and
calculations must be proficient in a biologically conceivable way. Be that as it may, it's a
vital piece of making sense of how the brain functions, since the capacity to settle on
choices is such a center ability of human brain (Sean Sanders, Jackie Oberst, 2016). The
model displays how a system of neurons, when related absolutely, identifies the best
choice in any given situation, and the future combined reward. Essentially, the model
exhibits how neural connections can adjust and reshape in accordance to the social
conditions. Mathematical modeling of the cerebral physiology needs to adapt to two
noteworthy issues, to be particular, the unpredictability of the framework itself and the
check of the displaying comes almost by appropriate estimations. The intracranial space
can be regarded as a shut framework, restricted to the about inflexible skull (Dario
Floreano, Auke J Ijspeert, and Stefan Schaal., 2014). This property intensifies expound
estimations, predominantly if long haul perceptions are prerequisite. As a beginning
stage, the preservation of mass is utilized including that a difference in mass ∂m
represents an adjustment in thickness ∂ρ though the adjustment in volume ∂v of a
compartment:
∑
Now, the related physiological presumptions were made: blood and cerebrospinal fluid
are not compressible medium; on the other hand, the brain tissue is compressible
medium. All motions, as the blood stream, can be understood to be laminar. The flux
amid the two compartments a and b:
6. PSJ Kumar et al. IJIMR 1(2) 180-191
The volume v change of a section can be demonstrated by doling out defiance D(∅) to
compartments encompassed by a versatile layer:
∫ ∅ ∅
Cranial bone of the human head goes about as a shut compartment; the joint volume
change of all compartments must be zero:
∑
For displaying the obstruction of all veins ,
In this system, the cerebral autoregulation be displayed by methods for the subordinate as
for time of the defiance :
whereas and ( ) the defiance is given by:
whereas is the cerebral blood stream and portrays cerebral blood
stream required for the run of the tissue metabolism. The level of autoregulation is given
by δ and μ is the time steady of the direction. The term itself has the additional
structure;
( )
Along these lines speaks to the greatest pick up for the autoregulation and is
the insubordination of the standard cerebral blood stream. As the autoregulation bend
isn't symmetrical, accept distinctive wherewithal for widening (X < 0) and
narrowing (X > 0):
{
The brain tissue is covered by the supposed cerebrospinal liquid. This liquid is created at
a capillary level and for the most part consumed at the sagittal sinus. The main thrusts of
creation and retention are the weight contrasts amongst capillaries and the cerebrospinal
7. Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks
liquid compartment, and sandwiched between the cerebrospinal liquid compartment
and the sagittal sinus , independently:
The sagittal sinus is a solid vessel and accordingly does not crumple if there should arise
an occurrence of a negative weight angle amongst outside and inside. Also, it ought to be
said, that an inversion of both motions isn't conceivable. The lacking compliances
and ( ) are depicted by:
√
( )
(| |)
whereas , are constants. Joining the heretofore mentioned transitions and
resistance over the mass security prompts to a plan of differential conditions. This model
could reliably recreate the essential trial perceptions like the autoregulation bend and the
weight volume bend.
5 Simulating Brain Model with Deep Learning
Neurons connect with each other in deep neural systems to realize, reason, recall, and
react out. Each activity, from the straightforward demonstration to the more
unpredictable process, includes a large number of thousands of neurons in innumerable
deep neural systems (Falotico et al., 2017). The primary test in planning an ongoing
model-prescient controller for human mind conduct lies in enabling expectation and
advancement to run steadily to guarantee optimality of controls, while giving the model
the latest state data and performing control at the required continuous rate. These trials
are exclusively shared memory space amid process correspondences. Given a proper
model, this method is particular and adaptable and the streamlining procedure is bland to
the robot included, while the control procedure is robot-particular, however nonspecific
to the job that needs to be done. Indeed, models for the streamlining procedure don't need
to be adapted locally. Presently, deep learning techniques embrace the upsides of neural
systems, while using new calculations and system models to defeat their downsides
(Ambrosano et al., 2016). Because of their adequacy as general non-direct learners, deep
learning has been connected to a wide range of issues, including visual acknowledgment,
normal dialect preparing, acoustic demonstrating, and many others.
