A Multimodal Deep Learning Approach for Identification of ‎Severity of Reflective Depression ‎

Social media consumes a greate time of our dialy times that generate a significant amount of information through expressing feeling and activities, sharing admiral contents, viewing, and more. This information mostly contains valuable discoveries. Despite many attempts to mining such produced data, it is still unexploited in certain issues and attracts many research areas. In this paper, we use the data extracted from social media from female’s pages to detect possibility of depression. A new deep learning model based on the psycholinguistic vocabulary to create the embedding words is developed. First, we extract the features from the data before and after the preprocessing phase. Second, the Convolutional Neural Network (CNN) is used to label the data for extracting the remaining features. Based on the previouse two phases; the developed model succeeded to predict the depression possibilty. Adetailed comparative analysis is also presented for the evaluation of the proposed system. The proposed indicator model proved promising results in predicting depression

Artificial Rabbit Optimizer with deep learning for fall detection of disabled people in the IoT Environment

Fall detection (FD) for disabled persons in the Internet of Things (IoT) platform contains a combination of sensor technologies and data analytics for automatically identifying and responding to samples of falls. In this regard, IoT devices like wearable sensors or ambient sensors from the personal space role a vital play in always monitoring the user's movements. FD employs deep learning (DL) in an IoT platform using sensors, namely accelerometers or depth cameras, to capture data connected to human movements. DL approaches are frequently recurrent neural networks (RNNs) or convolutional neural networks (CNNs) that have been trained on various databases for recognizing patterns connected with falls. The trained methods are then executed on edge devices or cloud environments for real-time investigation of incoming sensor data. This method differentiates normal activities and potential falls, triggering alerts and reports to caregivers or emergency numbers once a fall is identified. We designed an Artificial Rabbit Optimizer with a DL-based FD and classification (ARODL-FDC) system from the IoT environment. The ARODL-FDC approach proposes to detect and categorize fall events to assist elderly people and disabled people. The ARODL-FDC technique comprises a four-stage process. Initially, the preprocessing of input data is performed by Gaussian filtering (GF). The ARODL-FDC technique applies the residual network (ResNet) model for feature extraction purposes. Besides, the ARO algorithm has been utilized for better hyperparameter choice of the ResNet algorithm. At the final stage, the full Elman Neural Network (FENN) model has been utilized for the classification and recognition of fall events. The experimental results of the ARODL-FDC technique can be tested on the fall dataset. The simulation results inferred that the ARODL-FDC technique reaches promising performance over compared models concerning various measures

Reducing the BGP convergence time through different algorithms

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonOne of the many notable qualities of the Internet is its evolution from the interconnection of a small number of independent networks to its current size. Border gateway protocol (BGP) is the only routing protocol between different networks on the Internet. The programmable and open nature of BGP routing policies provides an adaptable protocol. However, the down side of this flexibility is that there are no limits on BGP convergence time. The overall aim of the study is to introduce a mechanism that could balance a reduction in BGP convergence time together with handling the number of BGP messages exchanged during convergence. In this thesis architectural designs as well as a simulation model for Fight, Flight, or Freeze BGP (FFF BGP) are initiated. One delay cause employed was a timer called the Minimum Route Advertisement Interval (MRAI), which is a built-in mechanism. It was set to 30 seconds, forcing the BGP routers to wait at least this time before sending advertisements for the same prefixes. Therefore, an optimum value for the MRAI timer was proposed, which improves the convergence process and does not harm the scalability. The second built-in mechanism, Route Flap Damping (RFD) is designed to detect and suppress flapping or insatiable routes. RFD severely penalises some sites for being well connected because of the high number of update messages exchanged by these sites, which results in operators disabling RFD. A new half-life was proposed to increase the stability and encourge operators to enable the RFD. An FFF BGP mechanism was used to speed up the process in the case of reachability information changes. This mechanism updates the KEEPALIVE message timer to detect any change within the reachability information faster and to deal with it, inspired by the FFF response mechanism. The two built-in mechanisms (MRAI and RFD) used with this proposed paradigm are the edited mechanisms based on this thesis. Experimental results demonstrate the advantage of the FFF BGP mechanism performance. It effectively achieved less than 60% of the present convergence time, and the throughput and traffic were not affected negatively. Therefore, it is recommended that the FFF BGP be deployed in real life

RETRACTED: Hosni Mahmoud, H.A.; Alabdulkreem, E. Bidirectional Neural Network Model for Glaucoma Progression Prediction. <i>J. Pers. Med.</i> 2023, <i>13</i>, 390

The Journal of Personalized Medicine retracts the article Bidirectional Neural Network Model for Glaucoma Progression Prediction [...

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities&#x2019; transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities

Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis Using Nature Inspired Algorithm With Deep Learning Approach

Endoscopic image analysis has played a pivotal function in the diagnosis and management of gastrointestinal (GI) tract diseases. Gastrointestinal endoscopy is a medical procedure where a flexible tube with an endoscope (camera) is inserted into the GI tract to visualize the inner lining of the colon, esophagus, stomach, and small intestine. The videos and images attained during endoscopy provide valuable data for detecting and monitoring a large number of GI diseases. Computer-assisted automated diagnosis technique helps to achieve accurate diagnoses and provide the patient the relevant medical care. Machine learning (ML) and deep learning (DL) methods have been exploited to endoscopic images for classifying diseases and providing diagnostic support. Convolutional Neural Networks (CNN) and other DL algorithms can learn to discriminate between various kinds of GI lesions based on visual properties. This study presents an Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis using an inspired Algorithm with Deep Learning (EIAGTD-NIADL) technique. The EIAGTD-NIADL technique intends to examine the endoscopic images using nature nature-inspired algorithm with a DL model for gastrointestinal tract disease detection and classification. To pre-process the input endoscopic images, the EIAGTD-NIADL technique uses a bilateral filtering (BF) approach. For feature extraction, the EIAGTD-NIADL technique applies an improved ShuffleNet model. To improve the efficacy of the improved ShuffleNet model, the EIAGTD-NIADL technique uses an improved spotted hyena optimizer (ISHO) algorithm. Finally, the classification process is performed by the use of the stacked long short-term memory (SLSTM) method. The experimental outcomes of the EIAGTD-NIADL system can be confirmed on benchmark medical image datasets. The obtained outcomes demonstrate the promising results of the EIAGTD-NIADL approach over other models

