Herein, we produced POT polymer-based tungsten (IV) oxide, (WO 2 ), cerium (IV) oxide, (CeO 2 ) and iron (III) oxide (Fe 2 O 3 ) novel composite materials by emulsion polymerization and one pot blending techniques. UV–Vis analysis confirmed various electronic transitions in π–π* and n–π* levels. FT-IR spectra showed the integration of reinforcement particles within the polymer matrix by the engagement of free functionalities of the polymer by the metal oxide moieties. SEM analysis described the diverse morphology (spherical, pellets, porous and rod-shaped) of the synthesized materials while the complete dispersion of reinforcement particles in the polymer matrix. Extensive cyclic voltammetry studies were done and the materials were found to be used as electroactive agents as they deliver reversible anodic and cathode peaks. The materials are less resistive toward the mobility of ions thus offering good electrical and storage properties. The prepared are proposed to be efficient in electrode and capacitors production technologies.

Early detection of plant diseases is crucial before plant growth is affected. Plant diseases have been detected and classified using a variety of machine learning (ML) models in the past. Deep Learning (DL) appears to have great potential in terms of increased accuracy; however, in agricultural applications of Convolutional Neural Networks (CNN) has widely been utilised by researchers. CNNs are so effective at identifying plant species, managing yields, detecting weeds, managing soil, and water, counting fruits, detecting diseases and pests, and evaluating plant nutrient status. A farmer can diagnose plant diseases quickly and accurately with an automated disease detection system. To speed up crop diagnosis, plant leaf disease detection systems must be automated. In this paper, we evaluated twelve different models on a new plant diseases dataset and demonstrated that the most accurate model was Densenet169. In training and validation, the accuracy was 97.2% and 97.8%, respectively.

The presence of dyes in water stream is a major environmental problem that affects aquatic and human life negatively. Therefore, it is essential to remove dye from wastewater before its discharge into the water bodies. In this study, Banyan (Ficus benghalensis, F. benghalensis) tree leaves, a low-cost biosorbent, were used to remove brilliant green (BG), a cationic dye, from an aqueous solution. Batch model experiments were carried out by varying operational parameters, such as initial concentration of dye solution, contact time, adsorbent dose, and pH of the solution, to obtain optimum conditions for removing BG dye. Under optimum conditions, maximum percent removal of 97.3% and adsorption capacity (Qe) value of 19.5 mg/g were achieved (at pH 8, adsorbent dose 0.05 g, dye concentration 50 ppm, and 60 min contact time). The Langmuir and Freundlich adsorption isotherms were applied to the experimental data. The linear fit value, R2 of Freundlich adsorption isotherm, was 0.93, indicating its best fit to our experimental data. A kinetic study was also carried out by implementing the pseudo-first-order and pseudo-second-order kinetic models. The adsorption of BG on the selected biosorbent follows pseudo-second-order kinetics (R2 = 0.99), indicating that transfer of internal and external mass co-occurs. This study surfaces the excellent adsorption capacity of Banyan tree leaves to remove cationic BG dye from aqueous solutions, including tap water, river water, and filtered river water. Therefore, the selected biosorbent is a cost-effective and easily accessible approach for removing toxic dyes from industrial effluents and wastewater.

Nature provides living creatures with the material resources to fill up their bio needs. From that point of inception, earth has been catering to the needs of homo-sapiens but, now, it has become worn out and exhausted because of the carelessness of its inhabitants and so requires vigorous attention. Many challenges are weathering upon her and various other health issues are also balancing out the natural phenomenon. To cure and wipe out the threats posed by diseases and challenges, the first and foremost step is to diagnose the issues and cure them by creating 4wthe awareness and making effective legislation. The diagnosis will lead towards remedies. The cure can best be served collectively, by strong legislation and effective mechanism. This research paper analyses environmental issues and examines the Laws dealing with them. The methodology used in this study is doctrinal employing an exploratory approach.

The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (σ2) of accuracy is 3.5 and the deviation (σ) is 1.87.

Online learning platforms such as Massive Open Online Course (MOOC), Virtual Learning Environments (VLEs), and Learning Management Systems (LMS) facilitate thousands or even millions of students to learn according to their interests without spatial and temporal constraints. Besides many advantages, online learning platforms face several challenges such as students’ lack of interest, high dropouts, low engagement, students’ self-regulated behavior, and compelling students to take responsibility for settings their own goals. In this study, we propose a predictive model that analyzes the problems faced by at-risk students, subsequently, facilitating instructors for timely intervention to persuade students to increase their study engagements and improve their study performance. The predictive model is trained and tested using various machine learning (ML) and deep learning (DL) algorithms to characterize the learning behavior of students according to their study variables. The performance of various ML algorithms is compared by using accuracy, precision, support, and f-score. The ML algorithm that gives the best result in terms of accuracy, precision, recall, support, and f-score metric is ultimately selected for creating the predictive model at different percentages of course length. The predictive model can help instructors in identifying at-risk students early in the course for timely intervention thus avoiding student dropouts. Our results showed that students’ assessment scores, engagement intensity i.e. clickstream data, and time-dependent variables are important factors in online learning. The experimental results revealed that the predictive model trained using Random Forest (RF) gives the best results with averaged precision =0.60%, 0.79%, 0.84%, 0.88%, 0.90%, 0.92%, averaged recall =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, averaged F-score =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91%, and average accuracy =0.59%, 0.79%, 0.84%, 0.88%, 0.90%, 0.91% at 0%, 20%, 40%, 60%, 80% and 100% of course length.

