Search results for: deep brain stimulation (DBS)

Authors: Hailyie Tekleselase

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Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

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Authors: Irina Deputat, Anatoly Gribanov, Yuliya Dzhos, Alexandra Nekhoroshkova, Tatyana Yemelianova, Irina Bolshevidtseva, Irina Deryabina, Yana Kereush, Larisa Startseva, Tatyana Bagretsova, Irina Ikonnikova

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In the modern world the number of elderly people increases. Preservation of functionality of an organism in the elderly becomes very important now. During aging the higher cortical functions such as feelings, perception, attention, memory, and ideation are gradual decrease. It is expressed in the rate of information processing reduction, volume of random access memory loss, ability to training and storing of new information decrease. Perspective directions in studying of aging neurophysiological parameters are brain imaging: computer electroencephalography, neuroenergy mapping of a brain, and also methods of studying of a neurodynamic brain processes. Research aim – to study features of a brain aging in elderly people by electroencephalogram (EEG) and the DC-potential level. We examined 130 people aged 55 - 74 years that did not have psychiatric disorders and chronic states in a decompensation stage. EEG was recorded with a 128-channel GES-300 system (USA). EEG recordings are collected while the participant sits at rest with their eyes closed for 3 minutes. For a quantitative assessment of EEG we used the spectral analysis. The range was analyzed on delta (0,5–3,5 Hz), a theta - (3,5–7,0 Hz), an alpha 1-(7,0–11,0 Hz) an alpha 2-(11–13,0 Hz), beta1-(13–16,5 Hz) and beta2-(16,5–20 Hz) ranges. In each frequency range spectral power was estimated. The 12-channel hardware-software diagnostic ‘Neuroenergometr-KM’ complex was applied for registration, processing and the analysis of a brain constant potentials level. The DC-potential level registered in monopolar leads. It is revealed that the EEG of elderly people differ in higher rates of spectral power in the range delta (р < 0,01) and a theta - (р < 0,05) rhythms, especially in frontal areas in aging. By results of the comparative analysis it is noted that elderly people 60-64 aged differ in higher values of spectral power alfa-2 range in the left frontal and central areas (р < 0,05) and also higher values beta-1 range in frontal and parieto-occipital areas (р < 0,05). Study of a brain constant potential level distribution revealed increase of total energy consumption on the main areas of a brain. In frontal leads we registered the lowest values of constant potential level. Perhaps it indicates decrease in an energy metabolism in this area and difficulties of executive functions. The comparative analysis of a potential difference on the main assignments testifies to unevenness of a lateralization of a brain functions at elderly people. The results of a potential difference between right and left hemispheres testify to prevalence of the left hemisphere activity. Thus, higher rates of functional activity of a cerebral cortex are peculiar to people of early advanced age (60-64 years) that points to higher reserve opportunities of central nervous system. By 70 years there are age changes of a cerebral power exchange and level of electrogenesis of a brain which reflect deterioration of a condition of homeostatic mechanisms of self-control and the program of processing of the perceptual data current flow.

Keywords: brain, DC-potential level, EEG, elderly people

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Authors: Edward Holupka, John Rossman, Tye Morancy, Joseph Aronovitz, Irving Kaplan

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A common modality for the treatment of early stage prostate cancer is the implantation of radioactive seeds directly into the prostate. The radioactive seeds are positioned inside the prostate to achieve optimal radiation dose coverage to the prostate. These radioactive seeds are positioned inside the prostate using Transrectal ultrasound imaging. Once all of the planned seeds have been implanted, two dimensional transaxial transrectal ultrasound images separated by 2 mm are obtained through out the prostate, beginning at the base of the prostate up to and including the apex. A common deep neural network, called DetectNet was trained to automatically determine the position of the implanted radioactive seeds within the prostate under ultrasound imaging. The results of the training using 950 training ultrasound images and 90 validation ultrasound images. The commonly used metrics for successful training were used to evaluate the efficacy and accuracy of the trained deep neural network and resulted in an loss_bbox (train) = 0.00, loss_coverage (train) = 1.89e-8, loss_bbox (validation) = 11.84, loss_coverage (validation) = 9.70, mAP (validation) = 66.87%, precision (validation) = 81.07%, and a recall (validation) = 82.29%, where train and validation refers to the training image set and validation refers to the validation training set. On the hardware platform used, the training expended 12.8 seconds per epoch. The network was trained for over 10,000 epochs. In addition, the seed locations as determined by the Deep Neural Network were compared to the seed locations as determined by a commercial software based on a one to three months after implant CT. The Deep Learning approach was within \strikeout off\uuline off\uwave off2.29\uuline default\uwave default mm of the seed locations determined by the commercial software. The Deep Learning approach to the determination of radioactive seed locations is robust, accurate, and fast and well within spatial agreement with the gold standard of CT determined seed coordinates.

