Reviewerly/exHarmony huggingface dataset:
CheckersMind: Enhancing Cognitive Ability in Dimentia Patients Through Checkers Game Therapy with Chatbot

In this study, explore how reinforcement learning could be used to improve the checkers-playing experience for people with dementia as part of a therapeutic gaming environment. In order to train an agent that can aid in both offensive and defensive strategies, the Soft Actor-Critic (SAC) reinforcement learning algorithm is used. The fundamental goal of this research is to find ways to stimulate the brains of people with dementia in order to improve their cognitive abilities, such as memory, problem-solving, and observational skills. The Deep Deterministic Policy Gradients (DDPG) and the Soft Actor-Critic reinforcement learning algorithms are compared and contrasted. The results show that the Soft Actor-Critic algorithm excels in this setting. This study adds to the growing body of evidence supporting the use of game-based therapies in the treatment of dementia, with the aim of improving patients' cognitive abilities and quality of life.
Spatially-Constrained Anomaly Detection in Crowded Environments using Meta-Heuristic Algorithm

Crowded-environment anomaly detection is crucial for public safety, surveillance, and crowd management. Traditional anomaly detection algorithms struggle with congested scenes. This study introduces a metaheuristic algorithm for spatially-constrained anomaly identification in congested environments. Metaheuristic optimization uses spatial limitations in the population to find anomalous patterns. The program can identify normal crowd behavior from aberrant by adding spatial linkages and boundaries between individuals. A metaheuristic algorithm intelligently searches for optimal constraint configurations to maximize normal-abnormal behavior discrimination. This adaptive optimization approach handles population densities, dynamics, and environmental factors. Proposed approach is tested on various crowded scene datasets. Proposed anomaly detection method outperforms others. Spatial limitations increase anomaly detection accuracy and robustness in overcrowded environments. This system has major applications. It detects violence, stampedes, and odd behavior in crowded settings to improve public safety. Eliminating false alerts and focusing on real anomalies can help crowd management and surveillance systems.
Enhancing Sentiment Analysis of Twitter Data Using Recurrent Neural Networks with Attention Mechanism

Sentiment analysis, the intricate task of discerning and classifying the myriad of sentiments conveyed within textual data, has captured substantial interest and intrigue, primarily driven by the pervasive utilization and influence of social media platforms. In this study, a novel approach to enhance sentiment analysis of Twitter data by employing Recurrent Neural Networks (RNNs) with an attention mechanism is proposed. The proposed model leverages the sequential nature of tweets and the attention mechanism to capture the inherent dependencies between words and highlight salient information. The RNN-based model on a large-scale dataset of annotated Twitter data, encompassing diverse sentiments is trained. The model effectively learns the contextual information and sentiment patterns, enabling accurate sentiment classification. A comprehensive set of tests were run to evaluate the effectiveness of this methodology, and the outcomes were meticulously compared to those of traditional machine learning algorithms and established deep learning models. The empirical findings demonstrate that proposed attention-based RNN model performs better than competing methods and achieves cutting-edge performance on sentiment analysis of Twitter data. Moreover, an in-depth analysis of the attention weights generated by the model, shedding light on the significant words and phrases influencing sentiment classification is conducted. This provides valuable insights into the underlying sentiment dynamics in Twitter data. Proposed research contributes to the field of sentiment analysis by proposing an effective and robust approach for Twitter sentiment classification. The findings highlight the potential of RNNs with attention mechanisms in capturing the nuanced sentiment expressions prevalent in social media text. The proposed model can facilitate various applications, including real-time sentiment monitoring, brand reputation analysis, and public opinion tracking, benefiting industries and researchers alike.
A Summary on Privacy and Security in Cloud Data using Various Approaches

Nowadays online users are prone to lot of security related issues in protecting their data. In order to achieve this privacy preservation in cloud plays a major role. For this purpose various technologies related to cyber security was proposed according to application in which the security has to be implemented. Some of these techniques include machine learning, deep learning, federated learning natural language processing, block chain etc. This paper analyses various approaches proposed by researches. This paper is divided into 7 sections such as role of cyber security in privacy preservation, privacy preservation in cloud using cyber security, what is a hybrid model and how to build it to implement features of security, Malicious node deduction in wireless sensor network using cyber security, relation between cyber-security and block-chain, federated learning and differential privacy.
MOODIFY: Web Application to Detect Human Emotions

