Reviewerly/exHarmony huggingface dataset:
A Unified Framework for Classification with Camera-Guided Labeling Approach

Several factors, including the widespread availability of high-quality image sensors and the exponential expansion of image data, have contributed to a significant breakthrough in computer vision in recent years. Picture classification is a key component of computer vision that has recently gained a lot of attention due to the vast variety of possible applications. These technologies have the potential to be employed in a variety of different disciplines, including surveillance, medical diagnostics, autonomous driving, and many more. For real-world deployment, a high level of classification accuracy is necessary, which is frequently obtained by laboriously tagging big picture sets. The amount of time necessary to complete this task may be difficult. In this paper, we present a unique and comprehensive framework for image categorization. The simplicity with which camera-guided labeling systems may be integrated into this framework is one of our key contributions. To create a robust and scalable framework for exact picture categorization, we use current breakthroughs in sensor, computer vision, and machine learning technologies. This enables our method to categorize photographs more precisely. Even though manually annotating images would require significantly more effort, this strategy may be beneficial. This demonstrates how the method may be used to eliminate the requirement for time-consuming human annotation These approaches make use of camera and sensor capabilities to give temporal data, sensor inputs, and contextual clues as extra information to help in labeling. They achieve this by using the capabilities of cameras and other sensors. They perform this activity in the hopes of improving the accuracy and efficacy of the product labeling procedure.
MAMBODM: Design of a Multimodal Augmentation Model with Bioinspired dataset Optimizations for Deep Learning-based classification of MRI images

Image augmentation is crucial for generating ample data in fields like medical image classification, especially when patient samples are limited, demanding high clinical precision for real-time applications. Numerous deep learning augmentation models have been proposed by researchers for medical image classification, but they tend to be generic or excessively complex. These models face scalability issues due to generating large image sets, limiting their practicality in clinical contexts. To address these challenges, a novel Multimodal Augmentation Model is introduced here, optimized with Bioinspired dataset strategies for Deep Learning-based MRI image classification. The model employs multiple augmentation techniques, such as shifts, flips, rotations, brightness, and zoom variations, followed by Genetic Algorithm (GA) optimization. GA utilizes a variance & accuracy-focused fitness function, enhancing image selection for efficient, accurate disease classification. A novel transfer learning Convolutional Neural Network (CNN) combines VGGNet-16 with tailored dense layers for superior classification. This approach outperforms state-of-the-art methods, yielding 5.9% higher accuracy, 3.5% higher precision, 4.1% higher recall, and 14.5% faster response, showing potential across clinical use cases.
Automated Cervical Cancer Prediction using Ensemble Machine Learning based Approaches

Cervical cancer is a widespread type of cancer that primarily affects women. Detecting it early is crucial for successful treatment and recovery. However, detecting cervical cancer before it spreads or reaches an advanced stage poses a significant challenge for the medical industry. To address this issue, a study was conducted that utilized various machine-learning classification models to predict cervical cancer. The dataset was balanced using the Adaptive Synthetic Sampling approach, and principal component analysis was used to select the top 13 features. Performance was evaluated based on several metrics, including Accuracy Score, Precision Score, Recall Score, and F1 Score. The research aimed to enhance the existing model and provide a more efficient one. The study's results are discussed in detail in this article, and medical institutes can leverage these methods to predict cervical carcinoma.
Building a Chatbot using Natural Language Processing

Chatbots are applications which simulate human conversational patterns, engage in human-to-human communication, and respond to user queries using data from their training set. There are two categories of chatbots: rule-based and self-learning. One of the beneficial things a company or organisation may have is a chatbot. They can be utilised for customer service and query resolution in many different industries. In this paper, research is done on NLP and a model is designed to build a chatbot using NLP techniques. The chatbot built using this model can be used as a normal conversation bot or as a customer support tool.
Enhancing Deep Neural Network Convergence and Performance: A Hybrid Activation Function Approach by Combining ReLU and ELU Activation Function

