Reviewerly/exHarmony huggingface dataset:
Optimized Ensemble Approach for Multi-model Event Detection in Big data

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately.
A Comprehensive Review of Image Restoration and Noise Reduction Techniques

Images play a crucial role in modern life and find applications in diverse fields, ranging from preserving memories to conducting scientific research. However, images often suffer from various forms of degradation such as blur, noise, and contrast loss. These degradations make images difficult to interpret, reduce their visual quality, and limit their practical applications.&#x0D; To overcome these challenges, image restoration and noise reduction techniques have been developed to recover degraded images and enhance their quality. These techniques have gained significant importance in recent years, especially with the increasing use of digital imaging in various fields such as medical imaging, surveillance, satellite imaging, and many others.&#x0D; This paper presents a comprehensive review of image restoration and noise reduction techniques, encompassing spatial and frequency domain methods, and deep learning-based techniques. The paper also discusses the evaluation metrics utilized to assess the effectiveness of these techniques and explores future research directions in this field. The primary objective of this paper is to offer a comprehensive understanding of the concepts and methods involved in image restoration and noise reduction.
Adapting Machine Learning Techniques for Developing Automatic Q&amp;A Interaction Module for Translation Robots based on NLP

Research on Automatic Q&amp;A Interaction Module of Computer-based Translation Robot is a study that focuses on developing an automatic question and answer (Q&amp;A) interaction module for computer-based translation robots. The goal of the research is to enhance the capability of translation robots to perform more human-like interactions with users, particularly in terms of providing more efficient and accurate translations. In this paper proposed a Conditional Random Field Discriminative Analysis (CRFDA) for feature extraction to derive translation robot with Q&amp;A. The proposed CRFDA model comprises of the discriminative analysis for the CRF model. The estimation CRF model uses the bi-directional classifier for the estimation of the feature vector. Finally, the classification is performed with the voting-based classification model for feature extraction. The performance of the CRFDA model is examined based on the Name Entity (Nes) in the TempVal1 &amp;2 dataset. The extraction is based on the strict and relaxed feature model for the exact match and slight variation. The simulation analysis expressed that proposed CRFDA model achieves a classification accuracy of 91% which is significantly higher than the state-of-art techniques.
Automatic Clustering by Spider Monkey Optimisation with Tabu Search Algorithm

Objective: To design automatic data clustering algorithm that find number of clusters automatically with balance between exploration and exploitation search space. Methods: This work proposes Spider monkey optimisation with tabu search algorithm named as SMOTS for automatic data clustering. In this algorithm, the local search of spider monkey is improved with tabu search algorithm. For better results, compact separated index with Gaussian kernel distribution is introduced as a fitness function. The experiments are performed on Vowel, Iris, Wine, Seed, E.coli and Thyroid data sets. The results are validated with cluster optimality, inter and intra cluster distances with 5 well known and 7 recently published algorithms like DE, GA, GWO, WOA, PSO, AHPSOM, ACICA, ACDCSA, AC-MeanABC, TMKGSO, Black Hole k-Means, and EOAK-means. To test the statistical significance of the proposed algorithm an unpaired t-test is performed between SMOTS and second best algorithms on mean inter cluster distance. Findings: In comparison with well-known clustering algorithm on six data set SMOTS produced 100%, 33.33%, 83.33% accurate results on cluster optimality, intra and inter cluster distance respectively. In comparison with recently published algorithms on six data set SMOTS produced 50%, 66.66%, 50% accurate results on cluster optimality, intra and inter cluster distance respectively. The hypothesis testing results shows that p-value of the t-test is less than 1% except vowel data set means SMOTS is highly statistically significant compare to second best algorithms. Novelty: In real life data set information about number of cluster is rarely available and this produced faulty results. Proposed method can process data without any prior information of number of clusters and data distribution with accurate results. Keywords: Spider Monkey Optimisation; Tabu Search; Automatic Clustering; Neighbour Search; Swarm Intelligence 2
Insights from a Comparative Study on the Variety, Velocity, Veracity, and Viability of Crowdsourced and Professional Fact-Checking Services

