Context Based Classification of Reviews Using Association Rule Mining, Fuzzy Logics and Ontology

The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules

DEVELOPMENT AND VALIDATION OF RP-HPLC METHOD FOR THE DETERMINATION OF BAMIFYLLINE HYDROCHLORIDE IN TABLET DOSAGE FORM

Objective: The objective of the current study was to develop and validate a novel RP-HPLC method for determination of bamifylline hydrochloride in pharmaceutical dosage form.Methods: Chromatographic separation was conducted on Agilent technologies-1260 series with the G1311C quaternary pump, eclipse XDB C18 column (4.6 mm i.d. X 250 mm, 5 µm particle sizes) and equipped with photodiode array detector G1315D. Mobile phase consisted of methanol and acetonitrile were mixed in the ratio of 90:10 v/v, was used at a flow rate of 1 ml/min and detection wavelength was set at 263 nm.Results: The retention time for bamifylline hydrochloride was found to be 2.913 min. The calibration was linear (r2= 0.9996) in the concentration range of 2-10 µg/ml. The limit of detection and the limit of quantitation were found to be 0.4825 μg/ml and 1.4621 µg/ml respectively. Recovery of bamifylline hydrochloride in tablet formulation was observed in the range of 99.6-99.8 %. Percentage assay of bamifylline hydrochloride (Bamifix) was found to be 99.4 % w/w.Conclusion: Thus the novel proposed method for bamifylline hydrochloride was found to be feasible for the estimation of bamifylline hydrochloride in bulk as well as a pharmaceutical dosage form.Â

Customer segmentation using machine learning

Customer segmentation has seen major growth in all sectors in the last decade. Several techniques have been devised to analyze customer behavior through loyalty, purchases, recency, frequency and monetary to develop efficient marketing strategies that cater to each client individually. As the availability of products and services increases, so does the competition. With the spiraling of automation accompanied by its cost-effectiveness and ease of availability, all businesses equip themselves with the required workforce and machinery to conduct experiments such as customer segmentation on an industrial scale. In the proposed work, the datasets are manipulated by extracting features from existing attributes. A widespread approach is RFM that calculates the Recency, Frequency and Monetary values for each customer tuple. This paper aims at laying out a new approach at every step of customer segmentation from pre-processing, clustering, validation and suggesting marketing strategies for customer retention. Two datasets- Online Retail II Set and Mall Customer Segmentation are modelled and the results from analysis of both the datasets are presented and compared to reach a generalized opinion

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background: Skin diseases are reported to contribute 1.79\% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Objective: Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. Methods: We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. Results: The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. Conclusions: To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background:Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency.Objective:Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions.Methods:We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance.Results:The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet.Conclusions:To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

Equilibrium, Thermodynamic, and Kinetic Studies

Publisher Copyright: © 2022 Razia Sulthana et al.The economic viability of adsorbing crystal violet (CV) using pepper seed spent (PSS) as a biosorbent in an aqueous solution has been studied. A parametrical investigation was conducted considering parameters like initial concentration of dye, time of contact, pH value, and temperature variation. The analysis of experimental data obtained was carried out by evaluating with the isotherms of Freundlich, Sips, Tempkin, Jovanovic, Brouers-Sotolongo, Toth, Vieth-Sladek, Radke-Prausnitz, Langmuir, and Redlich-Peterson. The adsorption kinetics were studied by implementing the Dumwald-Wagner, Weber-Morris, pseudo-first-order, pseudo-second-order, film diffusion, and Avrami models. The experimental value of adsorption capacity (Qm=129.4 mg g-1) was observed to be quite close to the Jovanovic isotherm adsorption capacity (Qm=82.24 mg g-1) at (R2), coefficient of correlation of 0.945. The data validation was found to conform to that of pseudo-second-order and Avrami kinetic models. The adsorption process was specified as a spontaneous and endothermic process owing to the thermodynamic parametrical values of ΔG0, ΔH0, and ΔS0. The value of ΔH0 is an indicator of the process's physical nature. The adsorption of CV to the PSS was authenticated from infrared spectroscopy and scanning electron microscopy images. The interactions of the CV-PSS system have been discussed, and the observations noted suggest PSS as a feasible adsorbent to extract CV from an aqueous solution.publishersversionpublishe

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Not AvailableAround 20 plant sources were selected to analyze presence of phytochemicals (Alkaloids, Flavonoids, Glycosides, Steroids, Saponins, Resins, Phenols, Tannins, Terpenoids). For the analysis of Phytochemical, sources were extracted through methanolic extraction. Among the analyzed sources, Syzygiumcumini, Datura stramonium, Perriwinkle, Cassia angustifoliavahlandEucallyptus were found to be rich in phytochemicals and ranked first followed by Ashoka, English neem, Amla (small), Tulasi and Tecomagrandis were ranked second; Tamarindusindica, Castor, Amlthas, Teak and Amla (big) were ranked third; and Pipal, Pongamiapinata, Aloevera, Radish and Hibiscus as fourth. Presence of phytochemicals in these plants confers antimicrobial activity of the sources, which can be utilized as an applicant on textile material to incorporate antimicrobial property.CIWA- ICAR and PJTSA

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Not AvailableHaving high potentiality in dye yield, gamma irradiation has been found new ways of exploring. In this study, Eucalyptus bark powder and myrobalan treated fabrics are irradiated through different dosages to explore the effectiveness of the dye. The resultant consistency of extract was more with high intensity. Along with improved color fastness properties, the printed samples have shown high absorption levels and intensity of the shade. When analyzed, the cotton fabric dyed with a respective gamma irradiation dosage of Eucalyptus dye source, 500Gy possessed good color strength for after treatment with 3 percent Sodium chloride. Later, by using 500Gy Eucalyptus bark dye and fabric samples were printed with mordants (alum, FeSO4, CuSO4, and SnCl2) and were found to be having good color strength properties.Not Availabl

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Not AvailableWhile conducting different agricultural operations, farmers face major difficulty with dirt, dust and organic or other substrates, which obstructs or hinders their regular activity. From the first hand information, by conducting surveys, primary data were collected from the selected villages near Ranga Reddy district Hyderabad, Telangana. Cuts in hands, sever finger wounds, piercing of thorns and kellicks, etc were found in harvesting Okra, Cotton, Chilli,Sugarcane crops and cutting of Castor, Fodder, and weed. This was only due to minimal care taken by the farmer workers. To overcome these difficulties, present study was under taken to design and develop a functional clothing kit includes, Apron, Knitted gloves and Scarf. Developed functional clothing was assessed against existing practice for its suitability, comfortability and durability. From the analysis, the acceptance level of the functional clothing by farm workers was found to be very good without obstructing their farm activities.CIWA- ICAR and PJTSA

Reinventing Traditional Practices for Sustainable Dwelling Solutions in Kerala

The need for sustainable architectural solutions has been identified as a priority by many present-day researchers and purpose of this study is to understand the relevance of traditional architecture in modern context. This paper deals with the study of architectural practices, which are traditionally practiced in Kerala region of India and to derive with cost effective sustainable solutions for future dwellings. Kerala experiences tropical warm humid climate and the traditional architectural styles found to be the direct responsive mechanism and adaptable solutions, using the available resources. The vision of Laurie baker had created an impact on the field and added a new dimension to the social imagination. The idea of well-being with reference to the western standards is disturbing the essence of social as well as economical sustainability, and which threatens the environmental balance as a whole. As architectural scenario in Kerala is undergoing drastic directionless changes in present period, a few design thoughts which would be the guiding force for the future is essential. This paper reviews the above aspects to conceive the ideas and solutions for sustainable dwellings in light of tradition.