Keywords: prostate, deep neural network, seed implant, ultrasound

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Authors: Abdelkrim Belhaoua, Jean-Pierre Radoux, Mariana Kuras, Vincent Récamier, Martial Piotto, Karim Elbayed, François Proust, Izzie Namer

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This paper proposes an augmented reality system dedicated to neurosurgery in order to assist the surgeon during an operation. This work is part of the ExtempoRMN project (Funded by Bpifrance) which aims at analyzing during a surgical operation the metabolic content of tumoral brain biopsy specimens by HRMAS NMR. Patients affected with a brain tumor (gliomas) frequently need to undergo an operation in order to remove the tumoral mass. During the operation, the neurosurgeon removes biopsy specimens using image-guided surgery. The biopsy specimens removed are then sent for HRMAS NMR analysis in order to obtain a better diagnosis and prognosis. Image-guided refers to the use of MRI images and a computer to precisely locate and target a lesion (abnormal tissue) within the brain. This is performed using preoperative MRI images and the BrainLab neuro-navigation system. With the patient MRI images loaded on the Brainlab Cranial neuro-navigation system in the operating theater, surgeons can better identify their approach before making an incision. The Brainlab neuro-navigation tool tracks in real time the position of the instruments and displays their position on the patient MRI data. The results of the biopsy analysis by 1H HRMAS NMR are then sent back to the operating theater and superimposed on the 3D localization system directly on the MRI images. The method we have developed to communicate between the HRMAS NMR analysis software and Brainlab makes use of a combination of C++, VTK and the Insight Toolkit using OpenIGTLink protocol.

Keywords: neuro-navigation, augmented reality, biopsy, BrainLab, HR-MAS NMR

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Authors: Sophio Kalmakhelidze, Eka Shekiladze, Tamar Sanikidze, Mikheil Gogebashvili, Nazi Ivanishvili

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Radiation-induced various degrees of brain injury and cognitive impairment have been described after cranial radiotherapy of brain tumors. High doses of ionizing radiation have a severe impact on the central nervous system, resulting in morphological and behavioral impairments. Structures of the limbic system are especially sensitive to radiation exposure. Hence, compounds or drugs that can reduce radiation-induced impairments can be used as promising antioxidants or radioprotectors. In our study Mice whole-body irradiation with 137Cs was performed at a dose rate of 1,1 Gy/min for a total dose of 5 Gy with a “Gamma-capsule-2”. Irradiated mice were treated with Herniarin (20 mg/kg) for five days before irradiation and the same dose was administrated after one hour of irradiation. The immediate and delayed effects of ionizing radiation, as well as, protective effect of Herniarin was evaluated during early and late post-irradiation periods. The results reveal that ionizing radiation (5 Gy) alters the structure of the hippocampus in adult mice during the late post-irradiation period resulting in the decline of memory formation and learning process. Furthermore, Simple Coumarin-Herniarin reveals a radiosensitizing effect reducing morphological and behavioral alterations.

Keywords: ionizing radiation, cognitive impairments, hippocampus, limbic system, Herniarin

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: baby care system, Internet of Things, deep learning, machine vision

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Authors: A. M. Gadanya, M. S. Sule

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Effect of oral administration of Gadagi tea on some antioxidant enzymes was assessed in healthy male albino rats. The rats were grouped and administered with standard doses of the 3 types of Gadagi tea i.e. Sak, Sada and Magani for a period of four weeks. Animals that were not administered with the tea constituted the control group. At the end of fourth week, the animals were sacrificed and their serum superoxide dismutase (SOD), glutathione reductase (GR) and catalase (CAT) activities were determined. The activities of the enzymes were also determined in the brain, liver, kidney and intestine homogenates of the rats. Mean SOD activity in brain of rats orally administered with “sada” was found to be significantly higher (P < 0.05) than that of the control group. Mean CAT activity in the intestine of rats orally administered with “magani” was found to be significantly higher (P < 0.05) than that of the control group and the experimental groups of Sak and Sada at standard dose level. Thus, all the “Gadagi” tea preparations studied at standard dose level could stimulate antioxidant enzymes, especially SOD in brain and CAT in intestine (by Sada) and CAT in intestine (by Magani).

