Reviewerly/exHarmony huggingface dataset:
Framework for IOT Based Real-Time Monitoring System of Rainfall Water Level for Flood Prediction Using LSTM Network

Recent floods have consistently resulted in human casualties in addition to harm to the environment and the economy. People are less likely to be aware of oncoming floods since there is not a reliable early warning system. A deep learning algorithm is suggested in this study to forecast groundwater levels. Even with inadequate data, the model can nevertheless successfully fulfil the prediction objective. In order to determine the degree of temporal dependence that exists between groundwater level and meteorological indicators, a hybrid model called CNN-LSTM-ML is developed. This model makes use of a network structure that is a mix of CNN and LSTM networks. Long Short-Term Memory (LSTM) networks are used by the rainfall forecasting model in order to provide predictions about future rainfall and water level values, which may lead to floods. In order to conduct experiments on the principal finding, historical data from two sites with rainfall and one location with streamflow were used. Additional information was acquired from two water level stations and one rainfall station in order to confirm the basic findings. With assessment errors for historical data MAE, RMSE, and MSE of 0.93, 1.7, and 3.025 correspondingly, the forecasting technique that used LSTM demonstrated great “accuracy” of the outcome reaching more than 92% using IoT. According to these results, the system has the potential to be used as a cure that does not involve the construction of new structures in order to minimize the damage caused by urban floods.
An Optimized Decentralized Trust Approach for Securing IoV Stream Data: A Smart Contract-Driven Framework

The Internet of Vehicles (IoV) is an emerging technology that enables the real-time sharing of data between vehicles, drivers, and other components of the transportation system. IoV has the potential to improve the overall efficiency and safety of the transportation system. However, it also presents several security and privacy challenges that need to be addressed. Cyberattacks, data breaches, and unauthorized access are some of the significant concerns in the use of IoV. To address these challenges, we propose an optimized decentralized trust approach that uses the Ethereum blockchain to provide a secure and effective method for protecting IoV stream data. The proposed approach involves storing IoV data and necessary files in a SQL server connected to the Ethereum blockchain through a smart contract. The smart contract is used to validate authorized users and store the data in the blockchain network. The blockchain's decentralized nature ensures that the data is available to anyone with the hash address, while the smart contract enhances security by controlling who can access the data. The proposed approach is open-source and utilizes smart contract functionality, making it suitable for various applications. The proposed approach's key advantage is that it addresses security and privacy concerns in IoV data management by leveraging blockchain technology. By using the Ethereum blockchain, we can establish a secure and decentralized framework for data sharing and storage. Furthermore, the use of a smart contract ensures the validity and authenticity of transactions and ensures that only authorized users can access the data. The proposed approach can be used for a wide range of IoV applications, including traffic management, collision avoidance, and route planning.
A Machine Learning Approached Model to Identify the Object for Visually Impaired Person

According to the World Health Organization (WHO), 253 million individuals worldwide are visually impaired, including 36 million who are blind and 217 million who have moderate to severe vision impairment. The objective of this study is to demonstrate an improved approach through a real-time working model that is used for the prediction of objects around a visually impaired person. The proposed model is based on object detection which aids in several aspects of object prediction such as accident reduction and managing daily routines while protecting themselves from hazards or obstacles. The proposed model has an accuracy of 72%. More datasets will be embedded in the future to conduct large-scale object prediction with greater accuracy.
A Hierarchical Clustering Random Forest Model Approach to Diagnose the Breast Cancer Diseases

Breast cancer poses a grave danger to women's health, accounting for the biggest number of cancer-related fatalities among females. Accurate identification and successful treatment are essential for reducing the breast cancer mortality rate. Widespread use of machine learning, namely Random Forest (RF), for accurate illness detection. Nevertheless, poor classification performance and substantial similarity across training decision trees may hinder the classification performance of the RF model. In a Hierarchical Clustering Random Forest (HCRF) model, sample trees with low similarity and high precision were selected using hierarchical clustering. Variable Importance Measure (VIM) enhanced breast cancer predictive characteristics using two datasets from the University of California Irvine (UCI) Machine Learning repository, the proposed method was evaluated for accuracy with 97.05 and 97.76 percent in HCRF with VIM feature selection on both datasets. The suggested approach is an efficient diagnostic tool for breast cancer.
Study of Cloud Providers (Azure, Amazon, and Oracle) According To Service Availability and Price

