DL Models Research Articles

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond.

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

Modified Barnacles Mating Optimization with Deep Learning Based Weed Detection Model for Smart Agriculture

Weed control is a significant means to enhance crop production. Weeds are accountable for 45% of the agriculture sector’s crop losses, which primarily occur because of competition with crops. Accurate and rapid weed detection in agricultural fields was a difficult task because of the presence of a wide range of weed species at various densities and growth phases. Presently, several smart agriculture tasks, such as weed detection, plant disease detection, species identification, water and soil conservation, and crop yield prediction, can be realized by using technology. In this article, we propose a Modified Barnacles Mating Optimization with Deep Learning based weed detection (MBMODL-WD) technique. The MBMODL-WD technique aims to automatically identify the weeds in the agricultural field. Primarily, the presented MBMODL-WD technique uses the Gabor filtering (GF) technique for the noise removal process. For automated weed detection, the presented MBMODL-WD technique uses the DenseNet-121 model as feature extraction with the MBMO algorithm as hyperparameter optimization. The design of the MBMO algorithm involves the integration of self-population-based initialization with the standard BMO algorithm. At last, the Elman Neural Network (ENN) method was applied for the weed classification process. To demonstrate the enhanced performance of the MBMODL-WD approach, a series of simulation analyses were performed. A comprehensive set of simulations highlighted the enhanced performance of the presented MBMODL-WD methodology over other DL models with a maximum accuracy of 98.99%.

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model.

Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used. But classifying many intricate pathology images by hand will be challenging for pathologists. The lack of labeling data makes the system difficult to build and costly. This article designs a Honey Badger Optimization with Deep Learning based Automated Osteosarcoma Classification (HBODL-AOC) model. The HBODL-AOC technique's goal is to identify osteosarcoma's existence using medical images. In the presented HBODL-AOC technique, image preprocessing is initially performed by contrast enhancement technique. For feature extraction, the HBODL-AOC technique employs a deep convolutional neural network-based Mobile networks (MobileNet) model with an Adam optimizer for hyperparameter tuning. Finally, the adaptive neuro-fuzzy inference system (ANFIS) approach is implemented for the HBO (Honey Badger Optimization) algorithm can tune osteosarcoma classification and the membership function (MF). To demonstrate the enhanced classification performance of the HBODL-AOC approach, a sequence of simulations was performed. The extensive simulation analysis portrayed the improved performance of the HBODL-AOC technique over existing DL models.

Automated biomass recycling management system using modified grey wolf optimization with deep learning model

Biomass residues encompass non-recyclable municipal solid waste, crop wastes, sewage effluents and sludges, domestic and industrial greywater, etc. Numerous wastes to energy conversion technology use biomass to generate various kinds of renewable energy to reduce environmental issues. The recycling rate seems to rise continuously, but reports reveal that humans are creating more waste than before. Machine learning (ML) can be used that offers a structure to take as a structural enhancement of the fact without being programmed. This study proposes an automated biomass recycling management system using modified grey wolf optimization with deep learning (ABRM-MGWODL) model. The presented ABRM-MGWODL technique aims to effectually identify and categorize the waste objects to enable effectual biomass recycling. The ABRM-MGWODL method would follow 2 major processes: waste object detection and waste object classification. For the waste object recognition and detection process, the YOLO-v4 model is exploited in this work. Next, the graph convolution network (GCN) method can be used for classifying recognized waste objects. Finally, hyperparameter tuning of the GCN model is effectually carried out using the MGWO algorithm, thereby enhancing the ABRM-MGWODL method's classification outcome. A widespread set of simulations were performed to ensure the superior waste classification efficacy of the ABRM-MGWODL model. The simulation outcomes demonstrate the improvements of the ABRM-MGWODL method to other DL models with increased accuracy of 99.01%.