Keywords: “Gadagi” tea, superoxide dismutase, catalase, glutathione reductase

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Authors: Raj K. Gupta, X. Gary Tan, Andrzej Przekwas

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Bomb blasts from Improvised Explosive Devices (IEDs) account for vast majority of terrorist attacks worldwide. Injuries caused by IEDs result from a combination of the primary blast wave, penetrating fragments, and human body accelerations and impacts. This paper presents a multiscale computational model of coupled blast physics, whole human body biodynamics and injury biomechanics of sensitive organs. The disparity of the involved space- and time-scales is used to conduct sequential modeling of an IED explosion event, CFD simulation of blast loads on the human body and FEM modeling of body biodynamics and injury biomechanics. The paper presents simulation results for blast-induced brain injury coupling macro-scale brain biomechanics and micro-scale response of sensitive neuro-axonal structures. Validation results on animal models and physical surrogates are discussed. Results of our model can be used to 'replicate' filed blast loadings in laboratory controlled experiments using animal models and in vitro neuro-cultures.

Keywords: blast waves, improvised explosive devices, injury biomechanics, mathematical models, traumatic brain injury

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Authors: Igor Faulmann, Arnaud Saj, Roland Maurer

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Spatial cognition consists in a plethora of high level cognitive abilities: among them, the ability to learn and to navigate in large scale environments is probably one of the most complex skills. Navigation is thought to rely on the ability to read a cognitive map, defined as an allocentric representation of ones environment. Those representations are of course intimately related to the two geometrical primitives of the environment: distance and direction. Also, many recent studies point to a predominant hippocampal and para-hippocampal role in spatial cognition, as well as in the more specific cluster of navigational skills. In a previous study in humans, we used a newly validated test assessing cognitive map processing by evaluating the ability to judge relative distances and directions: the CMRT (Cognitive Map Recall Test). This study identified in topographically disorientated patients (1) behavioral differences between the evaluation of distances and of directions, and (2) distinct causality patterns assessed via VLSM (i.e., distinct cerebral lesions cause distinct response patterns depending on the modality (distance vs direction questions). Thus, we hypothesized that: (1) if the CMRT really taps into the same resources as real navigation, there would be hippocampal, parahippocampal, and parietal activation, and (2) there exists underlying neuroanatomical and functional differences between the processing of this two modalities. Aiming toward a better understanding of the neuroanatomical correlates of the CMRT in humans, and more generally toward a better understanding of how the brain processes the cognitive map, we adapted the CMRT as an fMRI procedure. 23 healthy subjects (11 women, 12 men), all living in Geneva for at least 2 years, underwent the CMRT in fMRI. Results show, for distance and direction taken together, than the most active brain regions are the parietal, frontal and cerebellar parts. Additionally, and as expected, patterns of brain activation differ when comparing the two modalities. Furthermore, distance processing seems to rely more on parietal regions (compared to other brain regions in the same modality and also to direction). It is interesting to notice that no significant activity was observed in the hippocampal or parahippocampal areas. Direction processing seems to tap more into frontal and cerebellar brain regions (compared to other brain regions in the same modality and also to distance). Significant hippocampal and parahippocampal activity has been shown only in this modality. This results demonstrated a complex interaction of structures which are compatible with response patterns observed in other navigational tasks, thus showing that the CMRT taps at least partially into the same brain resources as real navigation. Additionally, differences between the processing of distances and directions leads to the conclusion that the human brain processes each modality distinctly. Further research should focus on the dynamics of this processing, allowing a clearer understanding between the two sub-processes.

