Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition Using Smartphone Sensors

In day-to-day existence, people act on many tasks. It is vital to record and analyze the daily presence of individual people. Hence it could assist with relieving a few medical conditions and different issues. Human Activity Recognizing is a key component of research topics in computer vision for different sectors like security monitoring, healthcare and human-computer association, and sports. Nowadays, the smartphone has become popular and helpful for people. Because smartphone has many various and effective sensors, in this paper, we have used smartphone sensors: an accelerometer and gyroscope to detect human activity. In our research, we collected 30 study participants labeled datasets between ages nine-teen to four-ty-eight (19-48) years who have executed actions such as activities of daily life including sitting, walking, standing, walking up or down stairs, and lying down while using a smartphone equipped with such sensors. The objective is to do each of the six activities in the correct order. Two sets of the recorded dataset were randomly chosen, with 70% of participants 30% chosen to produce test data, and the remaining 70% to produce training data. The results were gained along with compared by supervised classification algorithms such as Random Forest, decision trees, KNN method, Support vector machines, and Logistic Regression. By comparing those algorithms, we gained the best results accuracy from Logistic Regression which is 96.21%.
Blockchain Based Farmer’s Portal for Agri-Governance

Blockchain is a technology stores transaction confirmation in the form of crypto-currency. It develops the records of any digital asset transparent and unchangeable and works without involving any middlemen or third-party intermediary. They contribute security and set boundaries to the assets. This demonstrates how the farmer's portal uses blockchain technology to store footage of crop sales and purchases and to develop a model to maintain all the transactions between government initiatives and farmers based on farm subsidies. This farmer's portal designed on basis of blockchain helps those farmers to sell their products directly to the government and in receiving the subsidies without inclusion of any middle men or third-party sites. This helps the farmers in increasing the desired amount directly to themselves. It includes the farmers and government itself. The proposed solution designs an interface by using the python's integration with the blockchain system as a programming language will maintain the contract of trade to the advantage of vendors, farmers, and individuals. The information pertaining to farmers, the government, buying and selling an item, farm subsidies, and the total value of transactions is stored using this interface. Blockchain technology's immutability will make it easier for farmers to obtain the actual price of their harvested crops and reduce the cost of buying and selling crops, helps in receiving the subsidies compared to traditional methods and third-party involvement.
Fast Anomaly Detection For Multivariate Industrial Time Series Data

Anomaly detection is used to identify/predict atypical patterns or outliers within datasets. It proves highly beneficial in numerous applications like industrial monitoring, healthcare and cybersecurity etc. With the advent of Industry 4.0, system automation is rapidly increasing, resulting in an exponential surge in data generation. In a factory setting, a sudden deviation in signal values can result in significant losses. Having a robust anomaly detection system in place becomes crucial to promptly identify and flag anomalies, thereby minimizing potential losses. In this paper, the proposal entails a down-sampling technique that offers advantages in terms of mitigating noise, reducing storage requirements, and diminishing computational demands by 88% for a representative dataset. Additionally, an anomaly interval detection algorithm designed to label data is introduced by us, thereby facilitating its application in supervised anomaly detection methods and predictive anomaly detection.
A Comparative Analysis on the Effectiveness of GAN Performance

Generative Adversarial Networks (GANs) have emerged as a potent framework in the discipline of Artificial Intelligence (AI) for generating realistic synthetic data. With the increasing interest and advancements in GANs, there is obligation for a detailed comparative study to comprehend the competencies and vulnerabilities of different GAN variants. This paper sets forth a comprehensive study and comparison of various types of Generative Adversarial Networks (GANs) and their performance in generating high-quality images. GANs have gained popularity in recent years due to their capacity to generate realistic synthetic images. However, the effectiveness of GANs varies depending on the architecture and parameters employed. We have evaluated and compared the performance of different types of GANs, including DCGAN, SRGAN, and CGAN, on benchmark datasets such as CIFAR-10 and MNIST. The evaluation metrics include image quality, standard GAN loss functions and Fréchet inception distance (FID). The results demonstrate that the performance of GANs is highly dependent on the dataset and architecture used, with no single GAN type dominating across all datasets. This comparative study serves as a valuable resource for researchers and practitioners in AI, providing a foundation for selecting the appropriate GAN architecture for specific generative modeling tasks.
Role of Artificial Intelligence in Cyberbullying and Cyberhate Detection

