Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review of the Latest Advancements in Large Generative AI Models

There has been an increase in big generative models like ChatGPT and Stable Diffusion over the last two years. These models are capable of a wide range of activities, including providing general answers and producing creative representations. They have a significant impact on a variety of businesses and society since they have the ability to transform established work roles. Generative AI may, for instance, convert text into images using the DALLE-2 model, 3D images using the Dreamfusion model, photos into text using the Flamingo model, and even text into video using the Phenaki model. While ChatGPT can translate text into other texts, the AudioLM model can translate text into audio. Text is converted into code by the Codex model and scientific texts using the Galactica model. Algorithms like AlphaTensor can also be developed through generative AI. This research seeks to present a thorough overview of the most important generative models that have recently been released and their impact on various industries. It also makes an effort to taxonomize these models in order to better comprehend their functions and applications.
Hate Speech Detection in Multi-social Media Using Deep Learning

In recent days lots of advancements on the Internet and various social media such as Facebook, Twitter, Gab, Reddit, YouTube, Stromfornt, etc., many people continue to use multiple online social media platforms to express their views or comments and sometimes post Hate Speech or Offensive Language. It is challenging to detect hate speech manually, especially from social media data. A robust mechanism is essential to handle this issue, which will automatically detect hate speech on social networks. Many researchers have addressed this issue through many means. However, most methods are not accurate enough in detecting hate speech because of the massive volume of data, data dependencies, excessive parameters, and the use of only homogeneous social media data. To resolve this issue, we have Investigated a cross-platform hate speech recognition mechanism for Social Media Interactions and developed a Deep Neural Network (DNN) approach to tackle heterogeneous multi-social media data as a generic platform. This paper proposes a model for hate speech detection based on Deep Learning architecture. This model comprises multiple Deep neural networks combining Bidirectional-Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to produce the best performance on numerous social media datasets of 0.2 million (202377) annotated Tweets or comments. And also, the performance of the BiLSTM-CNN model has been analyzed, measured, and compared with existing classical machine learning methods. Our model is trained using ten epochs to split the dataset, 80% for training and 20% as validation data. Our proposed model’s performance is evaluated using the precision, recall, and accuracy parameters. Finally, we compared the performance of our model with many machine learning methods such as Decision Trees, Support Vector Machine (SVM) Linear, Logistic Regression, and Naive Bayes methods. It is evident from the result that our proposed model achieved an accuracy of 92.5%, and the F1-score value is 92.1% in the detection of hate speech. Overall, the proposed model outperforms other state-of-art methods.
Sanskrit OCR System

The use of optical character recognition technology has become increasingly important for researchers in recognition techniques. Recognition of Sanskrit character is a challenging area in pattern recognition, character recognition is used to convert printed or handwritten text into machine-encoded text. The proposed system explores the effectiveness of three distinct feature extraction methods, namely Discrete Cosine Transform (DCT), Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT), for character recognition. Each method is applied separately to a dataset of handwritten characters and evaluated for accuracy. Also the combination of SIFT + DCT, SIFT + HOG and DCT + HOG is used for comparison in order to check whether the combinations are effective or not compared to individual methods. For the Sanskrit OCR system it is observed that DCT gives the highest accuracy of 98.7%.
Integrating Scene and Text Recognition for Improved Scene Caption to Assist Visually Impaired

The paper introduces an innovative real-time system designed to enhance the scene perception of visually impaired individuals. The proposed model leverages a deep learning approach, employing a CNN-LSTM encoder-decoder framework for efficient image captioning. This novel system integrates scene text information using optical character recognition (OCR) to generate meaningful scene captions as audible feedback for blind or visually impaired users. The proposed model is implemented on embedded processing unit, Jetson Nano equipped with monocular camera and mono-earphone. The model extracts scene features and text information to provide detailed descriptions of the environment. The LSTM-CNN model was trained on a Flickr 8K dataset and a custom dataset, specifically tailored to the Indian environment. Eight different CNN architectures, including VGG-16, EfficientNet-V2L, MobileNet-V2, Inception Resnet-V2, Resnet152-V2, EfficientNet-B7, Xception, and NASNet-Large, were tested to compare their scene caption performance with sigmoid and Tanh activation. Extensive evaluations were conducted using metrics such as BLUE, GLUE, METEOR, and RIBES, with VGG-16 and EfficientNet-V2L achieving the highest BLUE scores of 0.491 and 0.489, respectively. The proposed system presents a promising solution to provide visually impaired individuals with essential information about their surroundings through auditory perception for independent mobility and reduced risk for accident.
Augmented Reality-Rich Environment: Designing for Mathematics Education