Enhanced Bird Swarm Algorithm with Deep Learning based Electroencephalography Signal Analysis for Emotion Recognition

Abstract Bioelectric signals comprise a massive count of data, and researchers in various domains containing cognitive neuroscience, psychiatry, and so on. Emotion is a vital part of regular human communication. The emotional conditions and dynamics of brain are connected by electroencephalography (EEG) signal which is utilized by Brain-Computer Interface (BCI), for providing optimum human-machine interaction. EEG-based emotion detection was extremely utilized in military, human-computer interactions, medicinal analysis, and other domains. Identifying emotions utilizing biological brain signals need accurate and effectual signal processing and extracting features approaches. But, one of the essential problems facing the emotion detection method, utilizing EEG signal is the detection accuracy. In this aspect, this study develops an Enhanced Bird Swarm Algorithm with Deep Learning based Electroencephalography Signal Analysis for Emotion Recognition (EBSADL-ESEG) technique. The ultimate aim of the EBSADL-ESEG technique lies in the recognition of emotions using the EEG signals accurately. To perform this, the EBSADL-ESEG technique initially extracts the statistical features from the EEG signals. In addition, the EBSA technique is employed for optimal feature selection process. Moreover, the gated recurrent unit (GRU) with root mean square propagation (RMSProp) optimizer is utilized for classifying distinct emotions (arousal, valence, and liking). The experimental analysis of the EBSADL-ESEG model is tested on DEAP dataset and the outcomes are investigated under diverse measures. The comprehensive comparison study revealed better outcomes of the EBSADL-ESEG model over other DL models.

Optimal deep transfer learning based ethnicity recognition on face images

In recent times, deep learning driven face image analysis has gained significant interest among several application areas like surveillance, security, biometrics, etc. The facial analysis intends to compute facial soft biometrics like ethnicity, expression, identification, age, gender, and so on. Among several biometrics, ethnicity recognition remains a hot research area. Recent advancements in computer vision (CV) and artificial intelligence (AI) models form the basis of an effective design of ethnicity recognition models. With this motivation, this paper introduces a novel Harris Hawks optimization with deep transfer learning based fusion model for face ethnicity recognition (HHODTLF-FER) model. The proposed HHODTLF-FER model is to determine the different kinds of ethnicity for applied facial images. A fusion of three pre-trained DL models, namely VGG16, Inception v3, and capsule networks (CapsNet) models, are employed. In addition, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. Finally, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work. In order to ensure the improved recognition performance of the HHODTLF-FER model, a wide ranging experimental analysis is performed using benchmark databases. The comprehensive comparative study highlighted the promising performance of the HHODTLF-FER model over the other approaches. • To develop an ethnicity recognition on face images using harris hawks optimization basd deep transfer learning. • To improve the classification performance, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. • To improve the classification performance, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work.

A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition

Next-generation communication systems will face new challenges related to efficiently managing the available resources, such as the radio spectrum. DL is one of the optimization approaches to address and solve these challenges. However, there is a gap between research and industry. Most AI models that solve communication problems cannot be implemented in current communication devices due to their high computational capacity requirements. New approaches seek to reduce the size of DL models through quantization techniques, changing the traditional method of operations from a 32 (or 64) floating-point representation to a fixed point (usually small) one. However, there is no analytical method to determine the level of quantification that can be used to obtain the best trade-off between the reduction of computational costs and an acceptable accuracy in a specific problem. In this work, we propose an analysis methodology to determine the degree of quantization in a DNN model to solve the problem of AMR in a radio system. We use the Brevitas framework to build and analyze different quantized variants of the DL architecture VGG10 adapted to the AMR problem. The evaluation of the computational cost is performed with the FINN framework of Xilinx Research Labs to obtain the computational inference cost. The proposed design methodology allows us to obtain the combination of quantization bits per layer that provides an optimal trade-off between the model performance (i.e., accuracy) and the model complexity (i.e., size) according to a set of weights associated with each optimization objective. For example, using the proposed methodology, we found a model architecture that reduced 75.8% of the model size compared to the non-quantized baseline model, with a performance degradation of only 0.06%.

Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.

