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The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.
Hamdi Altaheri; Ghulam Muhammad; Mansour Alsulaiman; Syed Umar Amin; Ghadir Ali Altuwaijri; Wadood Abdul; Mohamed A. Bencherif; Mohammed Faisal. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Computing and Applications 2021, 1 -42.
AMA StyleHamdi Altaheri, Ghulam Muhammad, Mansour Alsulaiman, Syed Umar Amin, Ghadir Ali Altuwaijri, Wadood Abdul, Mohamed A. Bencherif, Mohammed Faisal. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review. Neural Computing and Applications. 2021; ():1-42.
Chicago/Turabian StyleHamdi Altaheri; Ghulam Muhammad; Mansour Alsulaiman; Syed Umar Amin; Ghadir Ali Altuwaijri; Wadood Abdul; Mohamed A. Bencherif; Mohammed Faisal. 2021. "Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review." Neural Computing and Applications , no. : 1-42.
Intelligent sensing plays an important part in making our use of vehicles safe and problem-free. On average, a person spends over 35 hours in traffic jams each year. This valuable time could be saved by intelligent routing and real-time traffic alerts. Transport is a necessity of life, both in our everyday lives and at work. Navigation apps are now enabling users to access real-time alerts and alternatives. However, with the increase in the number of Internet-of-Vehicle-Things (IoVT), a large amount of data is produced within a short period of time. The huge data produced by the IoVT could be used to obtain greater perspective and to make dramatically smarter decisions. With this data, there is always a risk to security, trust, and privacy (STP). A standardized protocol is needed to preserve privacy and maintain the security of data. This paper addressed several STP issues in an intelligent transportation system. In addition, a deep learning model is proposed to process data generated by the IoVT.
Ghulam Muhammad; Musaed Alhussein. Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach. IEEE Consumer Electronics Magazine 2021, PP, 1 -1.
AMA StyleGhulam Muhammad, Musaed Alhussein. Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach. IEEE Consumer Electronics Magazine. 2021; PP (99):1-1.
Chicago/Turabian StyleGhulam Muhammad; Musaed Alhussein. 2021. "Security, Trust, and Privacy for the Internet of Vehicles: A Deep Learning Approach." IEEE Consumer Electronics Magazine PP, no. 99: 1-1.
Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.
Guan Wang; Jiali Yin; M. Shamim Hossain; Ghulam Muhammad. Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things. Future Generation Computer Systems 2021, 125, 376 -384.
AMA StyleGuan Wang, Jiali Yin, M. Shamim Hossain, Ghulam Muhammad. Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things. Future Generation Computer Systems. 2021; 125 ():376-384.
Chicago/Turabian StyleGuan Wang; Jiali Yin; M. Shamim Hossain; Ghulam Muhammad. 2021. "Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things." Future Generation Computer Systems 125, no. : 376-384.
Tungsten disulfide (WS2) thin films were deposited on soda-lime glass (SLG) substrates using radio frequency (RF) magnetron sputtering at different Ar flow rates (3 to 7 sccm). The effect of Ar flow rates on the structural, morphology, and electrical properties of the WS2 thin films was investigated thoroughly. Structural analysis exhibited that all the as-grown films showed the highest peak at (101) plane corresponds to rhombohedral phase. The crystalline size of the film ranged from 11.2 to 35.6 nm, while dislocation density ranged from 7.8 × 1014 to 26.29 × 1015 lines/m2. All these findings indicate that as-grown WS2 films are induced with various degrees of defects, which were visible in the FESEM images. FESEM images also identified the distorted crystallographic structure for all the films except the film deposited at 5 sccm of Ar gas flow rate. EDX analysis found that all the films were having a sulfur deficit and suggested that WS2 thin film bears edge defects in its structure. Further, electrical analysis confirms that tailoring of structural defects in WS2 thin film can be possible by the varying Ar gas flow rates. All these findings articulate that Ar gas flow rate is one of the important process parameters in RF magnetron sputtering that could affect the morphology, electrical properties, and structural properties of WS2 thin film. Finally, the simulation study validates the experimental results and encourages the use of WS2 as a buffer layer of CdTe-based solar cells.