Keywords: cognitive map, navigation, fMRI, spatial cognition

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Authors: Ian Omung'a

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Breast cancer remains to be one of the deadliest cancers for women worldwide, with the risk of developing tumors being as high as 50 percent in Sub-Saharan African countries like Kenya. With as many as 42 percent of these cases set to be diagnosed late when cancer has metastasized and or the prognosis has become terminal, Full Field Digital [FFD] Mammography remains an effective screening technique that leads to early detection where in most cases, successful interventions can be made to control or eliminate the tumors altogether. FFD Mammograms have been proven to multiply more effective when used together with Computer-Aided Detection and Diagnosis [CADe] systems, relying on algorithmic implementations of Deep Learning techniques in Computer Vision to carry out deep pattern recognition that is comparable to the level of a human radiologist and decipher whether specific areas of interest in the mammogram scan image portray abnormalities if any and whether these abnormalities are indicative of a benign or malignant tumor. Within this paper, we review emergent Deep Learning techniques that will prove relevant to the development of State-of-The-Art FFD Mammogram CADe systems. These techniques will span self-supervised learning for context-encoded occlusion, self-supervised learning for pre-processing and labeling automation, as well as the creation of a standardized large-scale mammography dataset as a benchmark for CADe systems' evaluation. Finally, comparisons are drawn between existing practices that pre-date these techniques and how the development of CADe systems that incorporate them will be different.

Keywords: breast cancer diagnosis, computer aided detection and diagnosis, deep learning, whole mammogram classfication, ultrasound classification, computer vision

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Authors: A. V. Sokolov, S. V. Vorobyev, A. Yu. Efimtcev, V. Yu. Lobzin, I. A. Lupanov, O. A. Cherdakov, V. A. Fokin

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Alzheimer’s disease (AD) is the most common form of dementia. Different brain regions are involved to the pathological process of AD. The purpose of this study was to evaluate brain activation by visual memory task in patients with Alzheimer's disease and determine correlation between memory impairment and atrophy of memory specific brain regions of frontal and medial temporal lobes. To investigate the organization of memory and localize cortical areas activated by visual memory task we used functional magnetic resonance imaging and to evaluate brain atrophy of patients with Alzheimer's disease we used voxel-based morphometry. FMRI was performed on 1.5 T MR-scanner Siemens Magnetom Symphony with BOLD (Blood Oxygenation Level Dependent) technique, based on distinctions of magnetic properties of hemoglobin. For test stimuli we used series of 12 not related images for "Baseline" and 12 images with 6 presented before for "Active". Stimuli were presented 3 times with reduction of repeated images to 4 and 2. Patients with Alzheimer's disease showed less activation in hippocampal formation (HF) region and parahippocampal gyrus then healthy persons of control group (p<0.05). The study also showed reduced activation in posterior cingulate cortex (p<0.001). Voxel-based morphometry showed significant atrophy of grey matter in Alzheimer’s disease patients, especially of both temporal lobes (fusiform and parahippocampal gyri); frontal lobes (posterior cingulate and superior frontal gyri). The study showed correlation between memory impairment and atrophy of memory specific brain regions of frontal and medial temporal lobes. Thus, reduced activation in hippocampal formation and parahippocampal gyri, in posterior cingulate gyrus in patients with Alzheimer's disease correlates to significant atrophy of these regions, detected by voxel-based morphometry, and to deterioration of specific cognitive functions.

Keywords: Alzheimer’s disease, functional MRI, voxel-based morphometry

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Authors: Reva Sharan Thakur, Mrinalini Tiwari, Jyoti das

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Cerebral malaria-associated over expression of pro-inflammatory cytokines and chemokines ultimately results in the up-regulation of adhesion molecules in the brain endothelium leading to sequestration of mature parasitized RBCs in the brain. The high-parasitic load subsequently results in increased mortality or development of neurological symptoms within a week of infection. Studies in the human and experimental cerebral malaria have implicated the breakdown of the integrity of blood-brain barrier during the lethal course of infection, cerebral dysfunction, and fatal organ pathologies that result in multi-organ failure. In the present study, using Plasmodium berghei Anka as a mouse model and in vitro conditions, we have investigated the effect of MSCs to attenuate cerebral malaria pathogenesis by diminishing the effect of inflammation altered organ morphology, reduced parasitemia, and increased survival of the mice. MSCs are also validated for their role in preventing BBB dysfunction and reducing malarial toxins. It was observed that administration of MSCs significantly reduced parasitemia and increased survival in Pb A infected mice. It was further demonstrated that MSCs play a significant role in reversing neurological complexities associated with cerebral malaria. Infusion of MSCs in infected mice decreased hemozoin deposition; oedema, and haemorrhagic lesions in vascular organs. MSCs administration also preserved the integrity of the blood-brain barrier and reduced neural inflammation. Taken together, our results demonstrate the potential of MSCs as an emerging anti-malarial candidate.