The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient's panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant placement planning to find the correct implant position and eliminate surgical risks. This study aims to develop a deep learning-based model that detects missing teeth's position on a dataset segmented from CBCT images. Five hundred CBCT images were included in this study. After preprocessing, the datasets were randomized and divided into 70% training, 20% validation, and 10% test data. A total of six pretrained convolutional neural network (CNN) models were used in this study, which includes AlexNet, VGG16, VGG19, ResNet50, DenseNet169, and MobileNetV3. In addition, the proposed models were tested with/without applying the segmentation technique. Regarding the normal teeth class, the performance of the proposed pretrained DL models in terms of precision was above 0.90. Moreover, the experimental results showed the superiority of DenseNet169 with a precision of 0.98. In addition, other models such as MobileNetV3, VGG19, ResNet50, VGG16, and AlexNet obtained a precision of 0.95, 0.94, 0.94, 0.93, and 0.92, respectively. The DenseNet169 model performed well at the different stages of CBCT-based detection and classification with a segmentation accuracy of 93.3% and classification of missing tooth regions with an accuracy of 89%. As a result, the use of this model may represent a promising time-saving tool serving dental implantologists with a significant step toward automated dental implant planning.

Patient-specific daily updated deep learning auto-segmentation for MRI-guided adaptive radiotherapy

Background and purposeDeep Learning (DL) technique has shown great potential but still has limited success in online contouring for MR-guided adaptive radiotherapy (MRgART). This study proposed a patient-specific DL auto-segmentation (DLAS) strategy using the patient’s previous images and contours to update the model and improve segmentation accuracy and efficiency for MRgART. Methods and materialsA prototype model was trained for each patient using the first set of MRI and corresponding contours as inputs. The patient-specific model was updated after each fraction with all the available fractional MRIs/contours, and then used to predict the segmentation for the next fraction. During model training, a variant was fitted under consistency constraints, limiting the differences in the volume, length and centroid between the predictions for the latest MRI within a reasonable range. The model performance was evaluated for both organ-at-risks and tumors auto-segmentation for a total of 6 abdominal/pelvic cases (each with at least 8 sets of MRIs/contours) underwent MRgART through Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD95), and was compared with deformable image registration (DIR) and frozen DL model (no updating after pre-training). The contouring time was also recorded and analyzed. ResultsThe proposed model achieved superior performance with higher mean DSC (0.90, 95 % CI: 0.88–0.95), as compared to DIR (0.63, 95 %CI: 0.59–0.68) and frozen DL models (0.74, 95 % CI: 0.71–0.79). As for tumors, the proposed method yielded a median DSC of 0.95, 95 % CI: 0.94–0.97, and a median HD95 of 1.63 mm, 95 % CI: 1.22 mm-2.06 mm. The contouring time was reduced significantly (p < 0.05) using the proposed method (73.4 ± 6.5 secs) compared to the manual process (12 ∼ 22 mins). The online ART time was reduced to 1650 ± 274 seconds with the proposed method, as compared to 3251.8 ± 447 seconds using the original workflow. ConclusionThe proposed patient-specific DLAS method can significantly improve the segmentation accuracy and efficiency for longitudinal MRIs, thereby facilitating the routine practice of MRgART.

Estimating daily reference evapotranspiration using a novel hybrid deep learning model