The tension between the increasing need for fact-checking and the limited capacity of fact-check providers inspired several crowdsourced approaches to address this challenge. However, little is known about how effectively crowdsourced fact-checking might perform in the real world at a large scale. We fill this gap by evaluating a Taiwanese crowdsourced fact-checking community and two professional fact-checking sites from four dimensions: Variety, Velocity, Veracity, and Viability. Our analysis shows the different focus these two types of sites have in terms of topic coverage (variety) and demonstrates that while crowdsourced fact-checkers are much faster than professionals (velocity) to answer new requests, these fact-checkers often build on the existing professional knowledge for repeated requests. In addition, our findings indicate that the accuracy of the crowdsourced community (veracity) parallels that of the professional sources; and that the crowdsourced fact-checks are perceived quite close to professionals in terms of objectivity, clarity, and persuasiveness (viability).
The Acceptance of TikTok as a Tool in English Language Learning among University Students

TikTok is considered a potential tool for enhancing the development of students’ language skills and proficiency. However, research on the extent of students’ acceptance of using it as a language learning tool is limited. Therefore, this study aimed to examine the level of acceptance of TikTok in English language learning and the factors contributing to the actual use of TikTok among undergraduate students in Malaysia. This study employed quantitative research methodology by disseminating a 32-item questionnaire to 200 undergraduate students at a public Malaysian university via a Google Form link. Sections A, B, C, D, E, and F of the questionnaire sought responses on the demographic profile, perceived usefulness, ease of use, attitudes, behavioural intention, and actual use of TikTok in English language acquisition, respectively. The findings revealed that the average scores for the individual items and the overall scores for each component being studied were within the range of 3 to 4.1 on a 5-point Likert scale. This result indicated that the student’s level of acceptance was above average. Students’ behavioural intentions were also found to be the most significant factor in determining whether or not TikTok was used for English language instruction. Students acknowledged the use of TikTok in English language learning, suggesting that it could be used as a pedagogical instrument in English language classrooms. Consequently, the findings highlight TikTok’s potential as a language learning tool in English language courses, as it strengthens educational platforms and enhances instruction by giving students more ways to learn languages.
Use of Fibonacci Sequence in Project Estimation

Background: Estimation and planning are crucial for successful software development projects, ensuring project readiness and preventing potential issues. Planning poker, an effective estimation method for agile teams, combines professional judgment, analogy, and disaggregation for fast, accurate estimates. The estimation technique Planning Poker is frequently employed in agile software development. When submitting estimates, Planning Poker only uses specific numbers on its cards, such as 0, 1, 2, 3, 5, 8, 13, 20, 40, and 100. This is inaccurately referred to in this work as a Fibonacci scale. Despite the frequent use of the Fibonacci scale in agile estimation, it is unknown how it influences the estimation process. Objectives: We conducted a study based on a software provider who estimates projects using a variety of estimation methods. Method: We used the planning poker technique for estimation. Findings: Using a Fibonacci scale as opposed to the conventional linear scale resulted in a median decrease in effort estimates. As the developer’s proficiency increased, the disparity between the effort estimates shrank. Novelty: For the purpose of estimating the project of a software development company that is presently in operation, we have utilized the currently available estimation method. Keywords: Planning Poker; Effort Estimation; Fibonacci Sequence; Story Point; Fibonacci Scale
An Entropy Based Alternate Approach to Develop an Ensemble Classifier

Objectives: To develop an alternative approach to improve the performance of ensemble classifiers. Methods: The Principle of Maximum Entropy and a precision based diversity measure are used to develop the model. Here joint entropy values are used which strongly reflect the combined uncertainties in the actual prediction and the prediction obtained during the training phase. Towards that purpose probabilistic confusion matrix has been used. The performance of the proposed method is tested against twenty different datasets. Each dataset is different from one another in terms of the number of classes and features. The results have been compared with several existing state of art ensemble methods uch as Bagging, AdaBoost, Naive Bayes (NB), Weighted Majority Vote (WMV) and TransEnsemble Classifier (TrEnL). Findings:The results show that the proposed method can select the best among the classifiers and perform better when compared to conventional classifiers. Novelty: The maximum entropy principle is applied at the final prediction probability matrix so that the method can be used as a generalized technique to improve the performance of ensemble classifiers. Keywords: Ensemble; Maximum Entropy; Confusion Matrix; Weighted Entropy; Probabilistic Confusion Matrix
LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases.
CNN based Blockchain Information Protection Model for Emerging Cloud Applications

