Reviewerly/exHarmony huggingface dataset:
Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques

In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55&#x0025;). Collaborative-based filtering was applied by just 18&#x0025; of the looked into approaches, and hybrid based by 16&#x0025;. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable.
Survey on Detection of Face Mask and Social Distancing using Tensorflow

The global COVID-19 epidemic has had a significant impact on the entire world, infecting more than eight million people worldwide. The two most important safety measures that can be taken by the people in puplic in order to prevent this disease is wearing face masks and following social distancing. In order to create and keep an eye on this type of environment, we put forward an efficient and convenient CV(ComputerVision)-based approach mainly focusing on the automated real-time surveillance system to identify face masks, voilation and maintain social distancing in all public areas by creating a model on a system that monitors all activity and manages violations using a camera that can be a webcam, a phone, or even a networked device. After Detecting a violation, the system is able to send an alert popup/alert box including real-time pictures of the group or an individual breaching the basic COVID-19 Protocols to a privacy group of Telegram that was made for these type of cause. In this paper we have proposed a system which is a mixture of both modern deep learning algorithm and geometric techniques for making a model satisfying robustness condition. The proposed model is totally based on three aspects detection,validation and report. Hence.this model can solve a problem by allowing us to use it and save time and make the most out of it to decrease the spread of the pandemic and by reducing the total COVID-19 cases. It can be successfully implemented in present scenarios where strong public safety measures are required, such as public meetings, shopping malls, movies, grocery stores, and so on. Since the goal is to minimize the time taken and also the cost by decreasing the number of individuals to inspect in public places at a time,we make use of Automated inspection.
Investigating the Role of Block Chain to Secure Identity in IoT for Industrial Automation

Everyone who has an app that can access the block chain can get their hands on the data. There are blocks for each new piece of data that come in. A chronological chain of data is formed when a block is filled with data and chained to the previous block. Smart things are protected from cyber-attacks thanks to the IoT's technology's transparency and virtual incorruptibility. The block chain may be used to enable secure connectivity between IoT devices by storing data in transactions and confirming those transactions with nodes. The Internet of Things (IOT) has helped industrial automation reach new heights. We can improve the security and privacy by adding block chain to it.
Risk Analysis of Home User's Vulnerability to Illegal Video Streaming Platform

Illegal streaming platforms offering free movies and TV shows may often contain malware disguised as pirated video files which could infect any device. This is an avenue for the hackers to steal user's photos, passwords, or other personal information stored on the infected device. People who cannot afford legitimate copies of movies often resort to illegal movie sites, which can infect their devices with malware. Therefore, the researchers aim to explore the environment of these illegal video streaming sites, raise awareness regarding streaming videos on illegal or unlicensed sites, and protect home users from malware against hackers. The researchers conducted a survey to 10 home users using Google Forms. Results showed that home users often use illegal streaming platforms and most of them are aware that illegal streaming sites may contain malware. However, they will continue to use it despite the potential damage. Those who can afford to subscribe to legal streaming services should download and subscribe to them to protect them from malwares. This can also have a positive impact on the movie industry. Furthermore, for more security, users should download malware protection that is suitable for their devices.
Breast Cancer Classification Using Machine Learning

Breast cancer is especially dangerous to women, with a high rate of morbidity and mortality. Therefore, the need for algorithm that detects the early sign of breast cancer is essential. The four classification algorithms logistic regression, decision tress, random forest and CNN predicts the breast cancer and then outcomes were compared. Machine learning plays important role in prediction of early sign of breast cancer. In this project we will be using three different classification machine learning algorithms. Then we will compare the accuracy and performance of all these algorithms. Data with imbalanced classes are a big problem in classification algorithms and hence requires proper preprocessing and handling. We will use dataset of breast cancer patients and then train the different machine learning models. In the end all the algorithms are compared based of performance and accuracy to find out which algorithm is the best for this problem. This study will show the accuracy of several models for this breast cancer categorization so that the appropriate approach may be employed. The purpose of this study is to forecast the accuracy of several breast cancer categorization algorithms.
Feature Selection with Stochastic Hill-Climbing Algorithm in Cross Project Defect Prediction

