Volume 11 | Issue 12 |

 Abstract

Identifying and correcting faults in IC design have become critical stages as the complexity of digital VLSI circuits continues to grow. Presented in this paper is a novel fault identification model based on deep learning (DL), utilizing a distinct type of artificial neural network (ANN) known as stacked sparse autoencoder (SSAE). The main goal of this proposed model is to tackle the challenge in the exploration domain by employing SSAE for identifying features and detecting anomalies in extensive electronic circuits. The model comprises three key stages: test pattern creation, feature reduction, and fault detection. Unsupervised learning using training data is implemented in the SSAE phase to enhance feature extraction. The evaluation of feature extraction effectiveness involves modifying the architecture of the SSAE network. The strategy achieves 99.3% fault coverage with ATALANTA and reduces features by 99.7% using SSAE for test patterns.

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 Keywords

Automatic Test Pattern Generation, ANN, Fault Detection, Digital Circuit, ML, SSAE, Test Pattern

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 Abstract

Mentha piperita L., a perennial herb, and Mentha arvensis var. piperascens, a member of the Labiatae family, are used to make peppermint oil. Numerous well-known essential oil species, including spearmint, basil, lavender, rosemary, sage, marjoram, and thyme, belong to this family. Known for its numerous therapeutic benefits, including analgesic, anesthetic, antiseptic, astringent, carminative, decongestant, expectorant, nervine, stimulant, stomachic, inflammatory diseases, ulcer, and stomach problems, this plant is highly valued in many indigenous medical systems. This review provides a thorough and current examination of the chemistry, pharmacology, analysis, and applications of peppermint oil. The culinary and pharmaceutical sectors have paid increased attention to the medicinal plant peppermint (Mentha piperita L.) due to its positive effects on human health. This article uses theoretical research to assess the molecular structure of peppermint molecules. Yes, an assessment of peppermint's health advantages was done. Two peppermints substances that appeared to bind to the active site of the aryl amine N-acetyltransferase enzyme were cineol and menthyl acetate, according to our molecular docking analysis. The inhibition of this enzyme by these substances is indicated by this kind of interaction. Popular plant Mentha piperita comes in a variety of forms (i.e., oil, leaf, leaf extract, and leaf water). The most versatile oil is peppermint oil, and usage statistics for this oil are also thought to be pertinent to compositions using leaf extracts. This herbal preparation is utilized for its taste and fragrance qualities in meals, pharmaceuticals, cosmeceuticals, and personal hygiene products. The aroma of peppermint oil is crisp and strong, and it tastes strongly before providing a cooling effect. It is also utilized in mouthwashes, toothpastes, aromatherapy products, bath preparations, and topical treatments due to its many medicinal qualities. Peppermint oil formulations applied topically have been used to reduce inflammation and itch.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Peppermint, Antiviral, Antibacterial, Muscle pain

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Trauma Rapid application is a comprehensive healthcare management tool designed to provide critical assistance during emergencies and improve overall healthcare access. Its primary objective is to swiftly connect patients in distress with emergency services and the nearest hospitals through seamless integration with Google Maps. This integration allows the application to expedite the dispatch of ambulances and trigger SOS alerts when urgent assistance is needed. Users of the application include doctors, patients, and ambulance drivers. Key features of the app include its ability to pinpoint and direct users to the closest medical facilities, optimizing response times and saving crucial minutes during medical emergencies. Beyond emergency response, the application offers a convenient platform for scheduling non-emergency medical consultations, encouraging proactive health practices. Doctors can register, manage appointments. Additionally, the application integrates a blood bank contact and police station contact, broadening its utility to address healthcare and safety concerns. Users can seek blood bank services and report emergencies to local law enforcement, making the Trauma Rapid application an indispensable tool for ensuring the well-being and safety of individuals in critical situations and their ongoing healthcare management.

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 Keywords

doctors, patients, hospitals, ambulance services, blood banks, police station, Google maps.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Manual colorization of black and white images is a laborious task and inefficient. It has been attempted using Photoshop editing, but it proves to be difficult as it requires extensive research and a picture can take up to one month to colorize. A pragmatic approach to the task is to implement sophisticated image colorization techniques. The literature on image colorization has been an area of interest in the last decade, as it stands at the confluence of two arcane disciplines, digital image processing and deep learning. Efforts have been made to use the ever-increasing accessibility of end-to-end deep learning models and leverage the benefits of transfer learning. Image features can be automatically extracted from the training data using deep learning models such as Convolutional Neural Networks (CNN). This can be expedited by human intervention and by using recently developed .implement image colorization using various CNN models while leveraging pre-trained models for better feature extraction and compare the performance of these models.

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 Keywords

Deep learning, Pre-trained model, CNN, image colorization.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With an emphasis on the creation and effectiveness of a Hindi Articulation Therapy App, this study aims to address the need for language-specific resources in articulation therapy. Misarticulation is a major barrier to effective communication in the Indian culture, especially when it comes to young children. Language specificity is typically lacking in current treatments, which limits their applicability and efficacy. By providing a multifaceted mobile application designed specifically for Hindi speakers that includes Position, Phoneme, and Picture levels in therapeutic exercises, this study seeks to close this gap. The study's methodology includes the development and use of the Hindi Articulation Therapy App. Real-time feedback mechanisms and a wide range of therapeutic activities and procedures serve as the app's development guidelines. The Phoneme level unifies words across initial, medial, and final positions; the Position level shows how sounds are articulated in various mouth positions; and the Picture level offers visual cues to improve comprehension. As the app is being developed, input from parents, speech therapists, and therapy patients is taken into account to improve it. A pilot research is carried out to evaluate the app's efficacy. In therapy sessions led by the app, participants include parents, kids, and experts. Tracking right and wrong word output during data collection enables a quantitative examination of therapeutic outcomes. Initial results show that the software has the ability to greatly enhance Hindi speakers' articulation. The app's user-friendly layout, interesting therapeutic exercises, and importance of language-specific material are highlighted by participant feedback. According to the results, the Hindi Articulation Therapy App presents a viable substitute for conventional techniques, effectively tackling the distinct language obstacles inherent in the Indian setting. The study adds significant knowledge to the creation and use of articulation therapy instruments tailored to a certain language. It is recommended that additional study be conducted and the app be improved in order to increase its accessibility and wider influence inside India's heterogeneous linguistic landscape.