Human Emotions are very complex psychological responses to various events and stimuli that are experienced subjectively. These Human Emotions are often influenced by cultural, social, and individual factors. There are a lot of emotions that are recognized and some of them are Happiness, Sadness, Anger, Fear, Disgust, Surprise, Love, Excitement, Guilt, Shame, Jealousy, Empathy and many more. Recently, emotion detection is a popular topic due to a vast potential application such as marketing, psychology, human-computer interaction, political science and curing diseases such as depression, anxiety disorder, chronic pain, eating disorders, etc. According to World Health Organization (WHO) depression, anxiety, etc. are common metal disorder and it is estimated that almost 5% of the adult suffer from it. One out of every Eight people in the world is living with a mental disorder. There is a need of a comprehensive system, which can detect human emotion which are showcased by his/her text. In world of Internet, social media platforms have become very essential means of communication for the entire world. However, sometimes while communicating using text chats people get confused because they are unable to understand the emotions showcased by the messages. This paper aims at providing a sustainable, robust and efficient web application using which anybody can understand the human emotions behind the text.
State-of-the-Art Object Detection: An Overview of YOLO Variants and their Performance

A fundamental component of computer vision is object detection, and the state-of-the-art object detection algorithm YOLO (You Only Look Once), which uses regression as its foundation has become one of the most effective techniques for real-time object detection in images and videos. This research study compares the performance of different YOLO object detection algorithms on the widely used Microsoft Common Objects in Context (MS COCO) dataset. It also provides a comprehensive analysis of how the YOLO algorithm has been implemented in various iterations, from YOLOv3 to YOLOv7, for various object detection tasks. The work focuses on assessing these models, each of which was pretrained over multiple weights, by putting them to the test on various datasets with various classifications. The primary objectives of this research are twofold: first, to contrast the performance of YOLO models amongst various versions, and second, to assess their effectiveness on both CPU and GPU platforms. Frames-per-second (fps) and mean Average Precision (mAP), which measures the precision of identifying an object, serve as the foundation for the comparison. This study tests the capability of YOLO versions to infer classes from photos and videos by running the models on several datasets with dozens of classes. The results obtained, offer insightful information about the advantages and disadvantages of each YOLO variation. The frames-per-second(fps) metric allows us to assess the computational efficiency of the models, highlighting their real-time processing capabilities. Simultaneously, mean Average Precision(mAP) serves as a measure of the accuracy and precision in detecting objects across multiple classes. By comparing the performance of different YOLO versions, this study intends to assist users in selecting the most suitable model based on their specific requirements. The research findings contribute to a better understanding of the trade-offs between accuracy and computational efficiency, enabling informed decisions for practical applications of object detection
IoT Based Hand Gesture Control Wheelchair for Disabled Person

A Hand Gesture-controlled automated wheelchair employing an Arduino-based microcontroller was designed and implemented. The primary goal of this research is to control a wheelchair via hand-wrist motion. Should there be any issues with the Hand Gesture control. Falling detection, an automatic brake system, and other elements are included in the design as well. To create the wheelchair prototype, relay-based H-Bridge Motor Driver Circuits, Arduino UNO R3, Adxl 335, Ultrasonic sensor, Universal Buzzer, nRF Transceiver, and were utilized. In that any person may operate the wheelchair with just a wrist movement, the way it is constructed makes it relatively simple. Other wheelchair designs that incorporate sophisticated computer systems and can process biological inputs like EEG, EMG, and EOG, among others, have been put forth in the literature. For very elderly or mobility-impaired people, it can be difficult to record these biosignals accurately. To control these wheelchairs, large computing systems and complicated signal processing are needed. The Hand Gesture control system in the proposed architecture avoids the need for additional sensors and intricate biological signal processing technologies. Due to its simpler use and lower cost compared to existing automated wheelchair designs, the suggested technology will have a significant impact on society.
Deep Learning Based Object Detection in Surveillance Video

