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Are Financial Risks Rewarded with Appropriate Returns in Russian Banks?
Jalal Hafeth Ahmad Abu-Alrop 대한산업공학회 2021 Industrial Engineeering & Management Systems Vol.20 No.4
The purpose of this study is to evaluate the risk efficiency of 85 Russian commercial banks During the period “2008 -2017”. This study uses the Data Envelopment Analysis (DEA) with financial ratios to evaluate the risk efficiency of Russian banks. The inputs of model DEA are represented by financial risk and leverage, while the outputs are repre-sented by profitability. The study found the impact of credit risk, operational risk, and liquidity risk on performance indicators in Russian banks was positive and important, but the impact of leverage and interest rate risk on perfor-mance indicators in Russian banks was limited and negative. The study also found that the medium Russian banks were the most effective in risk efficiency, while small banks were more efficient than large banks. The study also con-cluded that the leverage recommended by Basel 3 is insufficient to provide stability in banks. The study suggested a model for monitoring Russian banks on the basis of the concept of risk efficiency to improve banking regulation and increase risk efficiency.
Ahmad Jalal,Majid Ali Khan Quaid,김기범 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
In recent years, health-care industry has received a major boost due to sensors i.e., accelerometers, magnetometers etc., which allow its user to get instant updates about their current health status in indoor/outdoor environments. The real driving force behind the usage of accelerometer has been the fi tness industry but it also holds a prominent place in ambient smart home to monitor resident’s life-style. In this paper, we proposed a novel triaxial accelerometer-based human motion detection and recognition system using multiple features and random forest. Triaxial signals have been statistically processed to produce worthy features like variance, positive and negative peaks, and signal magnitude features. The proposed model was evaluated over HMP recognition data sets and achieved satisfactory recognition accuracy of 85.17%. The proposed system is directly applicable to any elderly/children health monitoring system, 3D animated games/movies and examining the indoor behaviors of people at home, malls and offi ces.
Ahmad Jalal,Shaharyar Kamal,Cesar A. Azurdia-Meza 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.1
Assessment of human behavior during performance of daily routine actions at indoor areas plays a significant role in healthcare services and smart homes for elderly and disabled people. During this consideration, initially, depth images are captured using depth camera and segment human silhouettes due to color and intensity variation. Features considered spatiotemporal properties and obtained from the human body color joints and depth silhouettes information. Joint displacement and specific-motion features are obtained from human body color joints and side-frame differentiation features are processed based on depth data to improve classification performance. Lastly, recognizer engine is used to recognize different activities. Unlike conventional results that were evaluated using a single dataset, our experimental results have shown state-of-the-art accuracy of 88.9% and 66.70% over two challenging depth datasets. The proposed system should be serviceable with major contributions in different consumer application systems such as smart homes, video surveillance and health monitoring systems.
( Ahmad Jalal ),( Shaharyar Kamal ),( Dong-seong Kim ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.3
Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.
Facial Expression Recognition using 1D Transform Features and Hidden Markov Model
Ahmad Jalal,Shaharyar Kamal,Daijin Kim 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.4
Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.
Facial Expression Recognition using 1D Transform Features and Hidden Markov Model
Jalal, Ahmad,Kamal, Shaharyar,Kim, Daijin The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.8 No.1
Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.