Keywords: cerebral malaria, mesenchymal stem cells, erythropoesis, cell death

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Authors: Rahele Mesbah, Nic Van Der Wee, Manja Koenders, Erik Giltay, Albert Van Hemert, Max De Leeuw

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Introduction: Patients with bipolar disorder (BD), characterized by depressive and manic episodes, often suffer from cognitive dysfunction. An up-to-date meta-analysis of functional Magnetic Resonance Imaging (fMRI) studies examining cognitive function in BD is lacking. Objective: The aim of the current fMRI meta-analysis is to investigate brain functioning of bipolar patients compared with healthy subjects within three domains of emotion processing, reward processing, and working memory. Method: Differences in brain regions activation were tested within whole-brain analysis using the activation likelihood estimation (ALE) method. Separate analyses were performed for each cognitive domain. Results: A total of 50 fMRI studies were included: 20 studies used an emotion processing (316 BD and 369 HC) task, 9 studies a reward processing task (215 BD and 213 HC), and 21 studies used a working memory task (503 BD and 445 HC). During emotion processing, BD patients hyperactivated parts of the left amygdala and hippocampus as compared to HC’s, but showed hypoactivation in the inferior frontal gyrus (IFG). Regarding reward processing, BD patients showed hyperactivation in part of the orbitofrontal cortex (OFC). During working memory, BD patients showed increased activity in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Conclusions: This meta-analysis revealed evidence for activity disturbances in several brain areas involved in the cognitive functioning of BD patients. Furthermore, most of the found regions are part of the so-called fronto-limbic network which is hypothesized to be affected as a result of BD candidate genes' expression.

Keywords: cognitive functioning, fMRI analysis, bipolar disorder, fronto-limbic network

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Authors: Cameron Wilson, Megan Hanrahan, Katie Brittain, Riona McArdle, Alison Keogh, Lynn Rochester

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Background: The loss of mobility and functional dependence is a significant marker in the progression of neurodegenerative diseases such as Parkinson’s Disease (PD). Pharmacological, surgical, and therapeutic treatments are available that can help in the management and amelioration of PD symptoms; however, these only prolong more severe symptoms. Accordingly, ensuring people with PD can maintain independence and a healthy wellbeing are essential in establishing an effective treatment option for those afflicted. Existing literature reviews have examined experiences in engaging with PD treatment options and the impact of PD on independence and wellbeing. Although, the literature fails to explore the influence of treatment options on independence and wellbeing and therefore misses what people value in their treatment. This review is the first that synthesises the impact of mobility-related treatments on independence and wellbeing in people with PD and their carers, offering recommendations to clinical practice and provides a conceptual framework (in development) for future research and practice. Objectives: To explore the impact of mobility-related treatment (both pharmacological and non-pharmacological) on the independence and wellbeing of people with PD and their carers. To propose a conceptual framework to patients, carers and clinicians which captures the qualities people with PD value as part of their treatment. Methods: We performed a critical interpretive synthesis of qualitative evidence, searching six databases for reports that explored the impact of mobility-related treatments (both drug and non-pharmacological) on independence and wellbeing in Parkinson’s Disease. The types of treatments included medication (Levodopa and Amantadine), dance classes, Deep-Brain Stimulation, aquatic therapies, physical rehabilitation, balance training and foetal transplantation. Data was extracted, and quality was assessed using an adapted version of the NICE Quality Appraisal Tool Appendix H before being synthesised according to the critical interpretive synthesis framework and meta-ethnography process. Results: From 2301 records, 28 were eligible. Experiences and impact of treatment pathway on independence and wellbeing was similar across all types of treatments and are described by five inter-related themes: (i) desire to maintain independence, (ii) treatment as a social experience during and after, (iii) medication to strengthen emotional health, (iv) recognising physical capacity and (v) emphasising the personal journey of Parkinson’s treatments. Conclusion: There is a complex and inter-related experience and effect of PD treatments common across all types of treatment. The proposed conceptual framework (in development) provides patients, carers, and clinicians recommendations to personalise the delivery of PD treatment, thereby potentially improving adherence and effectiveness. This work is vital to disseminate as PD treatment transitions from subjective and clinically captured assessments to a more personalised process supplemented using wearable technology.

