Development of Highly Flexible Piezoelectric PVDF-TRFE/Reduced Graphene Oxide Doped Electrospun Nano-Fibers for Self-Powered Pressure Sensor.

The demand for self-powered, flexible, and wearable electronic devices has been increasing in recent years for physiological and biomedical applications in real-time detection due to their higher flexibility and stretchability. This work fabricated a highly sensitive, self-powered wearable microdevice with Poly-Vinylidene Fluoride-Tetra Fluoroethylene (PVDF-TrFE) nano-fibers using an electrospinning technique. The dielectric response of the polymer was improved by incorporating the reduced-graphene-oxide (rGO) multi-walled carbon nano-tubes (MWCNTs) through doping. The dielectric behavior and piezoelectric effect were improved through the stretching and orientation of polymeric chains. The outermost layer was attained by chemical vapor deposition (CVD) of conductive polymer poly (3,4-ethylenedioxythiophene) to enhance the electrical conductivity and sensitivity. The hetero-structured nano-composite comprises PVDF-TrFE doped with rGO-MWCNTs over poly (3,4-ethylenedioxythiophene) (PEDOT), forming continuous self-assembly. The piezoelectric pressure sensor is capable of detecting human physiological vital signs. The pressure sensor exhibits a high-pressure sensitivity of 19.09 kPa-1, over a sensing range of 1.0 Pa to 25 kPa, and excellent cycling stability of 10,000 cycles. The study reveals that the piezoelectric pressure sensor has superior sensing performance and is capable of monitoring human vital signs, including heartbeat and wrist pulse, masticatory movement, voice recognition, and eye blinking signals. The research work demonstrates that the device could potentially eliminate metallic sensors and be used for early disease diagnosis in biomedical and personal healthcare applications.

Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ).

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models.

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical-chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft's persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

Effect of academic stress, educational environment on academic performance & quality of life of medical & dental students; gauging the understanding of health care professionals on factors affecting stress: A mixed method study.

INTRODUCTION: Throughout their academic careers, medical and dental students face challenges that cause varying levels of stress, affecting their academic performance and quality of life (QoL). Our study aims to ascertain the effect of academic stress and the educational environment on the QoL and academic performance of medical and dental students, encompassing the perspectives of both students and healthcare professionals. METHODS: A mixed-method research was conducted from February to May 2022, comprising students from a medical and dental college in Pakistan. During Phase 1, the students participated in the cross-sectional survey and completed the WHO Quality of Life Scale (WHOQOL-BREF), Academic Stress Scale, and Dundee Ready Educational Environment Measure (DREEM) Inventory questionnaires. Academic performance was evaluated through last year's annual assessment results of the students. During Phase 2 of the study, interviews with healthcare professionals who had experience as the students' counsellors were conducted. RESULTS: The mean age of the sample (n = 440) was 22.24 ±1.4 years. The Cronbach Alpha reliability of the DREEM inventory was 0.877, that of the Academic Stress Scale was 0.939 and the WHOQOL scale was 0.895. More than half of the students (n = 230, 52.3%) reported better QoL and the majority perceived a positive educational environment (n = 323, 73.4%) and higher academic stress (n = 225, 51.1%). Males had significantly more academic stress (p<0.05). Those who perceived a positive educational environment and better QoL had better academic performance (p<0.05). Academic performance was positively and significantly correlated with QoL and academic stress (p = 0.000). In qualitative analysis, 112 codes were generated which converged into 5 themes: challenging educational environment, psychological need and support, individual differences, relationship and family life, and adjustment issues. CONCLUSION: Medical and dental students encounter a myriad of challenges, along with significant academic stress, which detrimentally affects their academic performance, despite perceiving a positive educational environment. Conversely, a better QoL is associated with improved academic performance.

Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hundreds of people are injured or killed in road accidents. These accidents are caused by several intrinsic and extrinsic factors, including the attentiveness of the driver towards the road and its associated features. These features include approaching vehicles, pedestrians, and static fixtures, such as road lanes and traffic signs. If a driver is made aware of these features in a timely manner, a huge chunk of these accidents can be avoided. This study proposes a computer vision-based solution for detecting and recognizing traffic types and signs to help drivers pave the door for self-driving cars. A real-world roadside dataset was collected under varying lighting and road conditions, and individual frames were annotated. Two deep learning models, YOLOv7 and Faster RCNN, were trained on this custom-collected dataset to detect the aforementioned road features. The models produced mean Average Precision (mAP) scores of 87.20% and 75.64%, respectively, along with class accuracies of over 98.80%; all of these were state-of-the-art. The proposed model provides an excellent benchmark to build on to help improve traffic situations and enable future technological advances, such as Advance Driver Assistance System (ADAS) and self-driving cars.

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM.

The constantly evolving human-machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain-computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images.