Akhtaruzzaman; Shahiduzzaman; Nowshad Amin; Ghulam Muhammad; Mohammad Islam; Khan Rafiq; Kamaruzzaman Sopian. Impact of Ar Flow Rates on Micro-Structural Properties of WS2 Thin Film by RF Magnetron Sputtering. Nanomaterials 2021, 11, 1635 .
AMA StyleAkhtaruzzaman, Shahiduzzaman, Nowshad Amin, Ghulam Muhammad, Mohammad Islam, Khan Rafiq, Kamaruzzaman Sopian. Impact of Ar Flow Rates on Micro-Structural Properties of WS2 Thin Film by RF Magnetron Sputtering. Nanomaterials. 2021; 11 (7):1635.
Chicago/Turabian StyleAkhtaruzzaman; Shahiduzzaman; Nowshad Amin; Ghulam Muhammad; Mohammad Islam; Khan Rafiq; Kamaruzzaman Sopian. 2021. "Impact of Ar Flow Rates on Micro-Structural Properties of WS2 Thin Film by RF Magnetron Sputtering." Nanomaterials 11, no. 7: 1635.
The integration of artificial intelligence (AI) and the Internet of Things (IoT) has tremendous prospects in smart healthcare. The advancement of AI in the form of deep learning brought a revolution in automatic classification and detection systems. In addition, next-generation wireless communications such as 5G networking brought speed and the seamless transmission of data. With the convergence of these elements, the smart healthcare sector is currently booming. Particularly during the post-COVID-19 pandemic, the necessity of smart healthcare has come to light more than before. A significant number of people suffer from voice pathology. This pathology can be easily cured if detected early. In this study, a voice pathology detection system within a smart healthcare framework is proposed. The inputs are obtained by the IoT, namely microphones and electroglottography (EGG) devices to capture voice and EGG signals, respectively. Spectrograms are obtained from these signals and fed into a pretrained convolutional neural network (CNN). The features extracted from the CNN are fused and processed using a bi-directional long short-term memory network. The proposed system is evaluated using a publicly available database, called the Saarbruecken voice database. The experimental results show that bimodal input performs better than a single input. An accuracy of 95.65% is obtained for the proposed system.
Ghulam Muhammad; Musaed Alhussein. Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection. IEEE Access 2021, 9, 1 -1.
AMA StyleGhulam Muhammad, Musaed Alhussein. Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleGhulam Muhammad; Musaed Alhussein. 2021. "Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection." IEEE Access 9, no. : 1-1.
Due to the rapid developments in technology and the sudden expansion of social media use, Dialect Arabic has become an important source of data that needs to be addressed when building Arabic corpora. In this paper, thirty-three Arabic corpora are surveyed to show that despite all of the developments in the literature, Saudi dialect (SD) corpora still need further expansion. This paper contributes to the literature on SD corpora by creating the largest Saudi corpus – the King Saud University Saudi Corpus (KSUSC) – with +1B total words, including +119M SD words. The KSUSC not only is the newest and largest SD corpus but is also diverse, covering 26 domains in text collected from five different sources. This paper also contributes to the literature by developing a new incremental preprocessing system that is used to create relevant lexicons that are then used to clean and normalize the collected data. This incremental system is scalable and can be adapted for different resources and dialects. Moreover, the collection process for building the KSUSC is discussed in detail, and the challenges in collecting SD text with respect to each platform are highlighted. By the end of this paper, different design criteria are proposed and used with the KSUSC to conclude that the resulting corpus can be of great benefit to researchers who are interested in integrating the corpus with their own work or using its resulting lexicons with Saudi-based NLP tasks.
Hebah ElGibreen; Mohammed Faisal; Mansour Al Sulaiman; Sherif Abdou; Mohamed Amine Mekhtiche; Abdullah M. Moussa; Yousef A. Alohali; Wadood Abdul; Ghulam Muhammad; Mohsen Rashwan; Mohammed Algabri. An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus. IEEE Access 2021, 9, 1 -1.