A deep network is a progressive model where each layer applies a linear transformation
trailed by a non-linearity to the previous layer. Each such task, casually called a layer,
comprises of a direct change, say, a convolution of its info, trailed by a point insightful
nonlinear enactment work, e.g., a sigmoid (G. E. Hinton, S. Osindero, and Y.-W. Teh.,
8. PSJ Kumar et al. IJIMR 1(2) 180-191
2006). The major property of deep networks that is accepted to be the base of their
execution is that they have an extensive number of layers as identified with traditional
artificial neural networks (P.S.Jagadeesh Kumar, Yang Yung, Mingmin Pan, Wenli Hu,
2018). Specifically, there are three key factors in familiarizing the deep networks, in
particular the designs, regularization procedures and optimization ways, which are basic
to prepare well performing deep networks and understanding their need and exchange to
disentangle the mysteries of their prosperity. The thought of geometric security gives a
conceivable structure to realize its prosperity (Gewaltig, M., and Diesmann, 2007). In
supervised learning, an indefinite function f: A2
(Ω) → Z is observed on a training set;
{ }
Where the objective space Z can be thought as being discrete in a standard order of mind
conduct exercises setup with C = |Z| being the quantity of classes, or Z = RC
in a relapse
errand. Likewise, a disfigurement Zτ, where τ: Ω → Ω is a smooth vector field, follows
up on Z2
(Ω) as Zτ X (u) = X (u - τ (u)). Disfigurements countenance display nearby
interpretations, vicissitudes in perspective, revolutions and recurrence transpositions
(I. Lighthill. (1973). Furthermost extreme undertakings examined in robotics are
interpretation invariant, as well as, more crucially, additionally stable as for
disfigurements as;
| | ‖ ‖
For all X; τ, where ‖ ‖ trials the constancy of a given twisting field. At the end, the
amount to be anticipated does not change much if the input is somewhat distorted. In
undertakings that are interpretation equivariant;
| | ‖ ‖
This property is greatly more grounded than stationarity; since the space of nearby
distortions has high dimensionality of the request of RD
. Without a doubt regularization
and optimization are both utilized with help of convolutional neural networks
(A. Krizhevsky, I. Sutskever, and G. E. Hinton., 2012). In conditions of supervised, semi-
regulated and unsupervised learning, the convolutional deep belief networks (CDBN) can
be the best decision as on account of deep robotics. The convolutional deep belief
networks are worked by constructing a few convolutional and deep belief layers, in
propelling a bland various leveled portrayal;
∅ ( )
Where v = {v(1)
,…., v(K)
} is the hyper-vector of the system parameters. The yield
highlights appreciate interpretation invariance relying upon whether the spatial
determination is steadily lost by methods for deep belief pooling or kept settled.
Furthermore, on the off chance that one governs the convolutional tensors to be
cognizance boggling wavelet disintegration and utilizes complex modulus as point-wise
nonlinearities, one can provably acquire security to nearby distortions. In spite of the
circumstance that this strength isn't thoroughly demonstrated for non-specific minimally
bolstered convolutional tensors, it supports the observational triumph of CDBN models
over an assortment of computer vision applications. Brain simulations mimic the
electrical signals which are transmitted by neurons and the measure of the associations
between neurons (A. Prieto et al., 2016). The cerebrum reproduction begins with arbitrary
9. Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks
signs and the entire framework settles as indicated by rules which are understood to
supervise data preparing ventures in the mind. Subsequent to running these standards for
quite a while, stable signs may frame which can be contrasted with the signs of the mind
(T. R. Society, 2017). On the off chance that the signs of the regeneration are like
chronicles of the cerebrum, this builds our certainty that our picked rules are to some
degree like the standards that the mind works. In this way, substantial scale data handling
rules in the mind can be appropriate (E. Broadbent et al., 2009). The mind utilizes
learning algorithms which are altogether different from deep network adapting;
nevertheless, the design of neurons is like convolutional systems. In the proposed work,
convolutional deep belief networks were executed in recreating the human mind for
robotics. The flowchart of proposed human brain simulation is shown in Figure 1.