Keywords: parkinson's disease, medication, treatment, dance, review, healthcare, delivery, levodopa, social, emotional, psychological, personalised healthcare

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Biniam Seifu Debelo, Bheema Lingaiah Thamineni, Hanumesh Kumar Dasari, Ahmed Ali Dawud

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Background: One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe neurological dysfunction. They may be caused by a broad range of problems with the central nervous system during or after pregnancy, infections, brain injuries, and/or other health conditions. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory (spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if clinical observation is the primary basis for identifying newborn seizures. Objectives: In this study, a diagnosis system using deep convolutional neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data. Methods: Clinical multichannel EEG datasets were compiled using datasets from publicly accessible online sources. Various preprocessing steps were taken, including converting 2D time series data to equivalent waveform pictures. The proposed models underwent training, and their performance was evaluated. Results: The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of 92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. A multiclassification model is used to classify the severity level of neonatal seizures. The results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare institutions. Conclusion: The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making tool in resource-limited areas with a scarcity of expert neurologists.

Keywords: CNN, multichannel EEG, neonatal seizure, severity identification

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Authors: H. Emari, Z. Emari

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The purpose of this paper is to design an applied framework for strategic thinking which can be applied in all managerial levels and all types of organizational environments. No special applied frame has been presented for this thinking. This paper presents a theoretical framework for the thinking type of a manager by making a historical research and studying the scientific documents about thinking of a strategist. In the new theoretical framework it has been tried to suggest the best type of thinking for a strategist after analyzing the environment of his decisions. So, in this framework, the traditional viewpoint about strategic thinking, which has considered it as a special type of right-brain thinking against other types of right-brain thinking and suggested it for a strategist, was put aside and suggests that the strategist should use a suitable type of thinking under different conditions.

Keywords: strategic thinking, systemic thinking, lateral thinking, intuitive thinking, hybrid thinking

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Authors: Nouha Bouayed Abdelmoula, Rim Louati, Nawel Abdellaoui, Balkiss Abdelmoula, Oldez Kaabi, Walid Smaoui, Samir Aloulou

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Background and Aims: Cardiofaciocutaneous syndrome (CFC) is an autosomal dominant disorder with the vast majority of cases arising by a new mutation of BRAF, MEK1, MEK2, or rarely, KRAS genes. Here, we report a rare Tunisian case of CFC syndrome for whom we identify SOS1 mutation. Methods: Genomic DNA was obtained from peripheral blood collected in an EDTA tube and extracted from leukocytes using the phenol/chloroform method according to standard protocols. High resolution melting (HRM) analysis for screening of mutations in the entire coding sequence of PTPN11 was conducted first. Then, HRM assays to look for hot spot mutations coding regions of the other genes of the RAS-MAPK pathway (RAt Sarcoma viral oncogene homolog Mitogen-Activated Protein Kinases Pathway): SOS1, SHOC2, KRAS, RAF1, KRAS, NRAS, CBL, BRAF, MEK1, MEK2, HRAS, and RIT1, were applied. Results: Heterozygous SOS1 point mutation clustered in exon 10, which encodes for the PH domain of SOS1, was identified: c.1655 G > A. The patient was a 9-year-old female born from a consanguineous couple. She exhibited pulmonic valvular stenosis as congenital heart disease. She had facial features and other malformations of Noonan syndrome, including macrocephaly, hypertelorism, ptosis, downslanting palpebral fissures, sparse eyebrows, a short and broad nose with upturned tip, low-set ears, high forehead commonly associated with bitemporal narrowing and prominent supraorbital ridges, short and/or webbed neck and short stature. However, the phenotype is also suggestive of CFC syndrome with the presence of more severe ectodermal abnormalities, including curly hair, keloid scars, hyperkeratotic skin, deep plantar creases, and delayed permanent dentition with agenesis of the right maxillary first molar. Moreover, the familial history of the patient revealed recurrent brain malignancies in the paternal family and epileptic disease in the maternal family. Conclusions: This case report of an overlapping RASopathy associated with SOS1 mutation and familial history of brain tumorigenesis is exceptional. The evidence suggests that RASopathies are truly cancer-prone syndromes, but the magnitude of the cancer risk and the types of cancer partially overlap.