Breast cancer is one of the common disease in female gender population all over the world. The classical methods of segmentation and classification for malignant cells are not only repetitive but also very time-consuming. Therefore, a computer-aided diagnosis is needed for automatic segmentation and classification of malignant cells in breast cytology images. In this article, a machine learning-based approach is proposed for malignant cell segmentation and classification in breast cytology images. In the proposed approach, the segmentation of cells is performed by a level set algorithm which is used to extract statistical information related to the malignant and benign cells. Similarly, the gray level co-occurrence matrix is computed to exploit the texture information, and support vector machine-based classification is used for the classification of malignant and benign cells. It has been observed through experiments that the proposed approach achieved high accuracy (96.3%) in the classification of malignant and benign cells.

In this study, for the first-time, aqueous solution of Acid Fuchsin (AF) was spectrophotometrically evaluated as a possible chemical dosimeter for food irradiation dosimetry at low dose ranges. A 50 µM solution of AF at natural pH was gamma irradiated and absorbance of the solution was measured at λmax (i.e., 543 nm) in addition to other wavelengths (490, 510, 549 and 564 nm). The response of AF dosimeter was investigated by plotting various variables, i.e., absorbance (A), − log A, change in absorbance (∆A), log Ao/Ai and absorbance % (A %) against absorbed dose. The response plots suggested that the beneficial dose range of AF solution in water was up to 0.82 kGy when absorbance (A), change in absorbance (ΔA) and absorbance % (A %) were extrapolated against absorbed dose. Though, the response was linear and beneficial absorbed dose range was extended up to 1.65 kGy, when − log A and log Ao/Ai were plotted against absorbed dose. It was concluded from pre- and post-irradiation stability studies of AF dosimeter that it should be protected from light and heat during handling and storage. For detection of the reactive specie involved in the bleaching of AF dye the AF dye solution was saturated with O2, N2 and N2O gases.

•A deep learning-based framework is proposed for the classification of breast cancer in breast cytology images.•Three different deep learning architectures (GoogLeNet, VGGNet, and ResNet) have been analysed.•The proposed framework gives a high level of accuracy in the classification of breast cancer.

One of the primary causes of mortality among women aged 20–59 worldwide is breast cancer. Early detection and getting proper treatment can reduce the rate of morbidity of breast cancer. In this paper, we proposed a framework which combines machine learning and computational intelligence-based approaches in e-Health care service as an application of the Internet of Medical Things (IoMT) technology, for the early detection and classification of malignant cells in breast cancer. In the proposed approach, the detection of malignant cells is achieved by extracting various shapes and textured based features, whereas the classification is performed using three well-known classification algorithms. The most innovative part of the proposed approach is the use of Evolutionary Algorithms (EA) for the selection of optimal features, which reduces the computational complexity and accelerates the classification process in cloud-based e-Health care service. Similarly, an ensemble based classifier is used to select the best classifier by adopting the majority voting technique. The performance of the proposed approach is validated through experiments on real data sets which provide an accuracy of 98.0% in the detection and classification of malignant cells in breast cytology images.

The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

Statistical analysis of cells in breast cytology images is very important for the diagnosis of various diseases in the female population in developed and developing countries. Manual detection and counting of the cancer cell in real time is not only difficult but hugely time-consuming for pathologists. In this paper, we propose an algorithm for automatic analysis of breast cytology using Fine Needle Aspiration Cytology (FNAC) images. The proposed technique uses statistical measures which include perceptual information (like color) and morphological characteristics for the estimation of the initial cell boundary. Similarly, the level set technique is used for efficient and accurate identification of cellular objects which help in the precise counting of individual cancer cell breast cytology images. Experimental results obtained during the demonstration of the proposed approach show high correlations in precision with manual counting by a pathologist. It has been proved that the proposed approach is efficient in processing cells for counting the cancerous cells with high accuracy and avoid discrepancies(like color variations, human error) in manual counting by a pathologist.

In the present study, mixed-metal ceramic Fe0.01Al0.5La.0.01Zn0.98O particles and their composites with polyaniline (PANI) were prepared via sol-gel and in situ free-radical polymerization techniques, respectively. Particles and composite formation was confirmed by FT-IR spectroscopy. SEM studies showed the Fe0.01Al0.5La.0.01Zn0.98O particle’s homogeneous dispersion in the polymer matrix. Ceramic particles were found to be in microdimensions. XRD analysis confirmed crystallite size in the range from 22 to 28 nm. Extensive rheological characterization was performed to check the durability of the materials for possible applications. Flow-curve tests suggested that the prepared materials are non-Newtonian (shear thinning) in nature. Increasing temperature have no appreciable effect on the viscosity which confirmed the mechanical stability of the materials. Based on frequency sweep test findings, the mechanical rigidity of the polymer has been enhanced (G′ = 2 × 102−5.22 × 103 Pa) by the introduction of ceramic particles. Conversely, creep compliance has been decreased considerably.