AMA StyleHebah ElGibreen, Mohammed Faisal, Mansour Al Sulaiman, Sherif Abdou, Mohamed Amine Mekhtiche, Abdullah M. Moussa, Yousef A. Alohali, Wadood Abdul, Ghulam Muhammad, Mohsen Rashwan, Mohammed Algabri. An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleHebah ElGibreen; Mohammed Faisal; Mansour Al Sulaiman; Sherif Abdou; Mohamed Amine Mekhtiche; Abdullah M. Moussa; Yousef A. Alohali; Wadood Abdul; Ghulam Muhammad; Mohsen Rashwan; Mohammed Algabri. 2021. "An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus." IEEE Access 9, no. : 1-1.
Motor imagery electroencephalography (MI-EEG) signals are generated when a person imagines a task without actually performing it. In recent studies, MI-EEG has been used in the rehabilitation process of paralyzed patients, therefore, decoding MI-EEG signals accurately is an important task, and it is difficult task due to the low signal-to-noise ratio and the variation of brain waves between subjects. Deep learning techniques such as the convolution neural network (CNN) have shown an impact in extracting meaningful features to improve the accuracy of classification. In this paper, we propose TCNet-Fusion, a fixed hyperparameter-based CNN model that utilizes multiple techniques, such as temporal convolutional networks (TCNs), separable convolution, depth-wise convolution, and the fusion of layers. This model outperforms other fixed hyperparameter-based CNN models while remaining similar to those that utilize variable hyperparameter networks, which are networks that change their hyperparameters based on each subject, resulting in higher accuracy than fixed networks. It also uses less memory than variable networks. The EEG signal undergoes two successive 1D convolutions, first along with the time domain, then channel-wise. Then, we obtain an image-like representation, which is fed to the main TCN. During experimentation, the model achieved a classification accuracy of 83.73 % on the four-class MI of the BCI Competition IV-2a dataset, and an accuracy of 94.41 % on the High Gamma Dataset.
Yazeed K. Musallam; Nasser I. AlFassam; Ghulam Muhammad; Syed Umar Amin; Mansour Alsulaiman; Wadood Abdul; Hamdi Altaheri; Mohamed A. Bencherif; Mohammed Algabri. Electroencephalography-based motor imagery classification using temporal convolutional network fusion. Biomedical Signal Processing and Control 2021, 69, 102826 .
AMA StyleYazeed K. Musallam, Nasser I. AlFassam, Ghulam Muhammad, Syed Umar Amin, Mansour Alsulaiman, Wadood Abdul, Hamdi Altaheri, Mohamed A. Bencherif, Mohammed Algabri. Electroencephalography-based motor imagery classification using temporal convolutional network fusion. Biomedical Signal Processing and Control. 2021; 69 ():102826.
Chicago/Turabian StyleYazeed K. Musallam; Nasser I. AlFassam; Ghulam Muhammad; Syed Umar Amin; Mansour Alsulaiman; Wadood Abdul; Hamdi Altaheri; Mohamed A. Bencherif; Mohammed Algabri. 2021. "Electroencephalography-based motor imagery classification using temporal convolutional network fusion." Biomedical Signal Processing and Control 69, no. : 102826.
Recent achievements, based on lead (Pb) halide perovskites, have prompted comprehensive research on low-cost photovoltaics, in order to avoid the major challenges that arise in this respect: Stability and toxicity. In this study, device modelling of lead (Pb)-free perovskite solar cells has been carried out considering methyl ammonium tin bromide (CH3NH3SnBr3) as perovskite absorber layer. The perovskite structure has been justified theoretically by Goldschmidt tolerance factor and the octahedral factor. Numerical modelling tools were used to investigate the effects of amphoteric defect and interface defect states on the photovoltaic parameters of CH3NH3SnBr3-based perovskite solar cell. The study identifies the density of defect tolerance in the absorber layer, and that both the interfaces are 1015 cm−3, and 1014 cm−3, respectively. Furthermore, the simulation evaluates the influences of metal work function, uniform donor density in the electron transport layer and the impact of series resistance on the photovoltaic parameters of proposed n-TiO2/i-CH3NH3SnBr3/p-NiO solar cell. Considering all the optimization parameters, CH3NH3SnBr3-based perovskite solar cell exhibits the highest efficiency of 21.66% with the Voc of 0.80 V, Jsc of 31.88 mA/cm2 and Fill Factor of 84.89%. These results divulge the development of environmentally friendly methyl ammonium tin bromide perovskite solar cell.