Existing research has shown that carefully chosen training data can improve cross-project fault prediction performance (CPDP). In CPDP, however, there are factors to consider such as feature selection and data quality. Feature selection is a process of eliminating noisy features from a dataset when developing a predictive model. It increases the performance of the model and reduces the computational cost. In this study, the authors aim at utilizing the stochastic optimization algorithm to select the best feature subset. The stochastic variant of the hill-climbing algorithm is used to search the solution and a decision tree classifier is used to evaluate the solution after each iteration. The dataset is reduced according to the best feature subset and then CPDP performance is evaluated by the Naive Bayes classifier. In most of the prediction results, the proposed method shown the best results and has improved the existing CFS method by 1.5 &#x0025; in terms of accuracy.
A new enhancement of the k-NN algorithm by Using an optimization technique

Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens. Therefore, KNN can be used effectively as it does not require a residual classifier to be constructed in advance. KNN offers ease of use and can be applied across a broad variation spectrum. Here, a novel KNN classification approach is put forward using the Bayesian Optimization Algorithm (BOA) for optimisation. This paper seeks to make classification more accurate and suggest alterations of nearest neighbour K value to use information about dataset structure and the similarity measure of distance. The findings of experimental work based on the University of California Irvine (UCI) repository datasets in general shows improved performance of classifiers compared with conventional KNN and give greater reliability without a significant time cost to speed.
Design of a Blockcdhain based Security Algorithm for IoT in Healthcare

Electronic health records (EHRs) are becoming increasingly popular in healthcare organisations as a way to save costs and improve efficiency by replacing paper-based records. There are numerous advantages for patients, healthcare organisations, and healthcare professionals to having online access to patient records and transactions linked to diagnosis. However, it also poses severe privacy concerns over the patient&#x0027;s private data. Medical records can now be stored, accessed, and updated at any time and from any location thanks to the internet and its impact on the healthcare industry. Electronic Health Records (EHR) discrepancies and concerns about privacy and security should be addressed. The lack of consistency in EHR may have been the most serious problem, but it isn&#x0027;t the only one. The interoperability and privacy difficulties have already been addressed through the usage of decentralised online ledgers with blockchain-based systems. A Secure eHealth Framework based on a progressive temporal blockchain method is presented to overcome the aforementioned issues. As a result, the context-based smart contract helps to record medical data in a text format with temporal features and disseminate the data among all other peers as specified in the smart contract regulations Furthermore, the framework has the ability to address data concerns and deliver tamper-proof, secure transactions in the healthcare industry. RBAC and various system modules were put through their paces to see how well they worked with the Delegated Byzantine Fault Tolerance consensus algorithm (dBFT). The results suggest that the system is able to safely and effectively handle and disseminate EHRs.
Sentiment Analysis using Supervised Machine Learning

The dataset is the IMDB dataset which comprise of the reviews of 50k movies and it is available on the Kaggle, which has 2 columns in it. The two columns in the dataset contains the reviews and the sentiment which will help us to identify the reviews is a positive review, or a negative review about the movie. Machine Learning is a modern-day concept in which the machine can be trained in the same manner as an infant baby learns the things in an environment, with the use of machine learning certain patterns are used from the data to give the machine a learning, reasoning and predicting ability. The aim is to train and build a machine learning model that will be able to predict whether a movie review provided is a positive or negative review, this is also known as binary text Classification which will be done by using Scikit-learn library, which is a very common machine learning library used in python programming language.
An Review on Internet of Things (IOT) in Creating Better World Through Reduction in Emission of Greenhouse Gases – Multiple Regression Analysis

The development in digital technologies possess immense opportunity in addressing various social and related aspects. It has been noted that the energy consumption is expected to increase manifolds in the next two decades and there is a growing concern to meet the energy needs in a sustainable manner. The IoT enable in supporting the energy companies, government, business and individuals in enhancing the energy consumption, optimally use the resources in an effective manner and thereby reduce the emission of greenhouse gases. Hence the increasing complexities of the traditional power grids does not able to support the growing energy needs and though application of IoT power grids can support in enhancing the communication, achieve greater efficiency in power consumption and possess great integration between different sources of power generation. The use of smart networks enables better demand management, bidirectional interaction, and thus meets users&#x0027; needs and requirements. Implementing IoT can also help manage the integration of electricity consumption in a sustainable way. IoT-based smart grids can help reduce stress during rush hour. greenhouse gases
An Empirical Analysis of Python Programming for Advance Computing