This chapter discusses the potential of augmented reality (AR) technology for educational purposes in the mathematics classroom. It aims to discuss a perspective of integrating AR technology that bridges the gap between real-world phenomena and their mathematical representation, in mathematics education as well as what we learn as researchers in the attempt to use AR in the context of covariational reasoning. Unlike other technological tools (e.g., Dynamic Geometry Software or Computer Algebra Systems), AR has not been widely used in classroom settings. In part, this is due to technological difficulties in working with this technology at the classroom level. On the other hand, this may also stem from the lack of research and development in the field concerning the use of AR and its special affordances for mathematics teaching and learning. With this in mind, the chapter approaches the subject by describing a specific learning environment in the context of a specific area of content, namely, covariational reasoning. Two theoretical frameworks guided this chapter: The multimodal approach of learning and the covariational reasoning framework. The chapter discusses a wider perspective of integrating AR technology in the mathematics classroom, such as the crucial role of representations, of embodiment, the social aspects that AR affords, and forms of covariational reasoning.
Analysis and Design of Document Similarity Using BiLSTM and BERT

In this paper, we propose a deep learning-based approach to measure document similarity using bidirectional encoder representations (BERTs) from transformers and its implementation as an application programming interface (API). BERT has recently shown significant improvements in natural language processing and is widely used in various applications such as question answering and text classification. We trained and refined a BERT model on a large corpus of documents to measure document similarity. The proposed API receives two text arguments and returns the degree of similarity between them. On several benchmark datasets, we demonstrated that our approach outperforms conventional state-of-the-art similarity measures. Our experimental results show that the proposed method of measuring document similarity using BERT and API is effective and efficient, and that API implementations can be used in a variety of real-world scenarios is visible.
Fake Product Review Detection Using Machine Learning

Advancement of technology has surely made our life easier and convenient and E-commerce systems are one of the best gifts of technology. We can now have anything from any part of the world with just a few taps on our smart devices. Many E-commerce sites have gained popularity not just in urbanized areas but also rural areas in many parts of the world. We can have a product or service delivered in no time without stepping out and at the comfort of our own home. Also, we have wide range of products and brands which gives us number of options to make a perfect choice. When it comes to choosing a product, we definitely go through the reviews given by the customers who have used the products. But how do we know if they genuine ones? Technology, having it’s own advantages, can also be used for frauds. One such thing is providing fake reviews on products to attract potential customers and gain popularity amongst them in order to increase sales. Thus, identifying the fake reviews in an essential thing to do and research should be effectively done in this particular area. This chapter gives us an approach which uses machine learning algorithms to tackle this problem. Along with extracting the characteristics of the reviews it also helps us identify the behavior of the users who leave a review by using many algorithms like K-Nearest Neighbor, regression and NLP. In addition to these, this chapter is based on data mining to extract meaningful information. Hence this chapter provides us with a solution on how we can use machine learning to detect fake reviews while also taking behavioral patterns of the reviewers into consideration.
Character Values in Relation to the Sea Story Novel by Leila S. Chudori to Learning in High School

: The novel Laut Bercerita by Leila S. Chudori introduces various applied in high school learning. This study aims to identify these values and explore the implications of their application in learning Indonesian high school. This study uses a qualitative approach with content analysis techniques to identify the values of character education in the novel Laut Bercerita. The results of the study show that there are several character education values contained in this novel, including honesty, justice, friendship, simplicity, courage, and responsibility. The implications of these values for learning Indonesian in high school are improving language skills, understanding cultural values, and forming positive and constructive student character. This study also provides practical recommendations on how to integrate character education values into Indonesian language learning in senior high schools. The Novel Laut Bercerita by Leila S. Chudori has many character education values that are relevant to be applied in learning Indonesian in high school. Integrating these values into learning can help improve language skills, understand cultural values, and shape positive student character studies that wish to develop Indonesian language learning based on character education. Keywords: the value of character, storytelling marine novels, indonesian language learning in high school. DOI: http://dx.doi.org/10.61436/rietm/v2i1.pp05-08
Deep Learning and Machine Learning Models to Predict Energy Consumption in Steel Industry

This paper present the study results of predicting energy consumption in the steel industry using modeling methods based on machine learning and deep learning techniques. Machine learning algorithms used in this work include artificial neural network (ANN), k-nearest neighbors (kNN), random forest (RF), and gradient boosting (GB). Deep learning technique is long short-term memory (LSTM). Linear regression, which is the statistical-based learning algorithm, is also applied to be the baseline of this comparative study. The modeling results reveal that among the statistical-based and machine learning-based techniques, GB and RF are the best two models to predict energy consumption, whereas ANN shows the predictive performance comparable to the linear regression model. Nevertheless, LSTM outperforms both statistical-based and machine learning-based algorithms in predicting industrial energy consumption.
Parameter-Free Conglomerate nearest Neighbor Classifier Using Mass-Ratio-Variance Outlier Factors