Samiul Islam; K. Sobayel; Ammar Al-Kahtani; M. Islam; Ghulam Muhammad; N. Amin; Shahiduzzaman; Akhtaruzzaman. Defect Study and Modelling of SnX3-Based Perovskite Solar Cells with SCAPS-1D. Nanomaterials 2021, 11, 1218 .
AMA StyleSamiul Islam, K. Sobayel, Ammar Al-Kahtani, M. Islam, Ghulam Muhammad, N. Amin, Shahiduzzaman, Akhtaruzzaman. Defect Study and Modelling of SnX3-Based Perovskite Solar Cells with SCAPS-1D. Nanomaterials. 2021; 11 (5):1218.
Chicago/Turabian StyleSamiul Islam; K. Sobayel; Ammar Al-Kahtani; M. Islam; Ghulam Muhammad; N. Amin; Shahiduzzaman; Akhtaruzzaman. 2021. "Defect Study and Modelling of SnX3-Based Perovskite Solar Cells with SCAPS-1D." Nanomaterials 11, no. 5: 1218.
Recently, great progress has been witnessed in the application of mobile cloud computing in the field of health care such as online medical inquiries. However, due to the limitation of cognitive intelligence, QoE (Quality of Experience) is hampered by two problems, the first of which is that the traffic pressure of the core network cannot well meet the requirements of delay-sensitive emotional services, especially for users with different emergencies, while the second is that current applications cannot provide personalized service for different users. Based on the two problems, we propose an emotion-aware mobile edge computing architecture based on emotional task priority to guide the allocation of edge resources and to provide intelligent and personalized emotional services with higher QoE. Specifically, we first introduce the entities involved in the proposed architecture of emotion-aware mobile edge computing system. Next, we describe our optimal computing resource allocation strategy, including important concepts and a detailed algorithm. Finally, we build a test platform and conduct experiments, which show that the proposed architecture obtains better performance in terms of system utility compared with baseline methods.
Qiao Yu; Wenjing Xiao; Sheng Jiang; Mohammed F. Alhamid; Ghulam Muhammad; M. Shamim Hossain. Emotion-aware mobile edge computing system: A case study. Computers & Electrical Engineering 2021, 92, 107120 .
AMA StyleQiao Yu, Wenjing Xiao, Sheng Jiang, Mohammed F. Alhamid, Ghulam Muhammad, M. Shamim Hossain. Emotion-aware mobile edge computing system: A case study. Computers & Electrical Engineering. 2021; 92 ():107120.
Chicago/Turabian StyleQiao Yu; Wenjing Xiao; Sheng Jiang; Mohammed F. Alhamid; Ghulam Muhammad; M. Shamim Hossain. 2021. "Emotion-aware mobile edge computing system: A case study." Computers & Electrical Engineering 92, no. : 107120.
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
Loveleen Gaur; Ujwal Bhatia; N. Z. Jhanjhi; Ghulam Muhammad; Mehedi Masud. Medical image-based detection of COVID-19 using Deep Convolution Neural Networks. Multimedia Systems 2021, 1 -10.
AMA StyleLoveleen Gaur, Ujwal Bhatia, N. Z. Jhanjhi, Ghulam Muhammad, Mehedi Masud. Medical image-based detection of COVID-19 using Deep Convolution Neural Networks. Multimedia Systems. 2021; ():1-10.
Chicago/Turabian StyleLoveleen Gaur; Ujwal Bhatia; N. Z. Jhanjhi; Ghulam Muhammad; Mehedi Masud. 2021. "Medical image-based detection of COVID-19 using Deep Convolution Neural Networks." Multimedia Systems , no. : 1-10.
The recording device along with the acoustic environment plays a major role in digital audio forensics. We propose an acoustic source identification system in this paper, which includes identifying both the recording device and the environment in which it was recorded. A hybrid Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) is used in this study to automatically extract environments and microphone features from the speech sound. In the experiments, we investigated the effect of using the voiced and unvoiced segments of speech on the accuracy of the environment and microphone classification. We also studied the effect of background noise on microphone classification in 3 different environments, i.e., very quiet, quiet, and noisy. The proposed system utilizes a subset of the KSU-DB corpus containing 3 environments, 4 classes of recording devices, 136 speakers (68 males and 68 females), and 3600 recordings of words, sentences, and continuous speech. This research combines the advantages of both CNN and RNN (in particular bidirectional LSTM) models, called CRNN. The speech signals were represented as a spectrogram and were fed to the CRNN model as 2D images. The proposed method achieved accuracies of 98% and 98.57% for environment and microphone classification, respectively, using unvoiced speech segments.