Activation functions play an important role in Deep Neural Networks. The activation function can learn nonlinearities present in the data; therefore, it can learn intricate patterns present in the data. Rectified Linear Unit (ReLU) is an activation function that helps in encountering the problem of vanishing gradient. However, it suffers from ‘dying ReLU’ problem for the negative values. Leaky ReLU can solve the problem of ‘dying ReLU’; though it still suffers from a vanishing gradient problem due to the small gradient at for negative values, which results in slow convergence. Therefore, in this work, a combination of ReLU and Exponential Linear Unit (ELU) has been proposed considering the smoother convergence of the ELU activation function for the values on the negative side. Evaluating the effectiveness of the developed hybrid activation function compared to previous ReLU activation function versions such as SeLU, Leaky ReLU, ELU, etc. using a toy multi-layer perceptron and convolution neural network (CNN) model on the FashinMNIST and MNIST datasets. The improvement in the performance of these toy models when used with the proposed hybrid activation function on given datasets suggests the effectiveness of the proposed hybrid activation function.
Decentralized Query Solving Educational Website

With the blockchain technology's quick advancement and the rising need for the Internet's partial decentralization, the use of underlying technology built on the blockchain has drawn a lot of attention. This research paper presents the design and implementation of a decentralized query-solving educational website, leveraging blockchain technology and a stack of complementary technologies including Solidity, ThirdWeb, React, Node.js, MetaMask, and Hardhat. The proposed platform aims to revolutionize the traditional online learning experience by addressing challenges related to data integrity, trustworthiness of content, and query-solving efficiency. This study delve into the technical aspects of implementing a blockchain-based query-solving educational website, including the design considerations, data structure, consensus mechanisms, and privacy enhancements. The research paper highlights the technical considerations and challenges encountered during the implementation process, including the design of smart contracts, integration of ThirdWeb technologies, development of the user interface, and integration with MetaMask for secure authentication and transaction handling.
Supply Chain Management Using Blockchain: Opportunities, Challenges, and Future Directions

Blockchain innovation is being proclaimed as a unique advantage. Numerous examiners see blockchain innovation as a troublesome creation. Blockchain innovation has grabbed the eye of scholastics, chiefs, and specialists the whole way across the world. Blockchain is widely regarded by these entities as a highly disruptive technology that is already reshaping businesses and their supply chain models. Blockchain has the potential to transform supply chain (SC) processes in various ways, such as enhancing SC provenance, improving security, and reengineering business operations. An increasing number of recent studies have explored the use of blockchain in supply chains. Some have shown significant awareness of blockchain's role in functional transparency across various sectors, including e-commerce, agriculture, and public administration. We emphasize the identification of valuable opportunities, potential cultural implications, the current state of the art in advancements, as well as significant trends and challenges. We research a few modern areas, including transportation, fabricating, and auto, flight, finance, innovation, energy, medical care, horticulture and food, online business, and schooling, among others, that can be effectively overhauled with blockchain. The main aim of the study presents a complete evaluation of 178 examination distributions and examines different components of blockchain innovation in SC and coordinated operations the board.
Sarcasm Detection via sentiment and emotion analysis of News Headlines Using optimized simulation of Weibull entropy distribution

Sarcasm, a significant aspect of communication, finds expression through words, facial cues, and vocal intonations. In today's digital era, the prevalence of sarcastic comments is evident across social media comments, and news headlines. Detecting sarcasm in text data through mechanized means presents a challenging hurdle in Natural Language Processing, as it requires discerning expressions that convey the opposite of their literal meaning. This paper delves into the realm of sarcasm detection using an unsupervised mathematical optimization approach, drawing on an optimized probabilistic distribution technique and a sentiment lexicon tool. Incorporating a news headline dataset, we explore the nuances of sarcasm detection. The headlines often employ sarcasm to captivate readers, and this very tactic could result in misinterpretations or misconceptions which led readers to misjudge the intended meaning of these headlines, potentially leading to the propagation of incorrect information among peers and colleagues. This underlines the necessity of sarcasm detection, particularly within the realm of news and social media. We also delve into the identification of sentiment and emotional language traits embedded within sarcastic content. Our results spotlight the superiority of the proposed statistical strategy when compared to alternative methods. The amalgamation of optimized probabilistic distribution and sentiment lexicon tool contributes to the effectiveness of our approach, offering valuable insights into sarcasm, sentiment, and emotion within textual content.
Enhancing Face Detection and Recognition through Machine Learning Algorithm

Face recognition is a rapidly growing field with multiple applications. Numerous face recognition algorithms have been developed over the years. In this research, use a Histogram of Oriented Gradients (HOG) based face detector for precise face detection, which surpasses other machine learning techniques, such as Haar Cascade. The suggested approach uses HOG for feature extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE), two generally used pre-processing methods, throughout the recognition process. The test picture and the training images are both used to extract HOG features. Lastly, this system employs Support Vector Machine (SVM) for classification, as it effectively classifies the HOG features. Pre-processing techniques are utilized to equalize illumination, reduce noise, and enhance contrast. The study's findings show the dependability and effectiveness of this approach in achieving superior face recognition performance.
American gesture Language Detection Using Transfer Learning and Efficientnet B1 Technique

