Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through ...deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.
The essential requirement to harness well-known renewable energy sources like wind energy, solar energy,
etc
. as a component of an overall plan to guarantee global power sustainability will require ...highly efficient, high power and energy density batteries to collect the derived electrical power and balance out variations in both supply and demand. Owing to the continuous exhaustion of fossil fuels, and ever increasing ecological problems associated with global warming, there is a critical requirement for searching for an alternative energy storage technology for a better and sustainable future. Electrochemical energy storage technology could be a solution for a sustainable source of clean energy. Sodium-ion battery (SIB) technology having a complementary energy storage mechanism to the lithium-ion battery (LIB) has been attracting significant attention from the scientific community due to its abundant resources, low cost, and high energy densities. Layered transition metal oxide (TMO) based materials for SIBs could be a potential candidate for SIBs among all other cathode materials. In this paper, we discussed the latest improvement in the various structures of the layered oxide materials for SIBs. Moreover, their synthesis, overall electrochemical performance, and several challenges associated with SIBs are comprehensively discussed with a stance on future possibilities. Many articles discussed the improvement of cathode materials for SIBs, and most of them have pondered the use of Na
x
MO
2
(a class of TMOs) as a possible positive electrode material for SIBs. The different phases of layered TMOs (Na
x
MO
2
; TM = Co, Mn, Ti, Ni, Fe, Cr, Al, V, and a combination of multiple elements) show good cycling capacity, structural stability, and Na
+
ion conductivity, which make them promising cathode material for SIBs. This review discusses and summarizes the electrochemical redox reaction, structural transformations, significant challenges, and future prospects to improve for Na
x
MO
2
. Moreover, this review highlights the recent advancement of several layered TMO cathode materials for SIBs. It is expected that this review will encourage further development of layered TMOs for SIBs.
Na
+
ion intercalated layered metal oxides have tremendous applications as the cathode materials for SIBs owing to their superior electrochemical performance compared to other types of cathode materials.
Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. ...Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.
Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate ...malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.
The pharmacological characteristics of phytochemicals have prompted a lot of interest in their application in disease management. Due to the high incidence of cancer related mortality and morbidity ...throughout the world; experiments have concentrated on identifying the anticancer potential of natural substances. Many phytochemicals such as flavonoids and their derivatives produced from food offer a variety of new anti-cancer agents which prevent the cancer progression. Taxifolin, a unique bioactive flavonoid, is a dietary component that has grabbed the interest of dietitians and medicinal chemists due to its wide range of health benefits. It is a powerful antioxidant with a well-documented effect in the prevention of several malignancies in humans. Taxifolin has shown promising inhibitory activity against inflammation, malignancies, microbial infection, oxidative stress, cardiovascular disease, and liver disease. Anti-cancer activity has been shown to be relatively significant than other activities investigated in vitro and in vivo with a little or no side effects to the normal healthy cells. In summary this review offers the synopsis of recent breakthroughs in the use of taxifolin as a cancer treatment, as well as mechanisms of action. However, to develop a medicine for human usage, more study on pharmacokinetic profile, profound molecular mechanisms, and drug safety criteria should be conducted utilizing well-designed randomized clinical trials.
Display omitted
•Chemistry, sources and pharmacokinetic perspectives of taxifolin.•Pharmacological activities of taxifolin.•Chemotherapeutic activities of taxifolin.
Dermoscopy images can be classified more accurately if skin lesions or nodules are segmented. Because of their fuzzy borders, irregular boundaries, inter- and intra-class variances, and so on, nodule ...segmentation is a difficult task. For the segmentation of skin lesions from dermoscopic pictures, several algorithms have been developed. However, their accuracy lags well behind the industry standard. In this paper, a modified U-Net architecture is proposed by modifying the feature map's dimension for an accurate and automatic segmentation of dermoscopic images. Apart from this, more kernels to the feature map allowed for a more precise extraction of the nodule. We evaluated the effectiveness of the proposed model by considering several hyper parameters such as epochs, batch size, and the types of optimizers, testing it with augmentation techniques implemented to enhance the amount of photos available in the PH2 dataset. The best performance achieved by the proposed model is with an Adam optimizer using a batch size of 8 and 75 epochs.
ABSTRACT
The detection of redshifted hyperfine line of neutral hydrogen (H i) is the most promising probe of the epoch of reionization (EoR). We report an analysis of 55 h of Murchison Widefield ...Array (MWA) phase II drift scan EoR data. The data correspond to a central frequency $\nu _0 = 154.24 \, \rm MHz$ (z ≃ 8.2 for the redshifted H i hyperfine line) and bandwidth $B = 10.24 \, \rm MHz$. As one expects greater system stability in a drift scan, we test the system stability by comparing the extracted power spectra from data with noise simulations and show that the power spectra for the cleanest data behave as thermal noise. We compute the H i power spectrum as a function of time in one and two dimensions. The best upper limit on the 1D power spectrum are: $\Delta ^2(k) \simeq (1000~\rm mK)^2$ at k ≃ 0.2h Mpc−1 and at k ≃ 1h Mpc−1. The cleanest modes, which might be the most suited for obtaining the optimal signal to noise, correspond to k ≳ 1h Mpc−1. We also study the time-dependence of the foreground-dominated modes in a drift scan and compare with the expected behaviour.
Extant literature has established the effectiveness of various mental health promotion and prevention strategies, including novel interventions. However, comprehensive literature encompassing all ...these aspects and challenges and opportunities in implementing such interventions in different settings is still lacking. Therefore, in the current review, we aimed to synthesize existing literature on various mental health promotion and prevention interventions and their effectiveness. Additionally, we intend to highlight various novel approaches to mental health care and their implications across different resource settings and provide future directions. The review highlights the (1) concept of preventive psychiatry, including various mental health promotions and prevention approaches, (2) current level of evidence of various mental health preventive interventions, including the novel interventions, and (3) challenges and opportunities in implementing concepts of preventive psychiatry and related interventions across the settings. Although preventive psychiatry is a well-known concept, it is a poorly utilized public health strategy to address the population's mental health needs. It has wide-ranging implications for the wellbeing of society and individuals, including those suffering from chronic medical problems. The researchers and policymakers are increasingly realizing the potential of preventive psychiatry; however, its implementation is poor in low-resource settings. Utilizing novel interventions, such as mobile-and-internet-based interventions and blended and stepped-care models of care can address the vast mental health need of the population. Additionally, it provides mental health services in a less-stigmatizing and easily accessible, and flexible manner. Furthermore, employing decision support systems/algorithms for patient management and personalized care and utilizing the digital platform for the non-specialists' training in mental health care are valuable additions to the existing mental health support system. However, more research concerning this is required worldwide, especially in the low-and-middle-income countries.
Object detection is a critical and complex problem in computer vision, and deep neural networks have significantly enhanced their performance in the last decade. There are two primary types of object ...detectors: two stage and one stage. Two-stage detectors use a complex architecture to select regions for detection, while one-stage detectors can detect all potential regions in a single shot. When evaluating the effectiveness of an object detector, both detection accuracy and inference speed are essential considerations. Two-stage detectors usually outperform one-stage detectors in terms of detection accuracy. However, YOLO and its predecessor architectures have substantially improved detection accuracy. In some scenarios, the speed at which YOLO detectors produce inferences is more critical than detection accuracy. This study explores the performance metrics, regression formulations, and single-stage object detectors for YOLO detectors. Additionally, it briefly discusses various YOLO variations, including their design, performance, and use cases.
PDF