Mustafa A. Qamhan; Hamdi Altaheri; Ali Hamid Meftah; Ghulam Muhammad; Yousef Ajami Alotaibi. Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning. IEEE Access 2021, 9, 62719 -62733.
AMA StyleMustafa A. Qamhan, Hamdi Altaheri, Ali Hamid Meftah, Ghulam Muhammad, Yousef Ajami Alotaibi. Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning. IEEE Access. 2021; 9 ():62719-62733.
Chicago/Turabian StyleMustafa A. Qamhan; Hamdi Altaheri; Ali Hamid Meftah; Ghulam Muhammad; Yousef Ajami Alotaibi. 2021. "Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning." IEEE Access 9, no. : 62719-62733.
The landscape of fifth generation (5G) and beyond 5G (B5G)-enabled Internet of Things(IoT) is expected to seamlessly and ubiquitously connect everything, which includes 5G, cloud computing, artificial intelligence and other cutting-edge technologies to realize truly intelligent applications in smart cities. In this paper, we present an important key technology for smart city, which is a road target recognition algorithm for smart city applications and designs a set of corresponding programs to assist automatic drivers, pedestrians and visually impaired people in road safety, or to manage city infrastructure. The system can connect robots in cars, wearable devices and body area network in pedestrians or blind people. A target recognition algorithm based on scene fusion is designed to recognize the specific target in the road environment, and transfer reinforcement learning method is used to improve the accuracy and real-time performance of target recognition. The system provides them with travel assistance, identify dangerous or useful objects for them through high-performance target recognition services. It can collect the road visual scene data by road cameras and transmit it to edge devices for training model. The model is collaborated trained in the edge devices and aggregated by the cloud. Based on the transfer reinforcement learning method, the vision-based road target recognition has been implemented, and the accurate and reliable target recognition can be realized. Many details of experiments verify the effectiveness of our technology.
Ke Wang; Chien-Ming Chen; M. Shamim Hossain; Ghulam Muhammad; Sachin Kumar; Saru Kumari. Transfer reinforcement learning-based road object detection in next generation IoT domain. Computer Networks 2021, 193, 108078 .
AMA StyleKe Wang, Chien-Ming Chen, M. Shamim Hossain, Ghulam Muhammad, Sachin Kumar, Saru Kumari. Transfer reinforcement learning-based road object detection in next generation IoT domain. Computer Networks. 2021; 193 ():108078.
Chicago/Turabian StyleKe Wang; Chien-Ming Chen; M. Shamim Hossain; Ghulam Muhammad; Sachin Kumar; Saru Kumari. 2021. "Transfer reinforcement learning-based road object detection in next generation IoT domain." Computer Networks 193, no. : 108078.
Fabrication of quasi-solid state polymer electrolytes are recently being endorsed by electrochemists due to its superior electrical and physical properties. With the aspiration to develop a sustainable electrolyte component, this study is a novel attempt to fabricate quasi-solid electrolyte based on esterified starch. Potato starch was chemically modified via simple phthaloylation method. The resulting amorphous, hydrophobic starch derivative was used as a polymer base to prepare cost effective thermoplastic gel electrolytes via incorporation of propylene carbonate, dimethylformamide and lithium iodide. Fourier transform infrared spectroscopy and X-ray diffraction characterizations verified the impact of phthaloylation and plasticization in suppressing the crystallinity and hydrophilicity of starch. The biopolymer gel with 40 wt.% LiI recorded the highest room temperature ionic conductivity of 4.82 mS cm−1. The sample with highest ionic conductivity recorded the best efficiency of 3.56%, which is one of the highest values for starch electrolyte-based dye-sensitized solar cells (DSSC). The optimized efficiency indicate that starch-based electrolyte has good prospects for fabrication of quasi-solid DSSC.