Reference evapotranspiration (ET0) is usually employed for estimating actual crop ET together with crop coefficients (Kc). However, it is necessary to explore an alternative model to estimate ET0 concisely because of numerous limitations in the Penman–Monteith method. To improve the ET0 estimation accuracy using limited meteorological data, this study developed a novel hybrid deep learning model (d–LSTM) based on the meteorological data during 1961–2020 observed at fifty stations on the Loess Plateau, which used the Deep Belief Network (DBN) module to extract features from meteorological data and the Long Short–Term Memory (LSTM) module to expand time features and process data information with sequential features, respectively. Based on the comparative evaluation of ET0 estimation accuracy between the d–LSTM, DBN, LSTM, and nine empirical models, the results drawn from this study demonstrated that the d–LSTM model manifested the best performance as RH–based, Rn–based, and T–based ET0 estimating models. For local strategy, the value of R2, NSE, RMSE, MAPE, and GPI ranging 0.941 ± 0.020, 0.940 ± 0.032, 0.436 ± 0.457 mm d–1, 0.150 ± 0.016, and 1.611 ± 0.180 for D–LSTM1 (RH–based), 0.944 ± 0.030, 0.943 ± 0.037, 0.423 ± 0.313 mm d–1, 0.119 ± 0.013, and 1.917 ± 0.155 for D–LSTM2 (Rn–based), and 0.902 ± 0.091, 0.891 ± 0.094, 0.558 ± 0.319 mm d–1, 0.181 ± 0.058, and 1.440 ± 0.550 for D–LSTM3 (T–based). For external strategy, the average value of R2, NSE, RMSE, MAPE, and GPI were 0.874, 0.872, 0.651 mm d–1, 0.159, and 1.837 for D–LSTM1, 0.894, 0.892, 0.591 mm d–1, 0.138, and 2.000 for D–LSTM2, and 0.839, 0.827, 0.768 mm d–1, 0.212, and 1.482 for D–LSTM3. Following, the LSTM performed better than DBN for local strategy, but vice versa for external strategy. Despite DL models outperforming RH–based and Rn–based empirical models, HS outperformed the DBN3 for local strategy, and was superior to LSTM3 for external strategy. Under limited meteorological data, the Rn–based ET0 estimating models are superior to RH–based and T–based models, and RH–based achieved better accuracy than T–based for DL models, but vice versa for empirical models. There is significant spatial variability in the accuracy of daily ET0 models, but the high precision of the d–LSTM was stable on the Loess Plateau. Overall, the d–LSTM model, which combines the advantages of DBN and LSTM, is the most recommended ET0 model using incomplete meteorological data on the Loess Plateau, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling.

Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities

Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634.

A Multi-Layer Auto-Segmentation Quality Assurance and Correction Pipeline for MR-Guided Adaptive Radiotherapy

<h3>Purpose/Objective(s)</h3> Fast and accurate auto-segmentation on daily image is essential for MR-guided adaptive radiotherapy (MRgART). However, the state-of-the-art deep learning auto-segmentation (DLAS) still has limited success particularly for complex structures such as those in the abdomen. Inaccurate contours from DLAS need to be inspected and edited and this manual process can be labor intensive, time consuming and subject to human errors. This work aims to develop a multi-layer pipeline that includes an automatic contour quality assurance (ACQA) test, an automatic contour refinement (ACR) process, and an interactive semi-automatic segmentation (ISS) tool to quickly identify and correct for inaccurate auto-segmented contours, thus replacing the manual process in MRgART. <h3>Materials/Methods</h3> The proposed pipeline includes the ACQA, ACR and ISS modules that are developed to detect and correct for inaccurate auto-segmented contours in the following steps: (1) identifying contours/image slices of acceptable or with minor or major errors from the input auto-segmented contours using ACQA, (2) correcting the identified inaccurate contours/slices using ACR, (3) repeating steps (1) and (2) on the contours obtained from the previous ACR until the contours/slices become acceptable or the performance of ACR becomes saturated, and (4) correcting the un-correctable contours from ACR using ISS tool. The steps 1-3 are designed to be fully automated. Details on ACQA, ACR and ISS are being investigated in separate studies. Briefly, the ACQA module uses either machine learning or DL classification algorithms based on organ and image properties to validate contour quality. The ACR module utilizes multiple correction methods, including an active contour model (ACM), U-Net and Dense UNet DL models to sequentially refine the inaccurate contours with minor and/or major errors. The ISS tool uses the accurate contours on an adjacent slice as prior knowledge informant to guide a 2D UNet DL model to predict contours on a subsequent slice. We have demonstrated the performance of the multi-layer pipeline on complex abdominal organs on various MRIs. <h3>Results</h3> The ACQA models achieved AUC/accuracy of 0.95/0.89 for pancreas and 0.92/0.88 for stomach contours. The ACM model improved 54%/49% of bowels/pancreas contours from major into minor errors and the UNet/Dense UNet ACR model improved 43%/33% of the minor bowel/stomach contours to be acceptable. The ISS tool achieved DSC of 0.92, 0.91, 0.91, 0.87, and 0.87 for colon, small bowel, stomach, pancreas, and duodenum. The expected execution time of the pipeline on an auto-segmented MRI was within 5 minutes. <h3>Conclusion</h3> This proposed multi-layer pipeline can complement deep learning auto-segmentation and can improve the segmentation efficiency and accuracy. As part of our on-going efforts, the pipeline is being implemented as a post DLAS process, substantially speeding up daily recontouring in MRgART.