Computing in the cloud refers to a model of providing computing and networking resources on a utility-based, pay-as-you-go basis, without the need for a dedicated server or network. Many aspects of life, from medicine and education to the arts and entertainment, and beyond, are increasingly reliant on technological advancements. Here, Technology corporations play a decisive role. Both large and small IT companies have seen tremendous growth in recent years. Due to Cloud-Computing and its wonderful services, this is now feasible. In the cloud, users have an unrealistic expectation of available resources yet only incur costs in proportion to what they actually use. With the development of new technologies, the quantity of cloud service suppliers has increased rapidly, and each one has expanded its range of services. Cloud users, however, may have trouble determining which service provider is ideal for their specific requirements. We'll be looking at Microsoft Azure, Oracle Cloud, and Amazon Web Services, all of which provide cloud computing services. In order to more accurately assess available cloud computing service providers, readers of this article may benefit from examining the data presented here. Comparisons are made on a wide range of criteria, including the services and tools offered by each provider, the platforms they run on, the languages they support, the level of security and scalability, and the amount of data that can be stored in the cloud.
TOPSIS-based Feature Selection for Text Classifcation

For the classification of huge amount of textual data we need to classify them for the purpose of information retrieval, document classification, spam filtering and so on. To apply machine learning algorithms we need to extract the features from the text data in the numeric format which becomes huge in size. To select the most accurate features from the whole dataset having high relevancy and less redundancy feature selection becomes an important part. This paper proposes a method based on “Multi Criteria Decision Making (MCDM)” for the purpose of feature selection and then we have applied multinomial naive bayes classifier with the integration of some of the similarity measures and validated our method with three different datasets. The results that we get outstands pre existing methods, we get better classification accuracy with our method three different datasets. Our method outperformed some of the baseline methods.
I/O Steganography for Audio and Images in Secure Data Transmission

There are several uses for information concealing today. The use of data hiding knowledge may be morally or immorally acceptable. Data hiding methods, however, are difficult to classify into either the steganography or the watermarking categories because there is no clear distinction between these two concepts, and classification mostly depends on algorithmic application. Hence, regardless of how data concealing is classified, the most popular data hiding applications include copyright protection, covert communication, safe storage, and fingerprinting. Messages are altered by cryptography so they can't be understood. Secured data, varied size keys for data concealment, and quick and flexible ease of design are some of cryptography's main benefits. The biggest drawback of cryptography is that it is not suitable for the internet and is only compatible with devices like mobile phones, has complicated hardware, and uses unusual cypher patterns. Steganography is a centuries-old technique for obscuring data. In this essay, a survey of current audio steganography research is presented.
Fuzzy Logic in Computer Technology: Applications and Advancements

The development of Fuzzy Logic has had a profound impact on the field of computing. A crucial part of any decision-making process is the use of this mathematical method for dealing with uncertainty and imprecision. In this article, it will go over what Fuzzy Logic is, how it may be used in computing, and what recent developments have been achieved in the field. The strengths and weaknesses of using Fuzzy Logic in IT are also discussed. The introduction of Fuzzy Logic into the realm of computer science was a game-changer, since it provided a robust mathematical strategy for addressing uncertainty and imprecision in decision-making. This article introduces readers to Fuzzy Logic by discussing its background, basic ideas, and potential uses in the field of information technology. Artificial intelligence, control systems, decision-making, data analysis, and image processing are just some of the many areas where Fuzzy Logic has been put to use. In addition, this article discusses contemporary developments in Fuzzy Logic, such as those in machine learning, control systems, and robotics. Although this study extols Fuzzy Logic's numerous benefits, it also addresses the method's drawbacks. Workers in the IT industry would do well to familiarize themselves with the merits and drawbacks of Fuzzy Logic, as this knowledge may inform the development of superior products. This study's overarching goal is to introduce readers to Fuzzy Logic in the context of computer technology, including its advantages and disadvantages. Fuzzy logic has been more useful in many areas of computer science and technology. Used in areas such as control systems, image processing, robotics, and decision making, fuzzy logic allows for the processing of imprecise or uncertain input. This study gives a thorough introduction to fuzzy logic in computer technology, including its history, current developments, and potential future uses. The first section of the study provides a foundational understanding of fuzzy logic, its fundamental principles, and their applications. It then presents a comprehensive literature overview on fuzzy logic, covering its origins through the most current developments in the field. Next, the study talk about how fuzzy logic may be used in situations when precise reasoning isn't required, such as in control systems, image processing, robotics, and decision making. This study gives a case study of the development and deployment of a fuzzy logic controller for an industrial robot manipulator to demonstrate the use of fuzzy logic in the context of computer technology. The outcomes demonstrate that the robot can be effectively controlled by the fuzzy logic controller, despite the existence of uncertainties and disturbances.
Optimization Strategy of Storage Layout with Video Information Based on Multi-layer Coding Genetic Algorithm