Novel Coronavirus is deadly for humans and animals. The ease of its dispersion, coupled with its tremendous capability for ailment and death in infected people, makes it a risk to society. The chest X-ray is conventional but hard to interpret radiographic test for initial diagnosis of coronavirus from other related infections. It bears a considerable amount of information on physiological and anatomical features. To extract relevant information from it can occasionally become challenging even for a professional radiologist. In this regard, deep-learning models can help in swift, accurate and reliable outcomes. Existing datasets are small and suffer from the balance issue. In this paper, we prepare a relatively larger and well-balanced dataset as compared to the available datasets. Furthermore, we analyze deep learning models, namely, AlexNet, SqueezeNet, DenseNet201, MobileNetV2 and InceptionV3 with numerous variations such as training the models from scratch, fine-tuning without pre-trained weights, fine-tuning along with updating pre-trained weights of all layers, and fine-tuning with pre-trained weights along with applying augmentation. Our results show that fine-tuning with augmentation generates best results in pre-trained models. Finally, we have made architectural adjustments in MobileNetV2 and InceptionV3 models to learn more intricate features, which are then merged in our proposed ensemble model. The performance of our model is statistically analyzed against other models using four different performance metrics with paired two-sided t-test on 5 different splits of training and test sets of our dataset. We find that it is statistically better than its competing methods for the four metrics. Thus, the computer-aided classification based on the proposed model can assist radiologists in identifying coronavirus from other related infections in chest X-rays with higher accuracy. This can help in a reliable and speedy diagnosis, thereby saving valuable lives and mitigating the adverse impact on the socioeconomics of our community.

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM.

In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time-consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best-selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review.

Breast Cancer is a common dangerous disease for women. Around the world, many women have died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues, there are several techniques and methods. The image processing, machine learning, and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to save a women's life. To detect the breast masses, microcalcifications, and malignant cells,different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for breast cancer survival, it is essential to improve the methods or techniques to diagnose it at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are also challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

Intelligent microscopic approach for identification and recognition of citrus deformities.

Plant diseases are accountable for economic losses in an agricultural country. The manual process of plant diseases diagnosis is a key challenge from last one decade; therefore, researchers in this area introduced automated systems. In this research work, automated system is proposed for citrus fruit diseases recognition using computer vision technique. The proposed method incorporates five fundamental steps such as preprocessing, disease segmentation, feature extraction and reduction, fusion, and classification. The noise is being removed followed by a contrast stretching procedure in the very first phase. Later, watershed method is applied to excerpt the infectious regions. The shape, texture, and color features are subsequently computed from these infection regions. In the fourth step, reduced features are fused using serial-based approach followed by a final step of classification using multiclass support vector machine. For dimensionality reduction, principal component analysis is utilized, which is a statistical procedure that enforces an orthogonal transformation on a set of observations. Three different image data sets (Citrus Image Gallery, Plant Village, and self-collected) are combined in this research to achieving a classification accuracy of 95.5%. From the stats, it is quite clear that our proposed method outperforms several existing methods with greater precision and accuracy.

Lungs nodule detection framework from computed tomography images using support vector machine.

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps-playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well-known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion.

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK-1, ISIC MSK-2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.

Synthesis and evaluation of 5,6-disubstituted thiopyrimidine aryl aminothiazoles as inhibitors of the calcium-activated chloride channel TMEM16A/Ano1.

Transmembrane protein 16A (TMEM16A), also called Ano1, is a Ca(2+) activated Cl(-) channel expressed widely in mammalian epithelia, as well as in vascular smooth muscle and some tumors and electrically excitable cells. TMEM16A inhibitors have potential utility for treatment of disorders of epithelial fluid and mucus secretion, hypertension, some cancers and other diseases. 4-Aryl-2-amino thiazole T16Ainh-01 was previously identified by high-throughput screening. Here, a library of 47 compounds were prepared that explored the 5,6-disubstituted pyrimidine scaffold found in T16Ainh-01. TMEM16A inhibition activity was measured using fluorescence plate reader and short-circuit current assays. We found that very little structural variation of T16Ainh-01 was tolerated, with most compounds showing no activity at 10 µM. The most potent compound in the series, 9bo, which substitutes 4-methoxyphenyl in T16Ainh-01 with 2-thiophene, had IC50 ∼1 µM for inhibition of TMEM16A chloride conductance.

Reverse engineering Boolean networks: from Bernoulli mixture models to rule based systems.

A Boolean network is a graphical model for representing and analyzing the behavior of gene regulatory networks (GRN). In this context, the accurate and efficient reconstruction of a Boolean network is essential for understanding the gene regulation mechanism and the complex relations that exist therein. In this paper we introduce an elegant and efficient algorithm for the reverse engineering of Boolean networks from a time series of multivariate binary data corresponding to gene expression data. We call our method ReBMM, i.e., reverse engineering based on Bernoulli mixture models. The time complexity of most of the existing reverse engineering techniques is quite high and depends upon the indegree of a node in the network. Due to the high complexity of these methods, they can only be applied to sparsely connected networks of small sizes. ReBMM has a time complexity factor, which is independent of the indegree of a node and is quadratic in the number of nodes in the network, a big improvement over other techniques and yet there is little or no compromise in accuracy. We have tested ReBMM on a number of artificial datasets along with simulated data derived from a plant signaling network. We also used this method to reconstruct a network from real experimental observations of microarray data of the yeast cell cycle. Our method provides a natural framework for generating rules from a probabilistic model. It is simple, intuitive and illustrates excellent empirical results.