Roman Urdu Sentiment Analysis Using Transfer Learning

Numerous studies have been conducted to meet the growing need for analytic tools capable of processing increasing amounts of textual data available online, and sentiment analysis has emerged as a frontrunner in this field. Current studies are focused on the English language, while minority languages, such as Roman Urdu, are ignored because of their complex syntax and lexical varieties. In recent years, deep neural networks have become the standard in this field. The entire potential of DL models for text SA has not yet been fully explored, despite their early success. For sentiment analysis, CNN has surpassed in accuracy, although it still has some imperfections. To begin, CNNs need a significant amount of data to train. Second, it presumes that all words have the same impact on the polarity of a statement. To fill these voids, this study proposes a CNN with an attention mechanism and transfer learning to improve SA performance. Compared to state-of-the-art methods, our proposed model appears to have achieved greater classification accuracy in experiments.

End-to-End Deep Convolutional Recurrent Models for Noise Robust Waveform Speech Enhancement.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

Optimal weighted fusion based insider data leakage detection and classification model for Ubiquitous computing systems

With the rapid growth and evolving advancement in artificial technology, Ubiquitous computing methods plays a vital role in day to day lives. Insider threats were malicious activities which are executed by an official worker within an institution. Insider threats indicate cybersecurity challenges for public and private companies, as an insider attack could cause widespread damages to company properties than exterior attacks. Many prevailing methods in the domain of insider threat concentrated on identifying general insider attack situations. But insider attacks are takes place in several ways, and one of the dangerous attack is a data leakage attack which is accomplished by a malicious insider before one leaves a company. This article develops a Metaheuristic with Weighted Fusion Based Insider Data Leakage Detection and Classification (MWF-IDLDC) Model for Ubiquitous Computing Systems. The presented MWF-IDLDC technique primarily performs pre-processing and feature extraction at the initial stage. In addition, the MWF-IDLDC technique derives a weighted fusion-based feature extraction approach comprising three DL methods namely long short-term memory (LSTM), gated recurrent unit (GRU), and stacked autoencoder (SAE). For optimally tuning the hyperparameters related to the DL models, the ant lion optimizer (ALO) algorithm is utilized in this study. The design of fusion process with ALO hyperparameter optimizer demonstrate the novelty of the work. The experimental validation of the MWF-IDLDC approach can be tested using a series of simulations and the outcomes were scrutinized under distinct aspects. The comparative analysis highlighted the improvements of the MWF-IDLDC method compared to recent approaches with maximum accuracy of 99.40%.

Training generalizable quantized deep neural nets

While a number of practical methods for training quantized DL models have been presented in the literature, there exists a critical gap in the theoretical generalizability results for such approaches. Although empirical evidence often suggests a high tolerance of DL architectures to variations of training procedures, existing theoretical generalization analyses are often contingent on the specific designs of training algorithms, e.g., in stochastic gradient descent (SGD). This specialization makes such generalizability results inapplicable to the case of quantized DL models. In view of this critical vacuum, this paper provides several almost-algorithm-independent results to ensure the generalizability of a quantized neural network at different levels of optimality. These results include the characterizations of a computable, quantized local solution that ensures the generalization performance and an algorithm that is provably convergent to such a local solution.

Genomics transformer for diagnosing Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease and presents a complex etiology with genomic and environmental factors and no recognized cures. Genotype data, such as single nucleotide polymorphisms (SNPs), could be used as a prodromal factor for early detection of PD. However, the polygenic nature of PD presents a challenge as the complex relationships between SNPs towards disease development are difficult to model. Traditional assessment methods such as polygenic risk scores and machine learning approaches struggle to capture the complex interactions present in the genotype data, thus limiting their discriminative capabilities in diagnosis. On the other hand, deep learning models are better suited for this task. Nevertheless, they encounter difficulties of their own such as a lack of interpretability. To overcome these limitations, in this work, a novel transformer encoder-based model is introduced to classify PD patients from healthy controls based on their genotype. This method is designed to effectively model complex global feature interactions and enable increased interpretability through the learned attention scores. The proposed framework outperformed traditional machine learning models and multilayer perceptron (MLP) baseline models. Moreover, visualization of the learned SNP-SNP associations provides not only interpretability to the model but also valuable insights into the biochemical pathways underlying PD development, which are corroborated by pathway enrichment analysis. Our results suggest novel SNP interactions to be further studied in wet lab and clinical settings.

Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction on residential buildings

In digital era, energy efficient building remains a hot research topic because of increasing concern regarding their environmental impact and energy consumption. Designing a suitable energy efficient building based on their layout namely overall areas, distribution of the glazing areas, orientation, height, and relative compactness. Such components directly impact the heating load (HL) and cooling load (CL) of residential buildings. A precise predicting of load facilitates effective management of energy consumption and improves the quality of life. Lately, several studies have been implemented to predict the CL and HL. The most significant and challenging parts of predictive are defining the effective input parameter and developing a higher accuracy predictive model. The accuracy of predictive model based on machine learning algorithm must be enhanced by hybrid model. With this motivation, this article introduces an Improved Harris Hawks Optimization with Hybrid Deep Learning Based Heating and Cooling Load Prediction (IHHOHDL-HCLP) model on Residential Buildings. The major aim of the IHHOHDL-HCLP model is to determine the CL and HL to accomplish effective energy utilization. To accomplish this, the IHHOHDL-HCLP primarily pre-processes the raw data in two levels namely min-max normalization and polynomial equation. In addition, the HDL model involves convolutional neural network (CNN) along with long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for HL and CL prediction process. Finally, the IHHO technique was applied for fine-tuning the hyperparameters related to the DL model. The IHHOHDL-HCLP model has demonstrated maximum prediction results with low RMSE values of 0.00874 and 0.00821, respectively, when applied to HL and CL, respectively. The experimental result analysis of the IHHOHDL-HCLP model demonstrates the better performance of the IHHOHDL-HCLP model over other DL models.

Deep Learning Model for Global Spatio-Temporal Image Prediction

Mathematical methods are the basis of most models that describe the natural phenomena around us. However, the well-known conventional mathematical models for atmospheric modeling have some limitations. Machine learning with Big Data is also based on mathematics but offers a new approach for modeling. There are two methodologies to develop deep learning models for spatio-temporal image prediction. On these bases, two models were built—ConvLSTM and CNN-LSTM—with two types of predictions, i.e., sequence-to-sequence and sequence-to-one, in order to forecast Aerosol Optical Thickness sequences. The input dataset for training was NASA satellite imagery MODAL2_E_AER_OD from Terra/MODIS satellites, which presents global Aerosol Optical Thickness with an 8 day temporal resolution from 2000 to the present. The obtained results show that the ConvLSTM sequence-to-one model had the lowest RMSE error and the highest Cosine Similarity value. The advantages of the developed DL models are that they can be executed in milliseconds on a PC, can be used for global-scale Earth observations, and can serve as tracers to study how the Earth’s atmosphere moves. The developed models can be used as transfer learning for similar image time-series forecasting models.

Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images.

COVID-19 pandemic has a significant impact on the global health and daily lives of people living over the globe. Several initial tests are based on the detecting of the genetic material of the coronavirus, and they have a minimum detection rate with a time-consuming process. To overcome this issue, radiological images are recommended where chest X-rays (CXRs) are employed in the diagnostic process. This article introduces a new Multi-modal fusion of deep transfer learning (MMF-DTL) technique to classify COVID-19. The proposed MMF-DTL model involves three main processes, namely pre-processing, feature extraction, and classification. The MMF-DTL model uses three DL models namely VGG16, Inception v3, and ResNet 50 for feature extraction. Since a single modality would not be adequate to attain an effective detection rate, the integration of three approaches by the use of decision-based multimodal fusion increases the detection rate. So, a fusion of three DL models takes place to further improve the detection rate. Finally, a softmax classifier is employed for test images to a set of six different. A wide range of experimental result analyses is carried out on the Chest-X-Ray dataset. The proposed fusion model is found to be an effective tool for COVID-19 diagnosis using radiological images with the average sensy of 92.96%, specy of 98.54%, precn of 93.60%, accuy of 98.80%, Fscore of 93.26% and kappa of 91.86%.