In this research study, a mathematical model is established for the storage layout optimization problem and the related constraint problems, and the optimization scheme is solved by using the genetic algorithm introduced with the multi-layer coding strategy. In the first step, the review of the state-of-the-art genetic algorithms and the optimizations are reviewed. Then, the novel visual attention guided video image recognition algorithm is proposed to transfer the storage image to the digital scheme. Scale channel attention is used to construct the relationship between channels, assign weights to channels, and highlight more worthy objects. This will assist us to obtain the better image quality. Furthermore, the multi-layer coding genetic algorithm is proposed. The novel coding theory is applied to the traditional algorithms to reduce the time consuming of the algorithm. Lastly, the model is applied to the optimization strategy of the storage layout, the objective model is constructed with the help of the proposed algorithm. Through the testing under different iteration times, the optimal result is achieved.
Decentralized Lottery Using Blockchain

A decentralized lottery system using blockchain technology can offer a transparent and secure way to conduct lotteries without the need for a central authority. The system is built on a smart contract that defines the rules of the lottery, participants can purchase lottery tickets by sending cryptocurrency to the smart contract, and the winning ticket(s) are selected using a provably fair algorithm. The prize money is distributed automatically to the winners' cryptocurrency addresses using the smart contract, and all transactions and lottery results are recorded on the blockchain for transparency and immutability. While the system offers benefits such as trust, transparency, security, and accessibility, there are also potential challenges and considerations to keep in mind such as regulatory compliance, adoption, scalability, and user experience.
An Automatic Scoring System for Wushu Movements based on Real-time Action Image Recognition and Matching Algorithm

Existing engineering practices show that it is a complex task to require automatic detection and matching of corresponding features, usually without any prior knowledge of the scene and image. As the detailed application, this research study considered the scheme of automatic scoring system for Wushu movements based on real-time action image recognition and matching algorithm. Starting from the review of the state-of-the-art study results, the questions for the efficient image recognition is firstly presented. Then, efficient effective moment invariants are designed to serve as the pre-processing of the images and the quadtree method is used for the modelling. The new roles and methods are designed to conduct the task of the efficient image recognition. Furthermore, the SIFT based matching algorithm is designed. Combining these 2 algorithms, the efficient scoring system is implemented and comparison experiment is conducted. After testing, it is reflected that the proposed model has the better recognition accuracy and the scoring performance is good.
Virtual Visit to Pilgrimages Using Augmented Reality

Augmented reality is an immersive experience which combines both real-world and computer-generated content. The AR in the tourism sector is quite popular these days. The use of augmented reality in the tourism industry can improve the travel experience, provide useful information to travelers, and aid in the promotion of tourist sites and destinations. The aim of this study is to develop web application using AR/VR that interacts with the actual environment and aids in increasing tourism. This web-based application can be used to digitally travel to Indian pilgrimage sites for those who are unable to leave their homes but are interested in spiritual journeys. HTML5 and JavaScript are utilized to create non-installing and easy to use web-based application. This web application will connect 360-degree visuals of pilgrimages so that users can see a location from all angles. It will also give site-specific information and directions to the location. This research focuses on promoting India's culture and the grandeur of its monuments.
Hospital 4.0: Blockchain Helping in Hospitality Services

The hospital environment has significantly changed over the past several years as a result of continual technological advances, developments, and discoveries. The current study extensively assesses the use of cutting-edge Blockchain technology to healthcare system. Blockchain helps to reduce the burden of patients as it provides a reliable and trustworthy database of patients. Hospital 4.0 emerges as a result of blockchain integration in the healthcare system and hospital industries. This study recommends the next steps which provides depicting the technical innovation and contribution to attain sustainable development. In conclusion, the study is used to determine the difficulties and best course of action for future advancement in order to build sustainable and smart intelligent hospitals.
Artificial Intelligence Indulgence in Protection of Cybercrime