Bone Cancer Detection and Classification Using Owl Search Algorithm With Deep Learning on X-Ray Images

Bone cancer is treated as a severe health problem, and, in many cases, it causes patient death. Early detection of bone cancer is efficient in reducing the spread of malignant cells and decreasing mortality. Since the manual detection process is a laborious task, it is needed to design an automated system to classify and identify the cancerous bone and the healthy bone. Therefore, this article develops an Owl Search Algorithm with a Deep Learning-Driven Bone Cancer Detection and Classification (OSADL-BCDC) technique. The OSADL-BCDC algorithm follows the principle of transfer learning with a hyperparameter tuning strategy for bone cancer detection. The OSADL-BCDC model employs Inception v3 as a pretrained model for the feature extraction process which does not necessitate a manual segmentation of X-ray images. Besides, the OSA is applied as a hyperparameter optimizer for enhancing the efficacy of the Inception v3 method. Finally, the long short-term memory (LSTM) approach is used for identifying the presence of bone cancer. The proposed OSADL-BCDC technique reduces diagnosis time and achieves faster convergence. The experimental analysis of the OSADL-BCDC algorithm is tested using a set of medical images and the outcomes were measured under different aspects. The comparison study highlighted the improved performance of the OSADL-BCDC model over existing algorithms

Spatio-Temporal Features Representation Using Recurrent Capsules for Monaural Speech Enhancement

Single-channel speech enhancement is important for modern communication systems and has received a lot of attention. A convolutional neural network (CNN) successfully learns feature representations from speech spectrograms but loses spatial information due to distortion, which is important for humans to understand speech. Speech feature learning is an important ongoing research to capture higher-level representations of speech that go beyond conventional techniques. By considering the hierarchical structure and temporal relationships within speech signals, capsule networks (CapsNets) have the potential to provide more expressive and context-aware feature representations. By considering the advantages of CapNets over CNN, this study presents a model for monaural speech enhancement that keeps spatial information in a capsule and uses dynamic routing to pass it to higher layers. Dynamic routing replaces the pooling recurrent hidden states to get speech features from the outputs of the capsule. Leveraging long-term contexts provides identification of the target speaker. Therefore, a gated recurrent layer, gated recurrent unit (GRU), or long-short-term memory (LSTM), is placed above the CNN module and next to the capsule module in the architecture. This makes it viable to extract spatial features and long-term temporal dynamics. The suggested convolutional recurrent CapNet performs better compared to the models based on CNNs and recurrent neural networks. The suggested speech enhancement produces considerably better speech quality and intelligibility. With the LibriSpeech and VoiceBank&#x002B;DEMAND databases, the suggested speech enhancement improves the intelligibility and quality by 18.33&#x0025; and (0.94) 36.82&#x0025; over the noisy mixtures

Virtual Reality in the Treatment of Patients with Overweight and Obesity: A Systematic Review

Obesity is one of the world’s most serious health issues. Therefore, therapists have looked for methods to fight obesity. Currently, technology-based intervention options in medical settings are very common. One such technology is virtual reality (VR) which has been used in the treatment of obesity since the late 1990s. The main objective of this study is to review the literature on the use of VR in the treatment of obesity and overweight to better understand the role of VR-based interventions in this field. To this end, four databases (PubMed, Medline, Scopus, and Web of Science) were searched for related publications from 2000 to 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 645 articles identified, 24 were selected. The main strength of this study is that it is the first systematic review to focus completely on the use of VR in the treatment of obesity. It includes most research in which VR was utilized to carry out the intervention. Although several limitations were detected in the reviewed studies, the findings of this review suggest that employing VR for self-monitoring of diet, physical activity, and/or weight is effective in supporting weight loss as well as improving satisfaction of body image and promoting health self-efficacy in overweight or obese persons

Farmland fertility algorithm based resource scheduling for makespan optimization in cloud computing environment

Resource scheduling (RS) for makespan optimization in a cloud computing (CC) environment is an important aspect of handling effective resources in the cloud. Makespan optimization defines the minimization of time required to complete a collection of tasks in a computational environment. In the context of CC, makespan optimization aims to reduce the overall time required to execute tasks while effectively allocating and managing resources. RS in CC is a difficult task because of the number and variation of resources accessible and the volatility of usage-patterns of the resource assuming that the resource setting is on the service providers. Therefore, this article presents a Farmland Fertility Algorithm based Resource Scheduling for Makespan Optimization (FFARS-MSO) in Cloud Computing Environment. The presented FFARS-MSO technique is mainly based on FFA, which is stimulated by the farmland fertility in nature where the farmers split the various regions of the farm based on soil quality, and thereby every region's soil quality is distinct from others. In addition, the presented FFARS-MSO technique is utilized for load balancing and uniform distribution of resources depending upon the demand. The simulation outcomes ensure that the FFARS-MSO approach has reached effectual resource allocation over other optimization algorithms