6 Results and Analysis
In brain simulation, the dissemination of electrochemical particles is demonstrated by
differential equations. These differential equations are unpredictable, yet can be displayed
with basic strategies to estimate these perplexing conditions. The outcome has poor
exactness however the calculation is computationally productive and the precision is
inadequate to duplicate the exercises of genuine neurons alongside their size and
circulation of neurotransmitters. To increase the prediction accuracy of diseases, robots
are command-able. The proposed mathematical model is simulated with the human brain
using “Matlab Simulink” environment. Yet again, the competence and proficiency of the
simulation be contingent upon a concrete mathematical model and the specification of the
hardware configuration. At this time, the simulations are carried out at higher-end
machines. The simulation is accomplished with different methods and techniques for
Alzheimer‟s disease and Parkinson's sickness and their prediction accuracy are
exemplified in Table 1. A dataset of 100 patients with Alzheimer‟s disease and
Parkinson's sickness each were accounted. The result clearly predicts the deep learning
method surpasses the artificial neural networks and machine learning. Unquestionably,
convolutional deep belief networks based deep robotics provided better prediction
accuracy.
The point to detract from the convolutional deep belief networks is that they are not
allocated with complexity for the creation of memories. Thusly, the multifaceted nature
of the cerebrum is thought little of, on the grounds that it is truly hard to know how the
mind learns. At this juncture, any accurate estimates for the computational complexity as
such are impossible. But, from the related execution time, it can be learned that deep
learning methods are more complex and consumes enough time and space. Deep robotics
to be functionally capable and commanding, higher-end machines like super computers is
extremely advisable.
7 Conclusion
Steadfastly, a huge number of individuals are influenced by disarranges of the cerebrum
and central nervous system including Alzheimer's disease, Parkinson's sickness, stroke
and horrendous brain injuries. These diseases and injuries feature the significance of the
10. PSJ Kumar et al. IJIMR 1(2) 180-191
biological bases for human brain behavior. In-order to avoid hand-engineered complex
mathematical handlings and clinical observations related to neuroscience, deep robots
play a high-end solicitation in this concern. The anticipated application demonstrate that
by identifying nonlinear models, which are hard and complex, convolution deep belief
networks enables robotic systems to learn and to solve an extensive range of neurotic
problems. Deep robots were intelligent to advance prediction accuracy of human brain
behavior in both linear and non-linear models, giving more accurate results. The
simulation results indicate that the prediction accuracy was reckoned in deep learning
based convolution deep belief networks compared to machine learning based convolution
neural networks. In-spite of the several advantages of proposed deep robotics, they do
suffer from many drawbacks as well. Few of those drawbacks of the proposed work are
listed;
1) Simulating the mathematical model of human brain behavior is too complex with deep
learning technique due to their incapability of self-learning.
2) The accurate mathematical modeling of human brain remains uncertain and unreliable.
3) Processing the unique information of every neuron‟s communication remains special
irrespective of the learning algorithms.
The good news is that with more reliable deep learning techniques and concrete
mathematical model of the human brain, the computational neuroscience relating human
brain behaviors can be very well addressed in the future by incorporating the self-
learning capabilities of such deep robotics. Though deep learning techniques provide
better calibration in supervised and unsupervised behaviors of human brain simulation,
the authors‟ interest relies in training the deep learning techniques with self-learning
attributes to better simulate the human brain behaviors in the future days to come.
Acknowledgement
We sincerely thank and express our heartfelt appreciation to Dr.Xianpei Li, Institute for
Computational and Mathematical Engineering, Stanford University, California, and
United States in providing the necessary assistance in simulating the work.
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Figure 1 Flowchart of the Proposed Method of Robotic Human Brain Simulation for
Estimating Alzheimer‟s and Parkinson‟s disease
13. Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks
Table 1 Comparison of Prediction Accuracy
S.No Technique Method Disease
Prediction
Accuracy
1.
Artificial
Neural
Networks
Neural Back-
propagation
Networks
Alzheimer's
disease
61.87%
Parkinson's
sickness
63.34%
2.
Machine
Learning
Convolutional
Neural Networks
Alzheimer's
disease
72.96%
Parkinson's
sickness
69.23%
3.
Deep
Learning
Convolutional
Deep Belief
Networks (CDBN)
Alzheimer's
disease
85.65%
Parkinson's
sickness
81.49%