Vidhya Selvanathan; Mohd Hafidz Ruslan; Ammar Ahmed Nasser Alkahtani; Nowshad Amin; Kamaruzzaman Sopian; Ghulam Muhammad; Akhtaruzzaman. Organosoluble, esterified starch as quasi-solid biopolymer electrolyte in dye-sensitized solar cell. Journal of Materials Research and Technology 2021, 12, 1638 -1648.
AMA StyleVidhya Selvanathan, Mohd Hafidz Ruslan, Ammar Ahmed Nasser Alkahtani, Nowshad Amin, Kamaruzzaman Sopian, Ghulam Muhammad, Akhtaruzzaman. Organosoluble, esterified starch as quasi-solid biopolymer electrolyte in dye-sensitized solar cell. Journal of Materials Research and Technology. 2021; 12 ():1638-1648.
Chicago/Turabian StyleVidhya Selvanathan; Mohd Hafidz Ruslan; Ammar Ahmed Nasser Alkahtani; Nowshad Amin; Kamaruzzaman Sopian; Ghulam Muhammad; Akhtaruzzaman. 2021. "Organosoluble, esterified starch as quasi-solid biopolymer electrolyte in dye-sensitized solar cell." Journal of Materials Research and Technology 12, no. : 1638-1648.
Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%).
Ohoud Nafea; Wadood Abdul; Ghulam Muhammad; Mansour Alsulaiman. Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning. Sensors 2021, 21, 2141 .
AMA StyleOhoud Nafea, Wadood Abdul, Ghulam Muhammad, Mansour Alsulaiman. Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning. Sensors. 2021; 21 (6):2141.
Chicago/Turabian StyleOhoud Nafea; Wadood Abdul; Ghulam Muhammad; Mansour Alsulaiman. 2021. "Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning." Sensors 21, no. 6: 2141.
In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM’s hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT’s sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.
Zengchen Yu; Syed Umar Amin; Musaed Alhussein; ZhiHan Lv. Research on Disease Prediction Based on Improved DeepFM and IoMT. IEEE Access 2021, 9, 39043 -39054.
AMA StyleZengchen Yu, Syed Umar Amin, Musaed Alhussein, ZhiHan Lv. Research on Disease Prediction Based on Improved DeepFM and IoMT. IEEE Access. 2021; 9 ():39043-39054.
Chicago/Turabian StyleZengchen Yu; Syed Umar Amin; Musaed Alhussein; ZhiHan Lv. 2021. "Research on Disease Prediction Based on Improved DeepFM and IoMT." IEEE Access 9, no. : 39043-39054.
Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator’s command simultaneously. By using the Internet as the robot’s source of information and the Android phone as the robot’s sensory and control system partially, and adding them all to the robot’s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount.
Mohammad Shamim Islam; Mizanur Rahman; Ghulam Muhammad; M. Shamim Hossain. Design of A Social Robot Interact with Artificial Intelligence by Versatile Control Systems. IEEE Sensors Journal 2021, PP, 1 -1.
AMA StyleMohammad Shamim Islam, Mizanur Rahman, Ghulam Muhammad, M. Shamim Hossain. Design of A Social Robot Interact with Artificial Intelligence by Versatile Control Systems. IEEE Sensors Journal. 2021; PP (99):1-1.
Chicago/Turabian StyleMohammad Shamim Islam; Mizanur Rahman; Ghulam Muhammad; M. Shamim Hossain. 2021. "Design of A Social Robot Interact with Artificial Intelligence by Versatile Control Systems." IEEE Sensors Journal PP, no. 99: 1-1.
COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.
Ghulam Muhammad; M. Shamim Hossain. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images. Information Fusion 2021, 72, 80 -88.
AMA StyleGhulam Muhammad, M. Shamim Hossain. COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images. Information Fusion. 2021; 72 ():80-88.
Chicago/Turabian StyleGhulam Muhammad; M. Shamim Hossain. 2021. "COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images." Information Fusion 72, no. : 80-88.
The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.
Adam Zielonka; Marcin Wozniak; Sahil Garg; Georges Kaddoum; Jalil Piran; Ghulam Muhammad. Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances. IEEE Access 2021, 9, 26388 -26419.
