This research presents a navigation robotic system designed for the concurrent tasks of line following and obstacle avoidance in partially-known environments with presence of obstacles. By applying a strategically pos...
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Image matching is determining the correspondence between two images of the same scene. It is considered one of the most critical processes in computer vision and remote sensing. This paper aims to match multispectral ...
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This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, ...
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Approximate computing (AC) is a paradigm that introduces errors for reduced design metrics. AC has been recommended for implementation in error-resilient applications. Previously proposed AC implementations can be mod...
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The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial I...
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Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identi...
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An essential component of the diagnostic and treatment process is identifying brain tumors early in their onslaught. Traditional approaches struggle with processing sequential data and face limitations in maintaining ...
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The increased use of small satellites in commercial, defense, and research industries reinforces the need for efficient and reliable communication subsystems. Compact, space rated, communication subsystems present the...
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Detecting and identifying faults is essential for protection systems to minimize downtime and prevent cascading failures. This work proposes a real-Time deep learning-based fault diagnosis framework to detect the faul...
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Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the ...
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Signal processing based research was adopted with Electroencephalogram(EEG)for predicting the abnormality and cerebral *** proposed research work is intended to provide an automatic diagnostic system to determine the EEG signal in order to classify the brain function which shows whether a person is affected with schizophrenia or *** detection and intervention are vital for better ***,the diagnosis of schizophrenia still depends on clinical observation to *** reliable biomarkers,schizophrenia is difficult to detect in its early phase and hence we have proposed this *** this work,the EEG signal series are divided into non-linear feature mining,classification and validation,and t-test integrated feature selection *** this work,19-channel EEG signals are utilized from schizophrenia class and normal ***,the datasets initially execute the splitting process based on raw 19-channel EEG into 6250 sample point’s *** this process,1142 features of normal and schizophrenia class patterns can be *** other hand,157 features from each EEG patterns are utilized based on Non-linear feature extraction process where 14 principal features can be identified in terms of considering the essential *** last,the Deep Learning(DL)technique incorporated with an effective optimization technique is adopted for classification process called a Deep Convolutional Neural Network(DCNN)with mayfly optimization *** proposed technique is implemented into the platform of MATLAB in order to obtain better results and is analyzed based on the performance analysis framework such as accuracy,Signal to Noise Ratio(SNR),Mean Square Error,Normalized Mean Square Error(NMSE)and *** comparison,the proposed technique is proved to a better technique than other existing techniques.
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