Classification is one important area in machine learning that labels the class of an instance via a classifier from known-class historical data. One of the popular classifiers is k-NN, which stands for “k-nearest neighbor” and requires a global parameter k to proceed. This global parameter may not be suitable for all instances. Naturally, each instance may situate on different regions of clusters such as an interior instance placed inside a cluster, a border instance placed on the outskirts, an outer instance placed faraway from any cluster, which requires a different number of neighbors. To automatically assign a different number of neighbors to each instance, the concept of scoring from the anomaly detection research is desired. The Mass-ratio-variance Outlier Factor, MOF, is selected as the scoring scheme for the number of neighbors of each instance. MOF gives the highest score to an instance placed very far from any cluster and the lowest score to an instance surrounded by other instances. This leads to the proposed classifier called the conglomerate nearest neighbor classifier, which does not require any parameter assigning the appropriate number of neighbors to each instance ordered by MOF. Experimental results show that this classifier exhibits similar accuracy to the k-nearest neighbor algorithm with the best k over the synthesized datasets. Six UCI datasets, the QSAR dataset, the German dataset, the Cancer dataset, the Wholesale dataset, the Haberman dataset, and the Glass3 dataset are used in the experiment. This method outperforms two UCI datasets, Wholesale and Glass3, and displays similar performance with respect to these six UCI datasets.
SANKET—A Vision Beyond Gestures

Instead of spoken words, sign languages use the visual-manual modality to communicate meaning. Different gestures, behaviours, and signals are used by people to communicate with one another. Nearly 20% of the world’s population, or more than 1.5 billion people, suffer from hearing loss, according to the World Health Organization. In today’s world, it can be quite difficult to communicate with those who are hard of hearing because their primary form of communication, sign language, always calls for an interpreter. Like hearing persons, the deaf community requires access to all information. The lack of any sign language corpus has prevented the development of many text-to-sign language conversion systems, despite a variety of tools being available to interpret or recognise sign language and convert it to text. Daily news is that aspect of communication that informs us of the evolving events, problems, and people in the outside world. People with hearing impairments should be able to comprehend news just like everyone else. Our study intends to develop a system in which any news article is scraped, summarised using natural language processing technique, and then translated into American sign language (ASL).
Visual HOG-Enabled Deep ResiNet for Crime Scene Object Detection

Crime scene investigation is an important and challenging work for detecting suspects from the incident. Investigations begin from collecting various objects, markings, location, and scalability of the incident. Evaluation of artificial intelligence helped a lot in creating automated investigation models. To detect the crime scene objects, markings impact a lot in making decisions. Investigation scenario is highly sensitive; hence, detection of crime scene objects as early as possible is important. The proposed approach considers crime scene videos collected from CCTV cameras as prime input. Video-to-image conversion is implemented initially. Visual HOG histogram of orientation gradient feature (VHOG) is extracted from the image. Based on the feature, the background subtraction is done. The semantic object is extracted from the image through morphology factor as well as HOG feature matching. The correlated semantic object shades are compared with the training images. Deep resilient net (DRN) is created to make training and testing processes. Various images of the objects are separately trained using the neural network. Using the hidden layers of neural perceptrons, the similar blob of the object is continuously compared with all the salient objects in the database images. Based on the correlated score, the confusion matrix is formulated. The calculation of true positive, true negative, false positive, and false negative rate is evaluated. The novel structure is validated with repeated iterations of comparisons and further the achieved the accuracy of 91%. Reduction of false similarity issues is considered here to avoid occluded images, and rejection of false image is adopted steps here.
Computer Vision-Based Virtual Mouse Cursor Using Hand Gesture

The human–computer interaction (HCI) is a field in which the developer makes a user-friendly system. The hand gesture is most frequently used as interaction in the digital environment and thus complexity and flexibility of motion of hand is a research topic. It is the most natural expression for communication between humans and computers in virtual systems. The technology is shifting toward gesture interface by capturing the motion of our hands and therefore controlling devices. In this study, we are trying to establish an innovative way to implement HCI using Python and MediaPipe. Hand gesture study divides the hand into 21 segments to identify gestures and control functionalities of the mouse cursor. We will be implementing some commands associated with each gesture.
Pedestrian Detection Using YOLOv5 and Complete-IoU Loss for Autonomous Driving Applications