AMA StyleAdam Zielonka, Marcin Wozniak, Sahil Garg, Georges Kaddoum, Jalil Piran, Ghulam Muhammad. Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances. IEEE Access. 2021; 9 (99):26388-26419.
Chicago/Turabian StyleAdam Zielonka; Marcin Wozniak; Sahil Garg; Georges Kaddoum; Jalil Piran; Ghulam Muhammad. 2021. "Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances." IEEE Access 9, no. 99: 26388-26419.
In recent years, the green chemistry based-approach for the synthesis of nanoparticles has shown tremendous promise as an alternative to the costly and environmentally unfriendly chemically synthesized nanoparticles. In this study, copper oxide nanoparticles (CuO NPs) were synthesized through a green approach using the water extract of papaya (Carica papaya L.) peel biowaste as reducing as well as stabilizing agents, and copper (II) nitrate trihydrate salt as a precursor. The structural properties, crystallinity, purity, morphology, and the chemical composition of as-synthesized CuO NPs were analyzed using different analytical methods. The analytical results revealed that the synthesized CuO was observed as spherical-like in particles with measured sizes ranging from 85–140 nm and has monoclinic crystalline phase with good purity. The Fourier transform infrared (FTIR) spectroscopic results confirmed the formation of the Cu-O bond through the involvement of the potential functional groups of biomolecules in papaya peel extract. Regarding photocatalytic activity, the green-synthesized CuO NPs were employed as a photocatalyst for the degradation of palm oil mill effluent (POME) beneath the ultraviolet (UV) light and results showed 66% degradation of the POME was achieved after 3 h exposure to UV irradiation. The phytotoxicity experiment using mung bean (Vigna radiata L.) seed also showed a reduction of toxicity after photodegradation.
You-Kang Phang; Mohammod Aminuzzaman; Akhtaruzzaman; Ghulam Muhammad; Sayaka Ogawa; Akira Watanabe; Lai-Hock Tey. Green Synthesis and Characterization of CuO Nanoparticles Derived from Papaya Peel Extract for the Photocatalytic Degradation of Palm Oil Mill Effluent (POME). Sustainability 2021, 13, 796 .
AMA StyleYou-Kang Phang, Mohammod Aminuzzaman, Akhtaruzzaman, Ghulam Muhammad, Sayaka Ogawa, Akira Watanabe, Lai-Hock Tey. Green Synthesis and Characterization of CuO Nanoparticles Derived from Papaya Peel Extract for the Photocatalytic Degradation of Palm Oil Mill Effluent (POME). Sustainability. 2021; 13 (2):796.
Chicago/Turabian StyleYou-Kang Phang; Mohammod Aminuzzaman; Akhtaruzzaman; Ghulam Muhammad; Sayaka Ogawa; Akira Watanabe; Lai-Hock Tey. 2021. "Green Synthesis and Characterization of CuO Nanoparticles Derived from Papaya Peel Extract for the Photocatalytic Degradation of Palm Oil Mill Effluent (POME)." Sustainability 13, no. 2: 796.
Smart health care is an important aspect of connected living. Health care is one of the basic pillars of human need, and smart health care is projected to produce several billion dollars in revenue in the near future. There are several components of smart health care, including the Internet of Things (IoT), the Internet of Medical Things (IoMT), medical sensors, artificial intelligence (AI), edge computing, cloud computing, and next-generation wireless communication technology. Many papers in the literature deal with smart health care or health care in general. Here, we present a comprehensive survey of IoT- and IoMT-based edge-intelligent smart health care, mainly focusing on journal articles published between 2014 and 2020. We survey this literature by answering several research areas on IoT and IoMT, AI, edge and cloud computing, security, and medical signals fusion. We also address current research challenges and offer some future research directions.
Fatima AlShehri; Ghulam Muhammad. A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare. IEEE Access 2020, 9, 3660 -3678.
AMA StyleFatima AlShehri, Ghulam Muhammad. A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare. IEEE Access. 2020; 9 ():3660-3678.
Chicago/Turabian StyleFatima AlShehri; Ghulam Muhammad. 2020. "A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare." IEEE Access 9, no. : 3660-3678.