Sign language is the primary form of Conveying messages to individuals with hearing impairments or speech limitations. Regular people frequently undervalue the importance of gesture language, which is the only form of communication for the deaf and mute communities. These people are suffering significant setbacks in their lives as a result of these restrictions or impairments, including unemployment, severe depression, and a number of other symptoms. They also employ gesture translators aid as a form of communication. However, paying these interpreters would be excessively expensive, so a cheap solution to the issue is required. In order to translate the visual hand dataset based on American Sign Language into written information, a system has been created. The dataset of American Sign Language has three more characters—"del," "nothing," and "space," in addition to 29 classes for each letter in the English sign language. The collection has 10,208 pictures in it. The dataset underwent testing with diverse pretrained models. The majority of them performed rather typically, and Derived from this finding, we built a CNN model with EfficientnetB1 scaling loaded with weights It underwent training utilizing the ImageNet model, maintaining uniform scaling across depth, breadth, and resolution of dimensions utilizing a straightforward yet incredibly effective compound coefficient. We demonstrated the efficacy of this strategy through the model’s results. The model achieved an accuracy rate of 99.98%, with a validation accuracy of 100%.
Classification of Fall Detection Based on Daily Living Activities using Machine Learning Approach

Population aging is a worldwide phenomenon, with agricultural regions housing the greatest proportion of aged individuals in the labor force per unit of population. Nonetheless, in the area of agricultural machinery, very few comprehensive investigations on farmer falls have been carried out. The implementation of classification techniques for monitoring devices to identify farmworkers' falls and non-fall movements is the main topic of this study. Agricultural biomechanical parameters are taken into account while identifying everyday living activities. In this study, 40 healthy participants who conducted a activities of daily living (ADLs) and range of falls had their initial acquisition datasets of signals containing collected and processed signals from one gyroscope and two accelerometers. Using spatial features, the machine-learning classifiers were trained to discriminate between fall and non-fall events. The efficacy of the suggested strategy was assessed by supervised machine learning experiments. The k-nearest-neighbors (KNN) and support vector machine (SVM) algorithms were able to distinguish between falls and ADLs (binary-class classification) with ROC auc-scores of 0.999.
Sarcasm Detection in News Headlines Using Deep Learning

A communication technique known as sarcasm is used to express an emotion that is opposite or distinct from the literal meaning of the words being used, typically for satire, criticism, or amusement. Sarcasm detection is the process of identifying language that purposefully conveys the opposite meaning of what is being said. Today's sarcasm detection techniques employ a range of deep learning (DL) and machine learning (ML) algorithms, including logistic regression, random forests, decision trees, long short-term memories (LSTM), convolution neural networks (CNN) etc. However, these methods have limitations in accurately detecting sarcasm, particularly when dealing with large datasets. In this paper, we aim to provide a better method for sarcasm identification in news headlines that combines LSTM and CNN algorithms enhancing sarcasm detection's accuracy and here we managed to achieve nearly greater than 97% of accuracy by leveraging the strengths of both algorithms. Using a dataset of news headlines, we have also compared our hybrid model to other models to see how effective our proposed approach is more accurate.
Seismic Signal Processing and Aftershock Analysis using Machine Learning