Cyberbullying and Cyberhate are significantly intensified with a rise in social media users. Rude and derogatory terminology is prevalent on social media, attacking individuals or communities. Bullying and hate are challenging to identify due to their appearance as sarcasm or implicit hatred. Various Artificial intelligence-inspired models are being deployed to curb and recognize widespread cyberbullying and hate. This study provides a detailed analysis of the role of AI in detecting Cyberbullying and Cyberhate using Scientometric analysis. The Scientometric study uses the Scopus database, including all the publications till 2023. The results identify major contributors, significant publications, prominent journals, core research areas, and emerging trends that guide future researchers to explore eminent topics.
Emotion Classification and Respective Sticker Mapping Using Convolutional Neural Networks

The utilization of social media is aggravating and so is the demand of using messaging apps, to stay connected. People now not only send text messages but also prefer using stickers which is the new alternative to emojis. While an emoji can only portray emotion, stickers are used to graphically illustrate and capture the human mind’s attention by being more relatable and accurate. One thing to consider here is that men and women have a different choice of emoji to express their emotion so a random emoji may be ambiguous, i.e., confusing to the other gender. Now, emotion analysis has gained significant importance through online media in recent years. Thus, we have tried to primarily focus on two steps, namely emotion classification and gender analysis for sticker generation. Therefore, this paper employs machine learning models to classify emotions and predict gender to generate accurate stickers. Convolutional Neural Networks (CNNs) are highly productive in feature extraction and object classification on images which is why we have used a CNN for emotion analysis and another CNN for gender analysis combining both we can map the stickers from a prebuilt directory.
Neoteric Advancements in Neural Automatic Text Summarization: A Comprehensive Survey

Natural Language Processing (NLP) aids computers in understanding human language well enough to converse naturally. Text summarization is a very useful and important part of NLP. Text summarising is the process of creating an organised, logical, and concise summary of a large text document. This paper offers an extensive analysis of current developments in neural automated text summarization (ATS). The model architectures, including pre-processing and feature extraction, are discussed in detail. The paper also discusses the competitive efficiency of the widely recognized ATS architectures on different datasets. We have discussed below the various aspects of each technique that researchers could further refer to help develop newer and more efficient algorithms to solve the text summarization problem. Finally, we look ahead to the future and bring this study to a close.
Sketch to Image synthesis using attention based contextual GAN

Generating realistic and high-quality images from sketches remains a challenging task in the fields of computer vision and graphics. Although deep learning approaches, specifically Generative Adversarial Networks (GANs), have showed promise in generating images in this area, issues such as image quality and diversity persist. In this study, a novel approach for generating human faces from sketches was proposed by leveraging a contextual GAN with attention mechanisms and ResNet-50 feature extraction. Proposed method aims to enhance image quality by incorporating attention mechanisms. Specifically, we extract relevant features from the input sketch using the ResNet-50 feature extraction technique, which are then utilized to guide the image synthesis process. To evaluate the model’s performance, a comparison is made with existing approaches using the CHUK dataset, assessing the results based on the Structural Similarity Index (SSIM) score. The proposed technique achieves an impressive SSIM score of 0.78, outperforming other methods in generating realistic and faithful representations of the original images.
Image Inpainting of Synthetic Images Generated from Textual Data

Automatic synthesis of realistic images is complex; even cutting-edge AI and machine learning systems sometimes suffer from not fulfilling this expectation. However, the development of image processing has made it possible to perform operations on an image to improve or extract information from it and synthesize images from textual descriptions, making this a prominent area of study. Based on the idea of text-guided image production, the image inpainting task is replacing the damaged parts of an image with new, appropriate features that make sense in the given context. Researchers in this area have made some encouraging advances using neural image inpainting techniques. In contrast to previously developed text-guided image production methods, inpainting models must first compare the semantic content of the provided text to the remaining portion of the image to determine the semantic information for the missing section. Providing a mask and a text prompt with the information you need to make the desired changes can alter certain parts of an image. To boost the semantic similarity between the produced image and the text, an image-text matching loss is utilised. Further research is needed to resolve the challenge of guessing the missed content with only the context pixels. We propose a computationally efficient system to perform image inpainting on synthetic images that have been generated from textual data while ensuring visual coherence and accuracy.
Enhancing Customer Experience and Sales Performance in a Retail Store Using Association Rule Mining and Market Basket Analysis