Object Tracking from surveillance video has become a popular research area in the fields of image processing, video processing, and computer vision. The abilityto accurately identify specific objects from surveillance footage can have a significant impact on public safety and security. Our research employs YOLOv5, a state-of-the-art deep learning algorithm, for object detection in this domain. YOLOv5 is particularly effective in identifying objects in real time with high accuracy and speed. In this paper, we focus on using YOLOv5 for the detection of common objects in our daily lives. With various crimes and thefts occurring in society, it is challenging to guarantee everyone's safety on the streets, highways, or roadsides. Therefore, our research aims to address this issue by exploring how we can reduce manual labor and improve safety through the detection of specific objects in different scenarios. For instance, we can identify individuals based on their attire or if they are carrying a weapon or dangerous item. By fine-tuning our model, we can accurately detect and identify the required objects, allowing us to take appropriate action and enhance public safety. Overall, our research shows the potential of YOLOv5 for object detection in specific scenarios and demonstrates how it can be used to enhance public safety and security.
Design and Implementation of Integrated Circuits Using Enhanced Grey Swarm Optimized Artificial Neural Network

This work introduces computer-aided design (CAD) software that can automatically design and optimise Integrated Circuit (IC). To determine the device sizes that maximise performance goals by using artificial neural networks (ANNs). To accelerate the performance of ANN by using enhanced grey swarm optimization (EGSO). As neural networks can learn and generalise from data, they may be used to create the model even if the formulae for the individual components are not accessible. The TSMC <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.18\ \mu\mathrm{m}$</tex> CMOS process parameters were used in the HSPICE design environment to generate the training data. One real-world application is provided a CMOS technology of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.18\ \mu\mathrm{m}$</tex> to demonstrate the tool's effectiveness. The simulation results show the viability of the suggested strategy for sizing integrated circuits. The novel integrated circuit optimization system may employ parallel computing to optimise faster than the standard method.
Self-Driving Car Simulation Using Reinforcement Learning and Xception Model Tuning

The main idea is to have the environment (server) and then agents (clients). The server/client architecture means that the user can of course run both the server and client locally on the same machines, but it could also run the environment (server) on one machine and multiple clients on multiple other machines. For training and predicting at the same time, reinforcement learning is used. To obtain a convincing result in the training process would mean promising improvement in the field of automation and automotives. This simulation will be very helpful in all manners when developed with more real-life objects and circumstances that get into the simulation asset. The improvement in self-driving cars comes along with the advancement in the field of deep learning and data handling. Due to the sheer amount of multidimensional data that a self-driving car could create, a more efficient data handling methods are required to compute better results. There is always a chance to add layers to this particular deep learning framework and make it more efficient as the data captured evolves. The data may evolve in the fields of addition in new sensors, new obstacle identification, addition of more real time features. Progressing further into this study and implementation of a deep learning framework for self-driving cars, it is observed that the sensor data provides sufficient parameters for the estimation of new class variables. The model accuracy and results are compared in a way that is projected towards higher volume of data along with high computational power. This study recommends using cloud service to accommodate the graphical and CPU requirements as the computation for this particular multidimensional data could be very demanding. The understanding of how autonomous vehicles and how well it would be accepted is discussed below, along with the complications it would bring. It's a social change which many will oppose in the beginning but will slowly adopt as science improves and moves forward.
Enhancing Breast Cancer Classification in Mammography Images Using Multi-View Deep Convolutional Neural Networks

Early detection is crucial since breast cancer kills most women. Mammography imaging is used for early detection, although the data are hard to interpret. Deep learning systems excel at breast cancer classification. Most current studies employ one mammography image, which may omit significant features. This study presents a multi-view deep CNN-based breast cancer classification algorithm using mammography images. This approach uses information from the craniocaudal (CC) and mediolateral oblique (MLO) views. To improve breast cancer classification accuracy and robustness, the proposed model considers all these parameters. Researchers build and test the multi-view deep CNN model using mammography images from malignant and non-cancerous patients. This dataset's ground truth labels allow robust performance assessment. The multi-view deep CNN architecture learns unique features from each view, integrating complementing information and improving classification performance. Extensive trials compare the proposed multi-view deep CNN methodology to various state-of-the-art technologies. It outperforms single-view CNN models and feature-based classifiers in breast cancer classification. Multi-view information improves accuracy, sensitivity, and specificity, making breast cancer detection and discrimination more dependable. The proposed model has improved Accuracy 0.92, Sensitivity 0.89, Specificity 0.94, and Precision 0.91. This research uses Multi-View Deep Convolutional Neural Networks to improve mammography breast cancer classification. The model outperforms single-view and feature-based classifiers by adding craniocaudal (CC) and mediolateral oblique (MLO) viewpoints.
Face Identification Method Using Scale Invariant Feature Transform Method for Criminal Management System