It is difficult to picture life in the twenty-first century without the internet. Everything and anything can be reached quickly and easily with just one click adding to its disadvantages alongside to all of its benefits. Any illegal behaviour that is carried out online or using a computer is considered a cybercrime. The risk of cybercrime has expanded dramatically along with technology and the internet. Cybercrime can have serious repercussions; include monetary losses, theft of identity, and even dangers to national security. The use of AI is used extensively to prevent cybercrime in order to address this growing menace. Artificial intelligence (AI) based IDS serve to quickly identify and stop cyber-attacks, eliminating the potential harm that it may cause. The study examines the application of AI in preventing cybercrime, as well as the algorithms it employs and its potential applications in the future are all covered in this study. It also focuses at how to use AI to strengthen and improve cybercrime detection and tracking. Finally, the findings of proposed research is discussed and draw the conclusion that AI has a substantial impact on cybercrime detection and prevention.
A Study on Working and Applications of Sequential Deep Learning Models

This research study intends to explore the field of sequence-to-sequence and language models. To determine how this research helped towards the new era of artificial general intelligence. RNN, Bi-RNN, LSTM, Bi-LSTM, Encoder and Decoder, Bert, Attention Models, GPT, GPT-2, GPT -3, GPT -4 are some of the sequence models and transformers that are utilized in performing various natural language processing tasks like text summarization, named entity recognition, chatbots, question answering. The essential factor of sequence models is that the data processing is not more evenly and independently widely dispersed samples; rather, the data have some degree of dependence since they are processed in a sequential manner. Mainly this models deals with the text data as input. The applicability of sequential models in many domains will be discussed.
Assessing BigBirdPegasus and BART Performance in Text Summarization: Identifying Right Methods

In the past few years, automatic text summarization has risen as a key instrument for shrinking voluminous documents. This research study scrutinized the efficacy of the BART Large and BigBirdPegasus language models in generating abstractive summaries for biomedical scientific articles sourced from the PLOS and eLife databases. This study has employed three summarization methods and evaluated the ensuing summaries utilizing ROUGE, BLEU, and perplexity metrics. The findings demonstrated that direct summaries from articles secured the highest ROUGE and BLEU scores, while summaries derived from titles and keywords proved to be more succinct. BigBirdPegasus produced more intelligible summaries, reflected in lower perplexity scores, whereas BART Large offered summaries with superior ROUGE and BLEU scores, closely mirroring human summaries. This study emphasizes the importance of choosing an appropriate summarization technique, taking into account the particular needs and goals of a given application.
Verifcation of Academic Records Using Hyperledger Fabric and IPFS

Document forgery has become a significant concern recently due to the rapid development of modern technology and the simplicity of obtaining inexpensive, cutting-edge workplace supplies. Numerous cases of academic certificate fraud have been documented due to the lack of an effective anti-forgery system. Due to the lack of an effective anti-forgery system, incidents that result in the academic certificate being faked are frequently observed. As a result, methods for educational credential verification and authentication are becoming more and more necessary. Blockchain technology offers a promising solution for creating a secure, transparent, and immutable system for storing and verifying academic records. This paper proposes a permissioned blockchain-based system for verifying academic records using Hyperledger Fabric and InterPlanetary File System(IPFS). Hyperledger Fabric is a distributed ledger platform that supports smart contracts and private transactions among authorized participants. IPFS is a peer-to-peer file system that enables decentralized data storage and retrieval. The system allows academic institutions to issue digital certificates to their graduates and store them on IPFS. The hash of the certificates is then recorded on the Hyperledger Fabric blockchain. When a process is started to check if an academic certificate is real, the credentials are pulled from the off-chain database, and their hashes are generated. This hash is then compared to a hash that is already on the Blockchain. The suggested model completely satisfies all digital document verification system requirements by addressing the shortcomings and challenges in the current document verification methods.
Geriatric Fall Detection and Protection Based on IoT

Falls from altitudes or heights, fall on the same level from slipping or tripping, and falls from wheelchair overturns are common events that can result in severe injuries from striking the ground. An effective defense against all of these occurrences is a wearable airbag system. Motion processing methods are computed by an embedded Digital Motion Processor (DMP) based on the DMP. Using a Digital Motion Processor (DMP), We can successfully distinguish falls and other common motions. The mechanical trigger has a response time of 0.133 seconds, which allows for the release of compressed air without causing someone to fall on the ground. A prototype of the designed system is tested and the usability of the airbag system for real-time fall protection is been demonstrated.