Reviewerly/exHarmony huggingface dataset:
Optimization with Label Weighting Extreme Learning Machine for Human Activity Recognition

Human Activity Recognition (HAR), is a field of study related to the spontaneous detection of day-to-day activities performed by people based on time series data using sensors. Over the past few years, there have been numerous progressions in interconnected sensing technology namely edge computing, cloud, sensors, and IoT. In recent times, deep learning (DL) based method has gained popularity for HAR because they utilize representation learning technique that could identify hidden patterns in data and could automatically generate optimum features from raw input datasets produced from sensors without human interference. This study introduces a new Fire Hawks optimization with Label Weighting Extreme Learning Machine (FHO-LWELM) algorithm for HAR The presented FHO-LWELM technique aims to identify and classify different kinds of human activities. To do so, the presented FHO-LWELM technique exploits the LWELM model for recognition purposes. At the same time, the parameters involved in the LWELM model can be optimally chosen by the use of FHO algorithm and it led to improved recognition rate. The performance validation of the FHO-LWELM technique is examined on HAR dataset and the experimental outcomes stated the improved outcomes of the FHO-LWELM technique.
Smart Face Recognition Application using Deep Learning Neural Networks with Compressed Model Deployment

This research work presents a novel face recognition system with customized face dataset. After completion of the dataset, this dataset will be fed to neural network (or) model and find how much efficient it is in detecting the student. There are different types of neural networks like ANN and its variants like CNN and RNN. For example, a person is affected by fever or cold etc. In that case, through biometric application disease the can be spread from one person to the other persons. To combat this situation, image processing authentication technique is proposed. This research study develops a new html script for accessing the camera to capture images. The input image is then fed to neural network. Here, a customized CNN model is developed. After recognition, the embedding or features of the image will be compared with the dataset. The proposed model will act like a black box, where input must be provided and it will process the image data and provide the recognized output.
A Normalized ANN Model for Earthquake Estimation

Earthquake is one of the most devastating natural catastrophes, mainly because there is rarely any advance notice and hence little opportunity to react. This makes the issue of earthquake prediction highly crucial for human safety. This paper offers a technique for predicting earthquakes by using normalized artificial neural network (ANN). Exploratory Data Analysis (EDA) is applied on the raw dataset to find outliers and the co-relationship between input features. Then, Feature Engineering is performed to normalize the data and remove all unnecessary features. The training data is fed into the neural network model, which generates certain output. Error is calculated based on actual and generated output. Backpropagation algorithm is applied to minimize the error, after which this data is used to train the model. Finally, the Testing data is fed into the model to calculate accuracy and other performance metrics. The outcomes of several experiments are promising. Accuracy of prediction obtained was 94.3%. Also, the training and testing delay recorded were 2.12 seconds and 3.14 seconds respectively.
Application of Feature Selection Methods for Improving Classifcation Accuracy and Run-Time: A Comparison of Performance on Real-World Datasets

Big data are produced in high volume, velocity, veracity, and variety. They present unprecedented opportunities to improve our life that is deeply rooted in the use of information. Machine Learning and Data mining techniques are employed to find useful patterns and insights from the data. Feature selection is among the most important steps of Machine learning and data mining processes and, if carefully configured, will have a direct impact on the performance and reduction of the computational time of the learning algorithms. However, there are challenges in discovering useful patterns from the data due to high dimensionality and data quality issues. As a result, experienced professionals are required to provide guidance or input in almost all the processes, mainly the feature selection process and the selection and tuning of optimal parameters for the classification phase, thus making data mining a manual, expensive task and unsuitable for data that are generated at high velocity. This research study provides an overview of feature selection methods and perform feature selection on real-world datasets. Further, the impact of feature selection on classification accuracy and runtime is evaluated by using Support Vector Machine and Deep Learning. Finally, this research study highlights the importance and need for improved feature selection methods for sustainable, efficient, and green algorithms.
Fine-Tuned BERT with Attention-Based Bi-GRU-CapsNet Framework for Joint Intent Recognition and Slot Filing