Python is an object-oriented, scripting, and interpretive programming language that may be used for mentoring and real-world applications. This paper focusses primarily on Python software packages used in data science, pattern recognition, and IoT. This paper will first explain Python as a language, then introduce Data Science, Machine learning, and IOT, describing prominent packages in the Data Science and Machine learning community, such as NumPy, SciPy, TensorFlow, Keras, Matplotlib. This paper will also demonstrate the significance of Python in the development of the industry. Throughout, we shall utilize many code samples. We review so many research papers to analyze the usage of python in different fields and easily import packages in the programming software.
A Framework of Internet of Things (IOT) for the Manufacturing and Image Classifaication System

Real-time identification of industrial processes and real-time image classification are necessary for recent days to reduce the excess power consumption and detect water pollution respectively. Usually, robotic technology in manufacturing industries is a costly initiative; thus, researchers are searching for a low-cost and effective loT (Internet of Things) technology that will detect and measure real time-condition of industrial machines. Moreover, the loT technology is useful in image classification as well for detecting water pollution. This research has explained the benefits and cost-effectiveness of available loT along with a comparison between image classification techniques. A primary quantitative survey method has been carried out to collect practical information based on the judgement of 60 respondents. Thereafter, the ‘Purposive’ sampling technique has been applied to discuss the primary data and uphold a significant judgement. Findings and analysis suggested that WSN (Wireless Sensor Network) is a cheaper technology that can be integrated into small- and large-scale manufacturing industries. Image classification loT has been identified for detecting water pollution where texture analysis is cheaper than spatiotemporal analysis.
The Role of Implementing Cloud Computing Technology for Addressing Critical Security Issues and Overcoming the Challenges Effectively

The term cloud computing possesses the critical aspect to enhance the network by leveraging the available resources in an effective manner. It has been widely stated that the usage of enhanced IT infrastructure support in realising the goals of the stakeholders in an easier aspect. Cloud computing is a shared pool of operations that is growing in popularity due to its low cost, high efficiency, and high output. Along with its many advantages, cloud computing presents a considerably more difficult scenario in terms of data privacy, intellectual property rights, authenticated access, data security, and so on. Cloud computing technology is becoming ever more challenging in today&#x0027;s society as a result of these challenges. This paper aims to evaluate the security issues in cloud services and implementation of advanced technology to prevent these challenges. In this context, mixed method has been considered (primary quantitative and secondary qualitative) to gather relevant and factual information.
The Model of Smart Sensing Device For Sensitive Nanoclusters Modification in Sensing Properties

Many smart gadgets with novel features have recently been developed using nanotechnologies. Because of their unique features, nanocomposite oxides have been explored as atmospheric gas sensors for decades. Nanosensors are extremely important in the realm of environmental environmental testing. They provide significant advantages over traditional techniques and address the requirement for portable analytical instruments with qualities like as selectivity and stability, along with other things. The development of dependable nanosensors with key realistic needs and practical utility in the domain of environmental pollution screening and monitoring is anticipated. The structural features of the sensing materials employed in the manufacture of nanosensors are crucial in determining the final device's performance. Modern-day nanosensors have grown in sophistication and sensitivity as a result of several recent advances in the field of material science. They provide performance that is frequently comparable to traditional procedures.
A Conceptual Analysis on the Impact of Machine Learning Towards on Digital Marketing Transformation

Digital marketing is a means of online marketing and advertising a product, business or service that operates through digital technologies, electronic devices and online channels. The constant demands and increasing market competitions are effectively throwing great challenges to the business to maintain its existence. In such circumstances, implementation of the technology is the only option to prove the business superior to others. Digital marketing brings a lot of changes in the current era where the different organizations could able to diversify their business by selling products beyond the local area or pacified stores. Now, the development of digital technology mostly depends upon platform based advanced coding and machine learning algorithms. In this context, this research paper has investigated the impact of machine learning on digital marketing transformation by using the conceptual analysis process. This assignment has going to discuss the various concept and impacts of implementing digital business transformations through adopting the innovative facility as machine learning within organizations. However, the operational leaders of the business continuously put their best efforts through making survey responses regarding the influence of digitalization business.
Voice Impersonation for Thai Speech Using CycleGAN over Prosody