In the age of mobile internet, the amount of data is growing, and ability to process service data is always getting better. So, protecting data privacy and making sure the service environment is trustworthy have become very important. This paper looks at a trusted privacy service computing model for common uses of convolutional neural networks. The goal is to find data and model calculation methods that support homomorphic encryption to protect data privacy. Build a service process certificate and a method for distributing calculation rights based on blockchain and new contract technology to make sure that service calculations are open, trustworthy, and easy to track. Explore how the new cloud environment resource data service model helps resource providers, model owners, and users work together to make the most of their resources and grow the sharing economy. Lastly, experiments are done to figure out how the model protects privacy.
9/7 LIFT Reconfigurable Architecture Implementation for Image Authentication

Considering the information system medical images are the most sensitive and critical types of data. Transferring medical images over the internet requires the use of authentication algorithms that are resistant to attacks. Another aspect is confidentiality for secure storage and transfer of medical images. The proposed study presents an embedding technique to improve the security of medical images. As a part of preprocessing that involves removing the high-frequency components, Gaussian filters are used. To get LL band features CDF9/7 wavelet is employed. In a similar way, for the cover image, the LL band features are obtained. In order to get the 1st level of encryption the technique of alpha blending is used. It combines the LL band features of the secret image and cover images whereas LH, HL, and HH bands are applied to Inverse CDF 9/7. The resulting encrypted image along with the key obtained through LH, HL, and HH bands is transferred. The produced key adds an extra layer of protection, and similarly, the receiver does the reverse action to acquire the original secret image. The PSNR acquired from the suggested technique is compared to PSNR obtained from existing techniques to validate the results. Performance is quantified in terms of PSNR. A Spartan 6 FPGA board is used to synthesize the complete architecture in order to compare hardware consumption.
Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model

Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction.
Integrated Approach for Emotion Detection via Speech and Text Analysis

This paper aims to provide a comprehensive solution for effective reviews using deep learning models. Customers often have difficulty to find accurate reviews of the things they are interested in. The proposed framework implements a review mechanism to address this problem, which will give customers relevant reviews based on video reviews supplied in the product description. The goal of this system is to turn video reviews into a particular rating so that viewers may get a summary of the review without having to watch the full thing by simply glancing at the rating. Deep learning neural networks are used by the model for both text and audio processing in order to achieve this. The well-known RAVDESS dataset serves as the basis for the audio model's training and offers a wide range of emotional expressions. The suggested system uses two methods to provide reviews: text-based natural language processing and audio frequency spectrograms. By utilizing these two techniques, it may provide consumers accurate and trustworthy ratings while guaranteeing that the review procedure is not impeded. The aim is achieved with high accuracy to ensure that users can make informed decisions when purchasing products based on the provided reviews. With the aid of this review system, customers will be able to quickly find out crucial details about a product they are interested in, thus increasing their pleasure and loyalty.
HYDRO-METEOROLOGICAL ANALYSIS OF EXTREME WEATHER IN INDONESIA USING COUPLED ATMOSPHERIC-HYDROLOGICAL MODEL

In recent years, hydro-meteorological hazard in the area involving coastlines has been dominated the disaster occurrence in Indonesia. In the last 10 years, the combination of the extreme weather and the environmental capacity degradation result in more flooding vulnerability. In 11-17 January 2021, heavy flood affected South Kalimantan, Indonesia, with causing many casualties. The flood characteristics was similar with the extreme flood in Jakarta in the preceding year in 2020, and more extreme floods occurred in the following year in 2022. Our goal is to analyze the extreme weather from the hydro-meteorological perspective and identify the key factor behind the high impact flood in South Kalimantan. Two factors, sea surface temperature (SST) and soil moisture (SM) content were highlighted and the sensitivity analysis was performed to identify their influence to the flood response at the time of the event. The coupled atmospheric model of Weather Research and Forecasting (WRF) and the hydrological model extension (WRF-Hydro) will be used to reconstruct the flood event.
Assessment of Security Trepidation in Cloud Applications with Enhanced Encryption Algorithms

To alleviate crank in routine process in IT related work environment we are maintaining information’s in cloud storage even though we affected by pandemic and other natural disaster still can able to access data by avoiding degrade in target process. Members who posses account in cloud no need to have separate high end configuration devices because even less configured devices could connect to cloud and make use of all services using virtual machine. Applications belong to cloud storage intimidated in the aspect of safety. This paper reviews the various security related issues and its causes along with latest cloud security attacks. We discussed about different technology to protect information resides in cloud and analyzed different enhanced algorithm for encryption for securing the data in cloud due to surge use of devices interacting cloud services.