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 Keywords

Articulation Therapy, Therapeutic exercises, heterogeneous linguistic landscape, Misarticulation , therapeutic materials ,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Thyroid conditions are common worldwide. In India too, there's a significant burden of thyroid conditions. According to a protuberance from colourful studies on thyroid complaint, it has been estimated that about 42 million people in India suffer from thyroid conditions. This review will concentrate on the epidemiology of five common thyroid conditions in India hypothyroidism, hyperthyroidism, thyroid cancer. (1) Endocrine diseases are common in India of which the thyroid diseases represent a major subset. Thyroid dysfunction frequencies is rising at an intimidating rate in Indian population. Hypothyroidism and hyperthyroidism constitute the maximum chance of thyroid conditions in India. Hormone relief remedy has been a standard approach to thyroid dysfunction.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hyperthyroidism, Hypothyroidism, Thyroid cancer, Endocrine, India.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This article presents a new rule for automatic query construction that addresses the precise identification of syntactic and semantic patterns in sentences. The design and use of the method is carefully explained. Although the main goal is to create text-based questions, machine learning has shown great success in reading comprehension, especially focusing on creating questions from sentences. The system developed in human evaluation shows that it is more useful than other systems, that it creates problems like people and creates problems in general.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Natural Language Processing, Machine Learning, Artificial Intelligence, Question Generation, T5 Model

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Natural products from plant sources have been the basis of the treatment of various human disease. The use of herbal medicine becoming popular due to toxicity and side effects of allopathic medicines. Chromolaena odorata (L) King & Robinson is considered as weed plant and it is a perennial shrub belonging to the family Asteraceae. Clinical studies using aqueous extracts from Chromolaena leaves have shown various properties such as antimicrobial, wound healing, anti-diarrheal, astringent, antispasmodic, antihypertensive, anti-inflammatory, and diuretic. In the present study, organoleptic and microscopic studies were done which is the basis for the identification and determination of adulterants for identify the potent crude drug. The fresh leaves of Chromolaena odorata were studied by organoleptic evaluation, proximate analysis, and florescence analysis of powdered drug. The present information on the pharmacognostic evaluation of the plant drug Chromolaena odorata serve the important information to the identity and to determine the quality and purity of the plant material in the future.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

C. odorata, organoleptic, macroscopical, microscopical, fluorescence, adulteration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Technological development is unavoidable over time. That phenomenon has created a new trend, which is online transportation services. Online transportation services provide road transports that track and exploit the development of technology-based online application. Indonesia has the highest market share in Southeast Asia with an estimated transaction value of 18 million dollars by 2025. Indonesia has many online transportation service companies. Gojek, Grab, Maxim, and InDriver are the companies with the most user in Indonesia. Gojek leads an online transportation market in Indonesia, followed by Grab in second rank, Maxim in the third rank, and InDriver in the fourth. Online transportation services companies retain their customer by making customers repurchase. Customer repurchase intention us affected by customer satisfaction while using online transportation services. Customer satisfaction is affected by e-service quality and customer's trust to the brand. Hence, this study aims to find out how much e-service quality and trust affect customer satisfaction, and how much customer satisfaction affects customer's repurchase intention. This study uses quantitative method with descriptive analysis way. Data collection will be done by using questionnaire as a tool which will be spreaded out through Google Forms. Respondence's population is a sum of Grab's user in Indonesia, with a purposive sampling technique. Datas that already collected will be processed using SMART-PLS software.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

consumer behaviour, customer satisfaction, electronic service quality, online transportation services, price, repurchase intention, trust

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The surge in online reviews has revolutionized consumer decision-making, accompanied by the pervasive issue of fake online reviews. This research paper delves into a robust machine learning approach for combating this challenge. The paper addresses the crucial need for dependable methods to distinguish genuine user feedback from deceptive ones, fostering trust in online commerce and informed consumer decisions. Leveraging a diverse dataset of reviews, the study employs a multi-faceted methodology encompassing data preprocessing, feature extraction, and machine learning classification algorithms. Techniques such as text processing, sentiment analysis, and advanced classification models are utilized to assess their effectiveness in identifying fake reviews across diverse industries and platforms. The paper's outcomes highlight the relative performance of various machine learning algorithms, including Random Forests, Support Vector Machines, Logistic Regression, and more. The evaluation employs precision, recall, F1-score, and accuracy metrics to gauge predictive accuracy. These findings provide insights into the strengths and limitations of each algorithm, aiding practitioners in selecting the most appropriate model for their specific context. Concluding with the broader implications, the research underscores the significance of a machine learning-driven approach in countering fake reviews and elevating the credibility of online platforms. Serving as a valuable contribution to academia and industry, this research paper equips stakeholders with insights to promote more authentic and trustworthy digital interactions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake reviews, Online reviews, Machine learning, Classification algorithms, Text processing, Sentiment analysis, Trustworthiness, Deceptive content, E-commerce, Consumer trust, Data preprocessing, Feature extraction, Predictive accuracy, Robust detection, Data-driven approach, Decision-making, Information integrity, Online platforms, Performance evaluation, Algorithm comparison.

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Creative Commons Attribution 4.0 and The Open Definition