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The accurate splice site prediction has several applications in the field of medical sciences and biochemistry. For instance, any mutation affecting the splice site will lead to genetic diseases and cancer such as Lynch syndrome and breast cancer. For this purpose, collecting the Ribonucleic Acid (RNA) samples is an efficient and convenient method to detect the involvement of splicing defects in disease formation. Therefore, the present study aims to develop an accurate and robust Computer-Aided Diagnosis (CAD) method for swift and precise targeting of splice site sequences. A composite features-based model is proposed by integrating three different sample representation methods i.e., Dinucleotide Composition (DNC), Trinucleotide Composition (TNC) and Tetranucleotide Composition (TetraNC) for precise splice site prediction after converting the DNA sequences into numerical descriptors. The precision and accuracy of these features are analyzed by applying different machine learning algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB). Results show that the proposed model of composite features vector with SVM classifier achieved an accuracy of 95.20% and 97.50% for donor and acceptor sites datasets, respectively.
Waseem Ullah; Khan Muhammad; Ijaz Ul Haq; Amin Ullah; Saeed Ullah Khattak; Muhammad Sajjad. Splicing sites prediction of human genome using machine learning techniques. Multimedia Tools and Applications 2021, 1 -22.
AMA StyleWaseem Ullah, Khan Muhammad, Ijaz Ul Haq, Amin Ullah, Saeed Ullah Khattak, Muhammad Sajjad. Splicing sites prediction of human genome using machine learning techniques. Multimedia Tools and Applications. 2021; ():1-22.
Chicago/Turabian StyleWaseem Ullah; Khan Muhammad; Ijaz Ul Haq; Amin Ullah; Saeed Ullah Khattak; Muhammad Sajjad. 2021. "Splicing sites prediction of human genome using machine learning techniques." Multimedia Tools and Applications , no. : 1-22.
Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.
Waseem Ullah; Amin Ullah; Tanveer Hussain; Zulfiqar Khan; Sung Baik. An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Sensors 2021, 21, 2811 .
AMA StyleWaseem Ullah, Amin Ullah, Tanveer Hussain, Zulfiqar Khan, Sung Baik. An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Sensors. 2021; 21 (8):2811.
Chicago/Turabian StyleWaseem Ullah; Amin Ullah; Tanveer Hussain; Zulfiqar Khan; Sung Baik. 2021. "An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos." Sensors 21, no. 8: 2811.
Background: Peptidases are a group of enzymes which catalyze the cleavage of peptide bonds. Around 2-3% of the whole genome codes for proteases and about one-third of all known proteases are serine proteases which are divided into 13 clans and 40 families. They are involved in diverse physiological roles such as digestion, coagulation of blood, fibrinolysis, processing of proteins and prohormones, signaling pathways, complement fixation, and have a vital role in the immune defense system. Based on their functions, they can broadly be divided into two classes; GASPIDs (Granule Associated Serine Peptidases involved in Immune Defense System) and Non- GASPIDs. GASPIDs, in particular are involved in immune-associated functions i.e. initiating apoptosis to kill virally infected and cancerous cells, cytokine modulation for the generation of inflammatory responses, and direct killing of pathogens through phagosomes. Methods: In this study, sequence-based characterization of these two types of serine proteases is performed. We first identified sequences by analyzing multiple online databases as well as by analyzing whole genomes of different species from different orthologous and non-orthologous species. Sequences were identified by devising a distinct criterion to differentiate GASPIDs from Non-GASPIDs. The translated version of these sequences was then subjected to feature extraction. Using these distinctive features, we differentiated GASPIDs from Non-GASPIDs by applying multiple supervised machine learning models. Results and Conclusion: Our results show that, among the three classifiers used in this study, SVM classifier coupled with tripeptide as feature method has shown the best accuracy in classification of sequences as GASPIDs and Non-GASPIDs.
Fawad Ahmad; Saima Ikram; Jamshaid Ahmad; Waseem Ullah; Fahad Hassan; Saeed Ullah Khattak; Irshad Ur Rehman. GASPIDs Versus Non-GASPIDs - Differentiation Based on Machine Learning Approach. Current Bioinformatics 2021, 15, 1056 -1064.