Keywords: cardiofaciocutaneous syndrome, CFC, SOS1, brain cancer, germline mutation

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Authors: Natasha Yasmin Felles, Gerard Riley

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Background and Aims: Relationship breakdown is typical when one partner lives with an acquired brain injury caused by issues like a stroke. Research has found that the perception of relationship satisfaction decreases following such an injury among non-injured partners. Non-injured partners also are found to experience caregiver stress/burden as they immediately have to take the role of a caregiver along with being a partner of the injured. Research has also found that the perception of a continuous relationship, i.e. the perception of the relationship to be essentially the same as it was before the injury, also changes among those caregiving partners. However, there is a lack of available intervention strategies that can help those partners with both individual and relationship difficulties. The aim of this case study was to conduct a pilot test of an intervention aimed to explore whether it is possible to support a partner to experience greater continuity within the relationship poststroke, and what benefits such a change might have. Method: A couple, where one partner experienced an acquired brain injury poststroke were provided with Integrated Behavioural Couples Therapy for 3-months. The intervention addressed goals identified as necessary by the couple and by the formulation of their individual and relationship difficulties, alongside the goal of promoting relationship continuity. Before and after measures were taken using a battery of six questionnaires to evaluate changes in perceptions of continuity, stress, and other aspects of the relationship. Results: Both quantitative and qualitative data showed that relationship continuity was improved after the therapy, as were the measures of stress and other aspects of the relationship. The stress felt by the person with the acquired brain injury also showed some evidence of improvement. Conclusion: The study found that perceptions of relationship continuity can be improved by therapy and that improving these might have a beneficial impact on the stress felt by the carer, their satisfaction with the relationship and overall levels of conflict and closeness within the relationship. The study suggested the value of further research on enhancing perceptions of continuity in the relationship after an acquired brain injury. Currently, the findings of the study have been used to develop a pilot feasibility study to collect substantive evidence on the impact of the intervention on the couples and assess its feasibility and acceptability, which will help in further developing a specific generalized relationship continuity intervention, that will be beneficial in preventing relationship breakdown in the future.

Keywords: acquired brain injury, couples therapy, relationship continuity, stroke

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Bayan Almofty, Yuto Yamaki, Tadamasa Terai, Sadahito Uto

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Myopathy (muscles disease) treatment are expected in the field of regenerative medicine and applied research of cultured muscle to bio actuator is performed in Biomedical Engineering as applied research of cultured muscle. This study is about cultured myoblast C2C12 from mouse skeletal muscle and a mechanism of cultured muscle contraction by electric stimulation is investigated. Grayanotoxins (GTXs) belong to neurotoxins known to enhance the permeability of cell membrane for Na ions. Grayanotoxins are extracted from a famous Pieris japonica and Ericaceae as a phytotoxin. We investigated the functional role of GTXs on muscle cells (C2C12) contraction and membrane potential. A change in membrane potential is measured using a micro glass tube electrode contraction of myotubes is induced by applying an external electrical stimulation. The contraction and membrane potential change induced by injection of current using the micro glass electrode are also measured. From the result, contraction and membrane potential of muscle cells was affected by GTXs treatment, suggesting that the diverse chemical structures of GTXs are responsible for contraction and membrane potential of muscle cells.

Keywords: skeletal muscle, C2C12, myoblast, myotubes, contraction, Grayanotoxins, membrane potential, neurotoxins, phytotoxin

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

Procedia PDF Downloads 396

Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

Abstract:

Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

Procedia PDF Downloads 89

Authors: Kamal KC, Zhendong Yin, Dasen Li, Zhilu Wu

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Intra-plant disease classification based on leaf images is a challenging computer vision task due to similarities in texture, color, and shape of leaves with a slight variation of leaf spot; and external environmental changes such as lighting and background noises. Deep convolutional neural network (DCNN) has proven to be an effective tool for binary classification. In this paper, two methods for binary classification of diseased plant leaves using DCNN are presented; model created from scratch and transfer learning. Our main contribution is a thorough evaluation of 4 networks created from scratch and transfer learning of 5 pre-trained models. Training and testing of these models were performed on a plant leaf images dataset belonging to 16 distinct classes, containing a total of 22,265 images from 8 different plants, consisting of a pair of healthy and diseased leaves. We introduce a deep CNN model, Optimized MobileNet. This model with depthwise separable CNN as a building block attained an average test accuracy of 99.77%. We also present a fine-tuning method by introducing the concept of a convolutional block, which is a collection of different deep neural layers. Fine-tuned models proved to be efficient in terms of accuracy and computational cost. Fine-tuned MobileNet achieved an average test accuracy of 99.89% on 8 pairs of [healthy, diseased] leaf ImageSet.