In the field of natural language processing (NLP), the two most prominent research areas are slot tagging and intent recognition. Modern joint learning strategies examine the link between slot-tag identification and intent classification, utilising the shared knowledge between the two tasks for their collective advantage.However, methods that combine various variants of pre-trained Bidirectional Encoder Representations from Transformers [BERT] models with attention based capsule networks for joint slot-tag identification and intent detection have not been fully explored.This study proposes a multi-stage framework trained on different versions of BERT models $(\mathrm{B}\mathrm{E}\mathrm{R}\mathrm{T}_{\mathrm{b}\mathrm{a}\mathrm{s}\mathrm{e}}$ and $\mathrm{B}\mathrm{E}\mathrm{R}\mathrm{T}_{\mathrm{l}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{e}})$ with Bidirectional Gated Recurrent Unit [Bi-GRU] and self attention mechanism as intent detection decoder to capture the underlying information and to discover the explicit links.While the capsule network, in accordance with the dynamic routing algorithm, acts as a slot filler decoder in predicting intents and slots and representing the semantic and syntactic relationships. The experimental findings demonstrate that the proposed approach enhances semantic frame accuracy at the sentence level, outperforming various baseline methodologies by a significant margin with a 1.2% improvement in the intent Flscore and 3.24% in the slot Fl-score, relative to the previous state-of-the-art models on the SNIPS datasets.
Advanced Encryption Standard to Prevent Intruders in Email through Cloud Environment

The Advanced Encryption Standard (AES) algorithm is currently one of the most widely used symmetric block ciphers. Its unique structure enables it to be incorporated into various tools and software globally, providing a high level of security. As a result, considerable care must be taken when implementing it. There hasn't been any proof to far that this algorithm can be broken. The AES algorithm provides three different key sizes, namely 128, 192, and 256 bits, all of which use a block size of 128 bits. This research study will provide a detailed explanation of the AES algorithm, including instructions on how to encrypt and decrypt sensitive data using this method. To successfully retrieve the data encrypted using AES, several critical aspects of the algorithm should be considered.
Factors Influencing Video Quality of Experience in Ecologically Valid Experiments: Measurements and a Theoretical Mode

Users' perception of multimedia quality and satisfaction with multimedia services are the subject of various studies in the field of Quality of Experience (QoE). In this respect, subjective studies of quality represent an important part of the multimedia optimization process. However, researchers who measure QoE have to face its multidimensional character and address the fact that quality perception is influenced by numerous factors. To address this issue, experiments measuring QoE often limit the scope of factors influencing subjective judgments by administering laboratory protocols. However, the generalizability of the results gathered with such protocols is limited. The proposed PhD dissertation aims to address this challenge. In order to increase the generalizability of QoE studies we started with an identification of factors influencing user multimedia experience in a natural context. We proposed a new theoretical model of video QoE based on both original research and a literature review. This new theoretical framework allowed us to propose new experimental designs introducing influencing factors one by one in an additive manner. Thanks to the model, we can also propose comparable experiments which could differ in ecological validity. The proposed theoretical framework can be adjusted to other multimedia in the future.
Handwriting Text Recognition using CNN and RNN

In the field of optical character recognition, there are still unans wered research questions regarding the recognition of handwritten text. In this paper, an effective method for developing handwritten handbook recognition systems is proposed This article uses a 3-subcaste Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in conjunction with a supervised literacy technique. Although bit chart descriptions of the input samples boost the delicateness of any textbook recognition system, they are used as point vectors in the s ys tern. The objective vectors are pre-processed before the resulting goal variables according to input samples are applied to the CNN. Using samples of each digit in the number 123, the CNN&RNN training procedure is carried out to verify the system's general connection to new inputs. Two different algorithms for literacy are utilized in this study. Cumulative image processing techniques have also been developed to deal with the several characters that are provided in a single image, cocked image, and rotated image. The trained systern provides a better delicacy.
Modified Cuckoo Algorithm (mCA-CNN) for Detection and Diagnosis of Pancreatic Tumor using Region-based Segmentation Techniques

Globally, the pancreatic tumor is one of the principal sources of cancer death. This is because of a deficiency in promising tools for prompt identification of this cancer. Nowadays, the automatic discovery of pancreatic cancers with the help of novel computed tomography is extensively used for the analysis and presentation of pancreatic tumors. Conventional approaches are capable of extracting only low-level features. Tumors in pancreatic malignant that extremely impends the life span of infected people. Categorization of tumors without human intervention is a really challenging task. But image segmentation and classification have real-world complications, such as unbalanced categorization accuracy, a heavy workload, and the final outcomes determined by the subjective judgment of the medical expert during the analysis and presentation of pancreatic cancers. In addition, precise prediction of pancreatic cancers could help the clinical experts to provide the best therapeutic schedule for infected people of various stages. In this research work, Region-Based Segmentation (RBS) is used to segment the input images of pancreatic cancers. In case of feature extraction, Particle Swarm Optimization (PSO) _ Convolutional Neural Network (CNN), Cuckoo Algorithm _ Convolutional Neural Network (CNN), Modified Cuckoo Algorithm _ Convolutional Neural Network (CNN) are adopted. Results are evaluated based on Accuracy, Precision, Recall, time period. Results have proven that the proposed Modified Cuckoo Algorithm_ Convolutional Neural Network (CNN) performs better in all aspects.
Blockchain based Secure Event Management System using NLP and RNN Algorithm