AMA StyleFawad Ahmad, Saima Ikram, Jamshaid Ahmad, Waseem Ullah, Fahad Hassan, Saeed Ullah Khattak, Irshad Ur Rehman. GASPIDs Versus Non-GASPIDs - Differentiation Based on Machine Learning Approach. Current Bioinformatics. 2021; 15 (9):1056-1064.
Chicago/Turabian StyleFawad Ahmad; Saima Ikram; Jamshaid Ahmad; Waseem Ullah; Fahad Hassan; Saeed Ullah Khattak; Irshad Ur Rehman. 2021. "GASPIDs Versus Non-GASPIDs - Differentiation Based on Machine Learning Approach." Current Bioinformatics 15, no. 9: 1056-1064.
Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).
Zulfiqar Ahmad Khan; Amin Ullah; Waseem Ullah; Seungmin Rho; Miyoung Lee; Sung Wook Baik. Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy. Applied Sciences 2020, 10, 8634 .
AMA StyleZulfiqar Ahmad Khan, Amin Ullah, Waseem Ullah, Seungmin Rho, Miyoung Lee, Sung Wook Baik. Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy. Applied Sciences. 2020; 10 (23):8634.
Chicago/Turabian StyleZulfiqar Ahmad Khan; Amin Ullah; Waseem Ullah; Seungmin Rho; Miyoung Lee; Sung Wook Baik. 2020. "Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy." Applied Sciences 10, no. 23: 8634.
In current technological era, surveillance systems generate an enormous volume of video data on a daily basis, making its analysis a difficult task for computer vision experts. Manually searching for unusual events in these massive video streams is a challenging task, since they occur inconsistently and with low probability in real-world surveillance. In contrast, deep learning-based anomaly detection reduces human labour and its decision making ability is comparatively reliable, thereby ensuring public safety. In this paper, we present an efficient deep features-based intelligent anomaly detection framework that can operate in surveillance networks with reduced time complexity. In the proposed framework, we first extract spatiotemporal features from a series of frames by passing each one to a pre-trained Convolutional Neural Network (CNN) model. The features extracted from the sequence of frames are valuable in capturing anomalous events. We then pass the extracted deep features to multi-layer Bi-directional Long Short-term Memory (BD-LSTM) model, which can accurately classify ongoing anomalous/normal events in complex surveillance scenes of smart cities. We performed extensive experiments on various anomaly detection benchmark datasets to validate the functionality of the proposed framework within complex surveillance scenarios. We reported a 3.41% and 8.09% increase in accuracy on UCF-Crime and UCFCrime2Local datasets compared to state-of-the-art methods.
Waseem Ullah; Amin Ullah; Ijaz Ul Haq; Khan Muhammad; Muhammad Sajjad; Sung Wook Baik. CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks. Multimedia Tools and Applications 2020, 80, 16979 -16995.
AMA StyleWaseem Ullah, Amin Ullah, Ijaz Ul Haq, Khan Muhammad, Muhammad Sajjad, Sung Wook Baik. CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks. Multimedia Tools and Applications. 2020; 80 (11):16979-16995.
Chicago/Turabian StyleWaseem Ullah; Amin Ullah; Ijaz Ul Haq; Khan Muhammad; Muhammad Sajjad; Sung Wook Baik. 2020. "CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks." Multimedia Tools and Applications 80, no. 11: 16979-16995.
Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression, Artificial Neural Networks, Extreme Learning Machine and Support Vector Machine. These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network and Gated Recurrent Units into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features’ extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on individual Appliances Energy prediction and household electric power consumption datasets, when compared to other baseline models.
Muhammad Sajjad; Zulfiqar Ahmad Khan; Amin Ullah; Tanveer Hussain; Waseem Ullah; Mi Young Lee; Sung Wook Baik. A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting. IEEE Access 2020, 8, 143759 -143768.
AMA StyleMuhammad Sajjad, Zulfiqar Ahmad Khan, Amin Ullah, Tanveer Hussain, Waseem Ullah, Mi Young Lee, Sung Wook Baik. A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting. IEEE Access. 2020; 8 (99):143759-143768.
Chicago/Turabian StyleMuhammad Sajjad; Zulfiqar Ahmad Khan; Amin Ullah; Tanveer Hussain; Waseem Ullah; Mi Young Lee; Sung Wook Baik. 2020. "A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting." IEEE Access 8, no. 99: 143759-143768.