Due to growing crime rates in India, big data for criminal records has increased more than ever. The increased database has resulted in slower detection and identification of pre-existing criminals for related crimes. In this new cyber world, India's security is more important than ever. Numerous organized criminal operations are anticipated, some of which might aim to undermine our law enforcement and defense organizations. Technology becomes of utmost importance in such a situation. Video analytics is one use of technology used in law enforcement, and video analytics contains many different parts. Finding patterns in different videos is one of the elements. The task here is to analyze a photograph of a particular person and see its resemblance with respect to the related criminal. Now, if the aforementioned technology is applied to thousands of movies compiled from hundreds of intelligence sources, with cross-checks across all recordings, highly important information on trends in any kind of organized crime can be discovered. This study has used scale invariant feature transform (SITF) to compare the features of images. Therefore, a system where the details of the criminal can be observed on a descriptive basis by facial recognition will be of great use for the investigation.
Unsupervised Deep Learning Approaches for Anomaly Detection in IoT Data Streams

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification.
Trends and Challenges of Block Chain in Electronic Health Record System

The exponential growth in the quantity of digital information is accumulated in electronic health records. Electronic health records are now booming in providing patients, doctors and health care management in examining the profiles of patients and giving proper treatment when emergency. Thus, it has also led to huge impetus on exploiting the EHR to aid clinical decision support systems. The enormous increase of data breach, criminal deception, unjustifiable claiming and thefts in patients' data make difficult for health care systems to provide quality oriented care to patients by contradict it needs an effective authentication mechanisms and secured data storage. Blockchain technology recently have immensely attracting the healthcare industries with its storage methods, distributed and immutability nature of securing the data. There is a growing research body in this direction to develop useful insights for secured EHR. The main objective of the research is to identify various methods, frameworks, regulations and perform an extensive review. Recent advances in deep learning techniques with blockchain technology have contributed to secure applications using EHR. Towards this, overview of recent advancements and techniques employed to analyze EHR data securely is introduced. Reviews of the literature discuss the challenges of different approaches. In addition to that effective cryptographic authentication mechanisms that aid future research improvements and trigger innovative applications in healthcare will be highly beneficial for deploying an improved block chain security framework.
Unveiling Sentiment Analysis: Exploring Techniques and Navigating Challenges

Sentiment Analysis is analyzing text and extracting information like negative or positive opinion. It is also known as opinion mining. Due to the increase in social media usage sentiment analysis has become increasingly important as there is a lot of human generated data for example comments, reviews etc. that can be used to understand the sentiments of a person. Large organizations work on sentiment analysis systems. This system helps them in real world data analytics such as customer reviews on their product, brand engagement, surveys, etc. Moreover, this review paper encompasses a concise summary of relevant research papers, providing a holistic view of the advancements in sentiment analysis. By unraveling the techniques and exploring the challenges, this paper aims to contribute to the deeper understanding of sentiment analysis, paving the way for future improvements in this field.
Detecting Background Dynamic Scenes using Naive Bayes Classifier Analysis Compared to CNN Analysis

Surveillance, object tracking, and autonomous vehicles rely on background dynamic scene detection and analysis. This paper compares the Naive Bayes classifier and CNNs for background emotional scene recognition. Background passionate scenes provide obstacles with complicated and unpredictable motion patterns, and the study begins. A professionally managed dataset includes moving items, changing illumination, and environmental variations. Pre-processing and annotation help with training and evaluation. Background dynamic scene detection begins with Naive Bayes. Naive Bayes, a probabilistic approach that presupposes feature independence, is suitable for classification. Different feature extraction methods and Naive Bayes algorithms are used to evaluate background dynamics capture. Next, CNN algorithms consider the dataset. CNNs are great at detecting spatial connections in image and video data. To maximize background dynamic scene identification, CNN architectures are created and trained using different network depths, kernel sizes, and pooling algorithms. Accuracy, precision, recall, and F1-score are used to evaluate Naive Bayes and CNN algorithms. The results are extensively evaluated to assess each method's background dynamic scene-handling capabilities.