The anticipated method aims to develop a robust machine learning-based approach for aftershock classification using seismic sensing signals. Accurate classification of after- shocks plays a crucial role in understanding seismic processes and assessing risks associated with earthquakes. By distinguishing aftershocks from mainshocks and background noise, we enhance earthquake hazard prediction and mitigation. proposed methodology combines feature extraction techniques, machine learning algorithms, and rigorous evaluation measures for reliable aftershock classification. The study begins with an introduction to aftershock classification’s significance in earthquake research and disaster management, followed by a literature survey exploring existing methodologies and their limitations. Machine learning algorithms are identified as promising solutions for these challenges, motivating the choice of methodology. We preprocess seismic sensing signals through data cleaning and noise reduction to ensure high-quality input. Feature extraction methods capture signal characteristics, including statistical, time- domain, and spectral features. These extracted features serve as input to the primary classification algorithm, the SVM, which is best suited to handle complex data and non linear relationships with ease and effectiveness. SVM is trained on a labeled dataset comprising manually classified aftershocks and other seismic events, with hyperparameter tuning for optimization Extensive experiments on a specific region’s seismic dataset demonstrate outstanding accuracy, with an F1-score of 0.99974 and 99.98% accuracy. High precision and recall values indicate reliable aftershock classification, as confirmed by negligible false positives and false negatives from the confusion matrix analysis. In conclusion, this research presents a robust machine learning- based approach for aftershock classification using seismic sensing signals. The methodology offers high accuracy and reliability, contributing to earthquake research and disaster management by enabling precise aftershock identification. It is extendable to various seismic datasets and applicable in real-time earthquake monitoring systems. Future work involves exploring additional machine learning algorithms, incorporating domain knowledge, and integrating the models into operational systems to support seismic event decision-making
Pedagogical Training of Higher Education Teachers: Communication and Collaboration with Digital Technologies

This article aims to reflect upon a strategy of Higher Education (HE) teachers’ pedagogical training, steered toward the development of communication and collaboration skills using digital technologies. The training, aimed at teachers of a public HE institution in Portugal, was conceived and implemented in the 2020/2021 and 2021/2022 school years, based on the contributions of an Erasmus+ project which was concluded in 2021. Using action-research methodology and highlighting the dimension of reflection upon practices, the authors present the theoretical assumptions that back the training and strategy developed, reflecting on their contributions to teacher professional development, ensuing from the trainees’ evaluation and from their reflections.
Reconfigurable Metadata Schemas with Multivalued Elements: Towards Educational Repository Management Through Formal Grammars on the Clavy Platform

Clavy is an experimental platform for managing learning object repositories that allows defining metadata schemas specific to each educational domain and even to each repository. Additionally, Clavy enables dynamic reconfiguration of these schemas to modify the way learning objects are explored and visualized. In this paper, we motivate and detail the evolution undergone by Clavy metadata schema model to support multivalued descriptive elements. Furthermore, we characterize this extension as a restricted type of grammatical formalism, opening the possibility of using more general grammars to organize repository contents.
Contributions and potential of the WheelDecide digital application in a Basic Education class

Currently, Information and Communication Technologies (ICT) are found in practically all areas of activity and are of essential importance, and therefore, schools are no exception. The internship report aimed to investigate whether the WheelDecide digital application can promote collaborative work and the formative assessment process. To understand this issue, we carried out a qualitative investigation, opting for action research within the scope of Supervised Teaching Practice in Basic Education, in a 2nd year school class, with 21 students. In accordance with the selected methodology, direct/participant observation, field notes, semi-structured interviews with the class teacher and two teachers who teach at the 1st CEB, the application of questionnaire surveys for guardians and photographic records. During the investigation, five intervention sessions were developed, and during the process, different opinions were collected, which allowed triangulation of the data obtained, determining that using WheelDecide can promote collaborative work and create opportunities to carry out evaluation. formative. In this sense and considering the data analysis from semi-structured interviews, we found that teachers consider this resource to have potential, capable of providing significant learning. In the opinion of those in charge of education, it is possible to see that the use of this digital resource can trigger better learning, creating a more motivating environment, even though the presence and supervision of the teacher is not dispensable. After triangulating the data obtained, the importance of using this resource to create innovative contexts that provide more meaningful learning is recognized.
Assessment of learning Mediated by Digital Technologies in the Context of Environmental Education: Teacher Training through a MOOC Course

Historically, the act of evaluating has become an exclusionary, authoritarian and anti-democratic tradition, being a classificatory and selective mechanism, centered on the educator and the teaching system, not prioritizing who learns. Therefore, we must understand that when evaluating, we need instruments that are diagnostic and guided by equity, thus allowing to provide learning that makes sense to the student, since there is no "truth", but truths that are revealed and validated according to the epistemological, methodological relationship and the current historical moment. Therefore, the present research aims to offer an educational product in a massive and open course format, known as MOOC, thus making it possible to expand the scope of the offer, but optimizing budgetary and temporality issues. The course addresses the concepts of learning assessment, its methods and the use of digital technologies, in a formative perspective in the context of Environmental Education, seeking the integration of traditional doing combined with the insertion of new environments / knowledge through an emancipatory intentionality. The research is linked to the Rio Doce School Program, which aims to provide postgraduate training for educators (teachers, managers and community representatives working in schools) working in public schools of basic education.