Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.     

Suitability of VVC and HEVC for Video Telehealth Systems

Video compression in medical video streaming is one of the key technologies associated with mobile healthcare. Seamless delivery of medical video streams over a resource constrained network emphasizes the need of a video codec that requires minimum bitrates and maintains high perceptual quality. This paper presents a comparative study between High Efficiency Video Coding (HEVC) and its potential successor Versatile Video Coding (VVC) in the context of healthcare. A large-scale subjective experiment comprising of twenty-four non-expert participants is presented for eight different test conditions in Full High Definition (FHD) videos. The presented analysis highlights the impact of compression artefacts on the perceptual quality of HEVC and VVC processed videos. Our results and findings show that VVC clearly outperforms HEVC in terms of achieving higher compression, while maintaining high quality in FHD videos. VVC requires upto 40% less bitrate for encoding an FHD video at excellent perceptual quality. We have provided rate-quality curves for both encoders and a degree of overlap across both codecs in terms of perceptual quality. Overall, there is a 71% degree of overlap in terms of quality between VVC and HEVC compressed videos for eight different test conditions.     

Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem. In order to solve this problem, we propose a multi-channel Pyramidal neural convolutional (MP-CNN) network that requires a less number of learnable parameters. Using the deep CNN model, we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal. We validate the robustness and effectiveness of proposed AADS using KDD, a benchmark dataset for alcoholism detection problem. In order to find the brain region that contributes significant role in AAD, we investigated the effects of selected 19 EEG channels (SC-19), those from the whole brain (ALL-61), and 05 brain regions, i.e., TEMP, OCCIP, CENT, FRONT, and PERI. The results show that SC-19 contributes significant role in AAD with the accuracy of 100%. The comparison reveals that the state-of-the-art systems are outperformed by the AADS. The proposed AADS will be useful in medical diagnosis research and health care systems.     

Prediction of Cloud Ranking in a Hyperconverged Cloud Ecosystem Using Machine Learning

Cloud computing is becoming popular technology due to its functional properties and variety of customer-oriented services over the Internet. The design of reliable and high-quality cloud applications requires a strong Quality of Service QoS parameter metric. In a hyperconverged cloud ecosystem environment, building high-reliability cloud applications is a challenging job. The selection of cloud services is based on the QoS parameters that play essential roles in optimizing and improving cloud rankings. The emergence of cloud computing is significantly reshaping the digital ecosystem, and the numerous services offered by cloud service providers are playing a vital role in this transformation. Hyperconverged software-based unified utilities combine storage virtualization, compute virtualization, and network virtualization. The availability of the latter has also raised the demand for QoS. Due to the diversity of services, the respective quality parameters are also in abundance and need a carefully designed mechanism to compare and identify the critical, common, and impactful parameters. It is also necessary to reconsider the market needs in terms of service requirements and the QoS provided by various CSPs. This research provides a machine learning-based mechanism to monitor the QoS in a hyperconverged environment with three core service parameters: service quality, downtime of servers, and outage of cloud services.     

Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.     

3D Semantic Deep Learning Networks for Leukemia Detection

White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body’s immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of different metrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness.     

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation. Then, the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted–-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.     

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of CNN. Therefore, an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance. In this process, a hierarchical CNN model is designed, in which the former model is non-regularized (due to dense architecture) and the later one is regularized, specifically designed for small histopathology images. Moreover, the regularized model is integrated with CNN’s basic architecture to reduce overfitting. Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training. The training and testing accuracies of the non-regularized CNN model are 98% & 78% with an imbalanced dataset and 96% & 81% with a balanced dataset, respectively. The regularized CNN model training and testing accuracies are 84% & 75% for an imbalanced dataset and 87% & 86% for a balanced dataset.     

Fabrication of Mn–ZnO photoanodes for photoelectrochemical water splitting applications

Journal of Materials Science: Materials in Electronics - A photoelectrochemical (PEC) water splitting ability of pure ZnO and manganese-incorporated ZnO thin films fabricated via a simple...     

Enhancement in Physical Properties of Silver-Doped Fe–Ni Invar Nano-alloy Using Chemical Reduction Method

Journal of Superconductivity and Novel Magnetism - Silver-substituted Fe–Ni nano invar alloy is a new and innovative field of research due to their interesting invar, magnetic and electrical...