Due to the critical role in numerous applications, identifying pedestrians is a hotly debated issue of study in the field, particularly in the fields of surveillance, robotics, and vehicle safety. Regardless of different research methodologies progressed in the past years, pedestrian detection is a public challenge whose accuracy and robustness should be improved. This paper is concentrated on increasing the speed of detection and accuracy. With significant diffusion patterns deep learning has achieved remarkable performances in many applications, including pedestrian classification problems. In order to get the greatest accuracy, the version 5 (YOLOv5) model is trained using intensified and augmented images, then SiLU activation with complete intersection of union losses. 2975 images are used to train from 5000 images of familiar “CityPersons” dataset. The proposed algorithm achieved the speed of detection with 0.093 s/image, and this can be used in the real-time environment and exceptional miss rate (MR) over heavily occluded area from the dataset of CityPersons.
Smart Computer Commands Using Gesture Recognition

Gesture recognition is a technique that has a variety of applications. Hand gestures are a type of nonverbal communication that can be employed in a variety of contexts, including medical applications, human–computer interface (HCI), robot control, and communication between deaf-mute individuals. There have been studies on hand gestures that employed a variety of strategies, including those based on computer vision and instrumented sensor technology. In other words, there are various categories under which the hand sign can be categorized, including posture and gesture, dynamic and static, or a combination of the two. It also tabulates the effectiveness of various strategies, concentrating on computer vision methods that address similarity and difference points. After going through a grave pandemic, everyone has now started to become more aware of how this disease might spread, and therefore, people prefer a contactless way of interacting with devices. This has enabled people to rely on gestures to interact with computer devices. While using certain applications, many shortcuts are used, e.g., in Microsoft Word for copy and paste functions, keyboard shortcuts like Control + C and Control + V, respectively, are used. As these shortcuts are used many times, a gesture for these shortcuts can be assigned, i.e., when a particular gesture is detected, a keylogger will be activated which will perform the shortcut keys. These gestures can also be used as operating system functions such as putting a PC to sleep or opening a particular application. This project work aims to design and develop a gesture-based system for executing the most frequently required computer commands. The MediaPipe library which implements convolutional neural network (CNN) is used to carry out the proposed project work. In initial phase, ten most frequently required commands are implemented. The Qualcomm developer network dataset is used to develop the proposed system. The proposed system performs better in accuracy, effective recognition of gestures, and execution. The proposed research is an analysis of various algorithms used for gesture recognition, namely VGG16, SGD, SSD, YOLOv3, and MediaPipe. The obtained accuracy for the algorithms mentioned above is 85.68%, 77.98%, 82.00%, 97.68%, and 98.2%, respectively.
Rover with Obstacle Avoidance Using Image Processing

With the rapid development of robotics, autonomous rovers are finding numerous applications in the automation industry. This research paper aims to present an approach to designing an autonomous rover that can detect and avoid obstacles through advanced image-processing techniques. The proposed rover operates in real time, utilizing the Raspberry Pi module and an integrated camera, and is developed using the open-source computer vision (OpenCV) library. The Canny edge detection algorithm, one of the most reliable and widely-used image-processing techniques, is employed to process the images and video feeds captured by the camera. Additionally, the images are further enhanced using a bilateral filter to achieve improved results. The rover's navigation system is designed to enable autonomous movement, ensuring that the wheels can detect and avoid obstacles, thereby eliminating the possibility of collisions. The study presents an innovative approach to autonomous rover design and could significantly impact the field of robotics and automation.
A Comprehensive Study on Online and Offline Evaluation of Recommendation System

Recommendation systems (RSs) had surfaced as the backbone of E-Commerce businesses, helping in improving their efficiency and to make them user-friendly. E-Commerce applications generate vast amounts of information resulting in the problem of information overloading. Recommendation systems are information filtering systems that can anticipate items of one’s interest according to past choices, trends, demographic information, etc., and show recommended items to the users. Now, the question arises is, how well a recommendation system is recommending the items? So, the evaluation of the recommendation systems is a must, i.e., measuring user satisfaction based on recommended items. Evaluation can be done offline, through a user study, and online modes depending on the type of data and user’s involvement. This paper presents a comprehensive study of various metrics for the evaluation of the performance of recommendation systems. Table 1 provides a comparison between different evaluation metrics based on the types of the evaluation method, category of the experiment, and approach of evaluation. Table 2 presents the metrics used by research articles covered during this study. This study will help the developers and researchers in deciding the metrics to evaluate their recommendation systems.