When it comes to the daily news about incidents or disasters that occur in nature or are man-made, exchange of information is among the most contentious issues on social media sites. The suggested system aims to enhance the security and trustworthiness of event information by leveraging the immutability and decentralization properties of blockchain technology, as well as the accuracy and efficiency of NLP and RNN algorithms in processing and analyzing natural language data. The NLP and RNN algorithms are then applied to the data to detect and verify the authenticity and relevance of the event information. The blockchain technology is used to store and distribute the validated event data in a secure and decentralized manner.
Physique- Based Human Activity Recognition Using Deep Learning Approaches and Smartphone Sensors

Understanding human actions via the analysis of sensor data captured by wearable sensors is the goal of the complex subject of study known as sensor-based human activity recognition (S-HAR). Human participants' characteristics are only periodically included in deep learning (DL) approaches to S-HAR. Recognizing people was challenging for these DL methods because of the variety of physical characteristics people have. To address this challenge, we introduce a physique-based S-HAR architecture that could support deep learning networks to achieve higher identification a ccuracies a nd F1-scores. The HARSense dataset, a publicly available benchmark S-HAR dataset that compiles raw sensor data acquired from smartphones, was employed to build and evaluate five DL networks. A ccording to the experiments, the five models' detection performance improves dramatically when given access to biological data.
Detecting Multi-Pose Masked Face using Haar Cascade and LBP Classifiers

This study presents the Haar cascade and Local Binary Pattern algorithms to detect faces. Facial detection technology can identify a person's face in a pictures or videos. The significance of face detection has increased as modern cultures have developed, primarily because of the necessity for security, including legal requirements, global security, and other pressing situations. This research compares the performance of Haar Cascade and Local Binary Pattern in a real-time system for human face detection. The face detection technique uses both frontal and profile face detectors in a cascade of boosted classifiers to extract the “Haar” traits and apply them. The method integrates profile and frontal face cascades and categorizes the face based on the position by comparing it to a group of faces with comparable ranges. Algorithms are tested on different images of single pose, multi-pose and masked faces. The results show that the Haar cascade classifier outperforms the LBP classifier.
An Improved Sentence Embeddings based Information Retrieval Technique using Query Reformulation

Information retrieval (IR) is a field of study that focuses on gathering, organizing, storing, analyzing, and accessing information. It is used in all applications where searching for information is required through the internet. Selection and ranking of relevant web documents to satisfy users’ information need in response to users’ queries is the most important task associated with IR. To better comprehend the user’s intent and create a more efficient query, the initial query has been reformulated by adding new terms. The semantic text-similarity techniques proposed by researchers in the past demand a significant amount of trained labeled data as well as user intervention; both of them are scarce, hard to capture, and difficult to maintain. In general, these techniques ignored contextual information as well as word order, which causes the problems like data sparsity and latitudinal explosion. Deep learning techniques are now being utilized to find text similarities. Various sentence embedding models exist today that can use vectors to represent complete sentences and their semantics. This helps the search engine to comprehend the context, intent, and different aspects of the user text. So, in this paper, a sentence embedding model based query reformulation (QR) has been proposed for improving document ranking performance using a universal sentence encoder (USE) and a cosine similarity measure. Four standard datasets CACM, CISI, ADI, and Medline are used to perform all the experiments. The outcomes demonstrate the superior performance of the USE-based QR system over the SBERT sentence embedding model by 4.48 %, 5.97%, and 7.2% and 2.1% for ADI, CISI, CACM, and Medline datasets respectively.
Digital Image Encryption Using 256-Bit Advanced Encryption Standard Algorithm

Ensuring secure communication of multimedia messages is crucial for social networking and data sharing platforms. Prevention of data manipulation and theft has led to the development of various encryption techniques, but scope remains for a fast and efficient multi-media encryptor. Advanced Encryption Standard (AES) is mathematically one of the most complex cipher algorithms to crack and has been widely deployed in the banking sector. This paper aims to present a high throughput implementation of 256-bit AES cipher for encrypting digital images and explore its practicality in peer-to-peer communication. Preprocessing of images has been performed to make them suitable for encryption. A detailed study of the encryption results and histogram analysis has been carried out. The proposed algorithm achieved a Peak Signal to Noise Ratio (PSNR) of 61 dB for the decrypted image. Correlation between the input and the decrypted image was found to be 0.994 while the Mean Square Error (MSE) was calculated to be 0.0030.
Advanced Graph Analytics Algorithms On Genre Based Recommending System