The retail business grows steadily year after year andemploys an abounding amounts of people globally, especially with the soaring popularity of online shopping. The competitive character of this fast-paced sector has been increasingly evident in recent years. Customers desire to blend the advantages of old purchasing habits with the ease of use of new technology. Retailers must thus guarantee that product quality is maintained when it comes to satisfying customer demands and requirements. This research paper demonstrates the potential value of advanced data analytics techniques in improving customer experience and sales performance in a retail store. Apriori, FP-Growth, and Eclat algorithms are applied in the real time transactional data to discover sociations and patterns in transactional data. Support, confidence and lift ratio parameters are used and apriori algorithm puts out several candidate item sets of increasing lengths and prunes those that fail to offer the assistance that is required threshold. We identified lift values are more when considering frozen meat, milk, and yogurt. if the customer decides to buy any of these items together, there is a chance that the customer will buy 3rd item from that group. Research arrived High confidence score is for Items like Semi Finished Bread and Milk so these products should be sold together, Followed by Packaged food and rolls. As retailers continue to face increasing competition and pressure to improve their operations, The aforementioned techniques may provide you a useful tool to comprehend consumer buying habits and tastes and for utilising that knowledge to come up with data-driven decisions that optimise product placement, enhance customer satisfaction, and attract sales.
eGRUMET: Enhanced Gated Recurrent Unit Machine for English to Kannada lingual Translation

A branch of natural language processing called neural machine translation (NMT) focuses on using artificial neural networks for translating textual content in one language to another, preserving the semantics of the source language. The ability to provide high-quality translations that are frequently comparable to those generated by human translators is a cogent need and therefore, NMT systems have gained popularity. This article proposes a novel gated recurrent unit (GRU) machine that is functionally enhanced and tailored for English to Kannada lingual translation. The input data is first pre-processed by removing stop words, followed by the process of stemming and lemmatization. The approach described in this paper compares a primitive GRU system with the proposed system that is structurally denser and efficient. The novelty of the translation system has been achieved by two newly introduced blocks in the attention layer of the GRU machine. The BiLingual Evaluation Understudy (BLEU) performance evaluation of the enhanced GRU machine is performed on a dataset composed of English text with corresponding semantically equivalent Kannada text in order to test the efficacy of the method. The performance metric of time taken for translation of text from English to Kannada has been computed for a basic GRU system and the proposed system on multiple sentence-pairs. Both performance measures indicate better values with the proposed method.
Automated Ship Classification and Tracking in Satellite Imagery using Advanced Deep Learning Models

The identification of ships using satellite imagery has been a subject of continuous research due to its vital importance in maritime surveillance and navigation. It is challenging to recognize ships in satellite photographs due to the wide range of ship sizes and the complex backgrounds. In this study, we propose a system for classifying ships that combines the benefits of two deep-learning models, YOLOv3 and DenseNet. We began using YOLOv3 because it is a well-liked object identification model. However, we discovered that YOLOv3 is unable to recognize small objects, such as miniature ships, in satellite photographs. To get around this problem, we employed DenseNet, which is known for its ability to distinguish both large and small objects. However, DenseNet requires a significant amount of RAM for computations, which might be a disadvantage when resources are few. To get around this limitation, we coupled ResNet with DenseNet, a popular deep learning model recognized for its processing efficiency. As demonstrated by our recommended solution, which effectively recognizes ships in satellite data by combining the advantages of YOLOv3, DenseNet, and ResNet, ResNet and DenseNet working together achieved greater accuracy and more efficient use of memory. Our results demonstrate the efficacy of our approach and the potential applications to real-world issues like maritime surveillance and navigation.
Explanatory Case Study: Digital Forensics Investigation on DJI Spark Drone

Drones are now widely used and have grown in popularity in recent years. However, as a result of their greater use, there have been more crimes involving drones. In this explanatory case study, digital forensics methods are used to analyze a DJI Spark drone discovered at a crime scene. The purpose of this inquiry is to obtain vital data that might help with the investigation of drone-related crimes. The drone’s memory card and remote controller, which have flight records, pictures, and videos on them, have important details about the crime and how the drone was used. We gather and examine this material using digital forensics techniques. Investigative personnel will then be able to recognize potential suspects and comprehend the drone’s participation in the crime. Important details, such as the drone’s flight path, the location of the murder scene, and the identities of the people involved, are revealed by the recovered data. This explanatory case study emphasizes the importance of digital forensics in the analysis of drone-related crimes. Drones will probably be employed more frequently in criminal acts as they become more prevalent in society. Investigators may keep ahead of the curve and efficiently use drones as a source of priceless evidence in criminal investigations by using competent digital forensics techniques.
Sentiment Analysis of OYO Hotel Reviews Using NLP

Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language.This study presents an aspect-based novel approach for sentiment analysis of customer reviews for OYO hotels.The sentiment analysis conducted in this study is founded on the primary amenities individuals consider when making their final decisions regarding hotels. These key factors include the quality of food offerings, the attributes of the rooms, and the service provided by the staff. By analyzing sentiments expressed in relation to these crucial aspects, we aim to gain valuable insights into the preferences and perceptions of hotel guests.This analysis holds the potential to offer significant benefits not only to customers but also to hotels themselves.It enables the identification of areas of strength and areas requiring improvement. By leveraging sentiment analysis techniques, hotels can gain valuable insights into the specific aspects of their operations that excel and those that necessitate enhancement, thus facilitating targeted efforts to elevate the overall guest experience. This analytical approach serves as a valuable tool for guiding hotels in their quest for continuous improvement and aligning their strategic initiatives with customer preferences and satisfaction.
Image encryption using One Dimensional Chaotic Map and transmission Through OFDM system

It is important to prevent unauthorized access to data whether it is being transferred over a wireless network or stored on a personal computer. The incorporation of secure and highly dependable digital data, required to transmit from source to destination over a communication channel, is becoming more and more important for modern wireless communications. Confidentiality is achieved in two levels, the first occurs at the data level, while the second occurs at the network level. Data-level security employs cryptographic techniques. This paper proposed a simple algorithm and 1D chaotic map have been cascaded to get a high efficiency as complex algorithms or multi-dimensional chaotic maps. n this study, a variety of encryption techniques, including a cipher and permutation algorithms, are combined with the OFDM system to establish a secure image transmission over an AWGN channel. Security analysis is performed to evaluate the quality and security of the encryption process. For image transmission evaluation, the PSNR and BER are utilized. According to the statistical results, the proposed image encryption scheme is secure and efficient. Comparison with the previous studies is taking place, which shows this system is competitive with previous works.
Facial Detection and Anonymization for Privacy Protection from Social Networking Sites(SNS)

A social networking site (SNS) is an incredible information exchange platform that helps advance and shares a lot of useful information regarding items, news, training, travel, medical care, etc. However, there are a lot of risks involved with protecting a person's safety and security. Posts of photos online can be shared, labeled, and reposted again without the permission of the individuals pictured. Using facial recognition, we propose a framework for identifying each person and examining the web-based account connections between people. Replicated, labeled, or replicated posts will be checked, regardless of whether they're by a known or unknown individual. Whenever a person is obscure, the images are convolved through a low-pass filter in order to eliminate clamor and edges that obstruct the image. The proposed solution used deep-learning-based VGG19 engineering to characterize pictures, Haar overflow viola jones calculations for location, and PC vision-based obscuring methods to remove objects from the picture. Thus, a region under the ROC curve (AUC) precision of 88% was achieved. There was a 91.53% accuracy achieved by using the proposed model, and we also assessed the model using Precision, Accuracy, Review, and F1-Score matrices, which yielded an accuracy of 70.83%, 82%, 71%, and 0.68% respectively.
Sentiment Analysis of Tweets during Covid-19 Lockdown in India

Sentiment analysis has become most important and most popular field of natural language processing(NLP). Emotions can be extracted out of text using sentiment analysis. Everyone is using social media to receive and communicate different information on a big scale during COVID-19. The evaluation of such type of content is helpful for decision maker authorities. In this work, the sentiments of Indians during Lockdown are analysed using NLP and Machine Learning models. Tweepy API is used to extract data from Twitter and annotation is done using TextBlob and VADER libraries. Data preprocessing is done using NLTK. The experimental results show that Ensemble model with unigram has good performance. After analysing the sentiments of Indians, it is found that major people are in favour of lockdown decision made by the Indian government.
Block chain Enabled Farmer Centric Supply chain Management using Reinforcement learning

Farmers produce agri-food items for which purchasers pay in agri-food supply chains (ASCs). In the interim, purchasers accentuate the significance of guaranteeing the wellbeing of agri-food, and ranchers expect expanded income. Detectability and the board of agri-food items productively presents huge difficulties because of ASCs' dynamic and intricacy. Notwithstanding, most of current frameworks can't satisfy the necessities for ASC discernibility and the board. To begin, in order to guarantee decentralized security for agri-food, we develop a blockchain-based ASC design for item discernibility and use information in ASCs to address these issues. Then, an amazing decision is made about how to transport and store agri-food items to get the most out of them and make the most money using a Deep Reinforcement Learning (DR-SCM) method. A number of replication experiments are carried out so that the proposed blockchain- based architecture and DR-SCM system can be used in a variety of ASC scenarios. Implementing the recommended blockchain-based ASC configuration ensures meticulous thing conspicuousness, according to the disclosures. DR-SCM may also use Q-learning and heuristic approaches to boost item production.