Voice impersonation can be a challenging task for mimicking all aspect of the target speaker. This paper proposes a prosody conversion using a cycle-consistent adversarial network (CycleGAN) on non-parallel training data in voice conversion (VC). A CycleGAN model had demonstrated an outstanding performance in style transfer of the unpaired images. In this method, the converted voice was generated from the transformation of both prosodic and spectral features of the target voice. In this way it can better represent the persona of the target speaker. Experimental results from standard evaluation procedures for evaluating synthesized voice demonstrated that our prosodic CycleGAN significantly outperformed the conventional non-parallel VC method.
Research on Natural Language Processing Problems Based on LSTM Algorithm

In the process of the development of modern society, the network text analysis system is more and more important, and the research in the field of natural language processing has been comprehensively developed. In this research process, the natural language processing system mainly uses deep learning technology to realize deep learning technology, mainly through different neural networks to complete model construction, based on language analysis, using word segmentation and sentence classification methods. The long sentences are processed to ensure that the computer can better understand the text semantics of the system. Based on the above background, this paper designs a natural language analysis system to judge the word order and semantics of language characters conduct deep mining of language features based on neural networks. And simultaneously classify data mining results.
Research on Multi-task Cruise Path Planning of UAV Based on COA Optimization Algorithm

Aiming at the multi-task cruising problem of UAV in three-dimensional uncertain weather environment, a path planning method based on coyote optimization algorithm is proposed in this paper. First of all, through the analysis of the dynamic changes of clouds, the destination risk assessment model is constructed as the safety constraint for UAV to fly in uncertain weather environment, and then the problem of UAV multi-task cruise is analyzed. a method of UAV multi-task cruise path planning is proposed to maximize the benefits of UAV cruise decision-making. Finally, the coyote optimization algorithm is used to make the optimal decision in line with the flight constraints, and the corresponding path coordinate sequence is obtained by decoding. The simulation results show that this method can find the path that satisfies the constraints and minimizes the penalty cost, so that the UAV can ensure the flight safety at the same time.
Multi-feature Microblog Sentiment Analysis based on BERT-AttBiGRU model

In recent years, there has been an increasing amount of research on Weibo sentiment analysis techniques, but less attention has been paid to emoji. However, emoji are closely related to Weibo sentiment. So to judge the microblog sentiment tendency more accurately, in this paper, we select Weibo comments that contain a large number of emoji and propose a neural network classification model that combines emoji. The model first obtains word vectors containing contextual semantic information through the BERT pre-training model, then extracts deep-level feature information by using bi-directional gated recurrent network (BiGRU), and then puts the emoji vector and text vector into the Attention mechanism, and assigns weights to the extracted feature information to highlight the important information. Finally, the Softmax function is used to classify the microblog sentiment.The experimental results prove that the accuracy of the model reaches 97.65%, which effectively improves the accuracy of microblog sentiment classification.
An Intelligent Detection Method for Civil Aircraft Skin Damage Based on YOLOv3

research-article An Intelligent Detection Method for Civil Aircraft Skin Damage Based on YOLOv3 Authors: Mengmeng Zhou Nanjing University of Aeronautics and Astronautics, China Nanjing University of Aeronautics and Astronautics, ChinaView Profile , Hongfu Zuo Nanjing University of Aeronautics and Astronautics, China Nanjing University of Aeronautics and Astronautics, ChinaView Profile , Juan Xu Nanjing University of Aeronautics and Astronautics, China Nanjing University of Aeronautics and Astronautics, ChinaView Profile , Changlin Qian Nanjing University of Aeronautics and Astronautics, China Nanjing University of Aeronautics and Astronautics, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 586–590https://doi.org/10.1145/3544109.3544317Published:18 July 2022Publication History 1citation28DownloadsMetricsTotal Citations1Total Downloads28Last 12 Months28Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access