It is crucial for online service providers like Netflix to offer their viewers a selection of top-notch movies. In a bipartite network, this kind of challenge can be thought of as a ranking problem. This is a well-known issue that a ranking system can resolve. However, many traditional rating systems have the same flaw they favor older films over recent ones, despite of Basic services the fact that some recent films might have better quality. In the study, we propose a ranking method applying Equality rebalance methodology to minimize the time bias of the ranking in bipartite graphs Environment. Then, using three actual datasets as our ground truth benchmark, we run experiments. The research findings demonstrate that our proposed methodology enhances predictive performance or accuracy by at least 20% and as much as 80% while also lowering the time bias of ranking scores. The contribution of this paper shows the different types of algorithms for recommend the genre type with less computational time.
IoT based Fall Detection System

Nowadays, the majority of people leave their parents alone at home and seek employment in cities. People are more prone to developing visual or hearing issues as they age. Due to these problems, they are most vulnerable to falling. When they fall, they run the chance of suffering many consequences, including death. In order to address this issue, we developed an idea for a fall detection system that would be quite beneficial for the elderly. The acceleration and angular velocity of the elder’s movements are always calculated when they first begin using this technology. The alert is delivered to the family members if the values exceed the threshold. Mail is used to tell those who live far away, but alarm is used to alert those who live close by in order to save the elderly as soon as possible. The MAX30100 is used to measure the elderly person’s heart rate in order to determine how they are doing. GPS to locate the elderly person. As soon as a fall is detected, family members receive an alert with the position and heart rate of the older. Elders are informed about their daily medications using a real-time clock. For the purpose of identifying actual falls, a full algorithm has been developed.
Comparative Study of Multilingual Text Detection and Verification from Complex Scene

In last few decades automotive industry has tremendous development and improvement for In-Vehicle-Infotainment (IVI), Instrumental cluster, Heads up display (HUD).Validation of warnings on HMI contents in Driver information system, In-Vehicle Infotainment, and Centre media Display is a big challenge. Manual testing fails in such scenario because of human error. These warnings can be multilingual and sometimes text can be displayed horizontal or vertical on Human Machine Interface (HMI). The big challenge is automating the text verification for these multilingual scenarios with horizontal/vertical scrolling text. Sometime its difficult when complex background is used for alert or warning in digital clusters. This paper proposes a machine/ deep learning based solution which will be implemented for multilingual text detection and verification irrespective of font size, as well as various background gradients applied on HMI.
Crypto-Transfer Bitcoin and Ethereum: A Brief Overview

Blockchain technology has the potential to revolutionize the way we conduct transactions by offering a secure, decentralized, and transparent system. In this paper, we present a comprehensive overview of a blockchain-based transaction system, highlighting its key features and advantages. We discuss the underlying cryptographic mechanisms and consensus algorithms that ensure the security and integrity of transactions, as well as the various privacy-enhancing features, such as pseudonymity and zero-knowledge proofs. The paper also explores the challenges and limitations of blockchain-based transaction systems and identifies areas for future improvement. This study provides a thorough analysis of the current state of blockchain technology and its potential for transforming the way we conduct transactions, making it an essential resource for researchers, practitioners, and anyone interested in the future of finance and technology.
Integration of Decentralized BlockChain with Cloud &amp; IoT Based SCM

A decentralized blockchain (BC) technology is becoming the disruptive era of the next generation that takes a major role in versatile applications. This integrates with two broad connecting environs cloud computing and the Internet of Things (IoT). It is a promising solution for assuring security for all types of data exchanged in the particular application. The provisioning of security is allowed based on access control mechanisms, user authentication, data authentication, and encrypting data. The development of decentralized blockchain technology toward global use cases has multiple challenges and issues. In this paper, the concept of the cloud of things i.e. BCOT model is studied based on a variety of applied scenarios. It includes a discussion about the participation of blockchain in supply chain management applications. The key challenges in this integration of blockchain with cloud and IoT are detailed, along with the extension of research directions.
Securing and Visualizing Sensor Data on Private Blockchain

In this technological era, technologies like 5G, Blockchain and IoT are growing rapidly which gives birth to problems like data securing of IoT devices. This paper aims to design and develop a secure architectural environment that will provide automation with a security layer of Blockchain technology, that introduces high Security and Privacy. The Overall abstract of this research paper is based on IoT automation System, subdivided into two parts: Real-Time data visualization & Blockchain Network. We use the open-source IoT platform(s) “Hyperledger Explorer” or “ThingsBoard” for real-time data visualization. And use the Hyperledger Fabric blockchain network for providing a private blockchain network.