Keywords: deep convolution neural network, depthwise separable convolution, fine-grained classification, MobileNet, plant disease, transfer learning

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Authors: Matan Cohen, Maxim Shoshany

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Climatic, topographic and geological diversity, together with frequent disturbance and recovery cycles, produce highly complex spatial patterns of trees, shrubs, dwarf shrubs and bare ground patches. Assessment of spatial and temporal variations of these life-forms patterns under climate change is of high ecological priority. Here we report on one of the first attempts to discriminate between images of three Mediterranean life-forms patterns at three densities. The development of an extensive database of orthophoto images representing these 9 pattern categories was instrumental for training and testing pre-trained and newly-trained DL models utilizing DenseNet architecture. Both models demonstrated the advantages of using Deep Learning approaches over existing spectral and spatial (pattern or texture) algorithmic methods in differentiation 9 life-form spatial mixtures categories.

Keywords: texture classification, deep learning, desert fringe ecosystems, climate change

Procedia PDF Downloads 76

Authors: Anas H. Aljemely, Jianping Xuan

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Rolling bearing fault diagnosis plays a pivotal issue in the rotating machinery of modern manufacturing. In this research, a raw vibration signal and improved deep learning method for bearing fault diagnosis are proposed. The multi-dimensional scales of raw vibration signals are selected for evaluation condition monitoring system, and the deep learning process has shown its effectiveness in fault diagnosis. In the proposed method, employing an Exponential linear unit (ELU) layer in a convolutional neural network (CNN) that conducts the identical function on positive data, an exponential nonlinearity on negative inputs, and a particular convolutional operation to extract valuable features. The identification results show the improved method has achieved the highest accuracy with a 100-dimensional scale and increase the training and testing speed.

Keywords: bearing fault prognostics, developed CNN model, multiple-scale evaluation, deep learning features

Procedia PDF Downloads 182

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 94

Authors: Teris Cheung, Joyce Yuen Ting Lam, Kwan Hin Fong, Calvin Pak-Wing Cheng, Julie Sittlington, Yu-Tao Xiang, Tim Man Ho Li

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Insomnia is one of the most common health problems in the general population. Insomnia can be acute, intermittent, and become chronic, often due to comorbidity with other physical and mental health conditions. Although there are conventional pharmaceutical and psychotherapeutic treatments to treat symptoms of insomnia, however; there is no robust and novel randomized controlled trial (RCT) using transdermal neurostimulation on individuals with insomnia symptoms. This gives us the impetus to execute the first nationwide RCT. Aim: To evaluate the efficacy of Electrical Vestibular Stimulation (VeNS) on individuals with insomnia in Hong Kong. Design: This study was a two-armed, double blinded, randomized, sham-controlled trial. Sampling: 60 community-dwelling adults aged 18 and 60 years with moderate insomnia symptoms or above (Insomnia Severity Index > 14) were recruited. All subjects were computerized randomized into either the active VeNS group or the sham VeNS group on a 1:1 ratio. Intervention: All participants received a home-use VeNS device and used 30-min VeNS sessions during five consecutive days across a 4-week period (total treatment hours: 10). Baseline measurements and post-VeNS evaluation of the psychological outcomes, including 1) insomnia severity, 2) sleep quality, and 3) quality of life were investigated. The short-and long-term sustainability of the VeNS intervention was assessed immediately after poststim and at a 1-month and 3-month follow-up period. Data analysis: A mixed GEE model was used to analyze the repeated measures data. Missing data were managed by multiple imputations. The level of significance was set to p < 0.05. Significance of the study: This is the first trial to examine the efficacy and safety of VeNS among adults with insomnia symptoms in Hong Kong. Findings that emerged were used to determine whether this VeNS device can be considered a self-help technological device to reduce the severity of insomnia in the community setting and to reduce the global disease burden. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT04452981.

Keywords: adults, insomnia, neuromodulation, rct, vestibular stimulation

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Authors: AnjalI A. Prasad

Abstract:

Nowadays, social media played an important role in spreading misinformation or fake news. This study analyzes the fake news related to the COVID-19 pandemic spread in social media. This paper aims at evaluating and comparing different approaches that are used to mitigate this issue, including popular deep learning approaches, such as CNN, RNN, LSTM, and BERT algorithm for classification. To evaluate models’ performance, we used accuracy, precision, recall, and F1-score as the evaluation metrics. And finally, compare which algorithm shows better result among the four algorithms.

Keywords: BERT, CNN, LSTM, RNN

Procedia PDF Downloads 191