Reviewerly/exHarmony huggingface dataset:
Prediction of Phishing Sites in Network using Naive Bayes compared over Random Forest with improved Accuracy

The main aim of this research article is to improve the accuracy rate in efficient Novel prediction of phishing sites in the network by using Naive Bayes (NB) in comparison with Random Forest (RF) Classifier. Materials & Methods: This research makes use of the publicly accessible Kaggle data set for efficient novel network prediction of phishing sites. The sample size for efficient Innovative prediction of phishing sites in the network with an enhanced accuracy rate was 80 (Group 1 = 40 and Group 2 = 40), and calculations were done using G-power 0.8 with alpha and beta values of 0.05 and 0.2, respectively, and a 95% confidence interval. Naive Bayes (NB) with N=10 samples and Random Forest (RF) with N=10 samples efficiently forecast phishing sites in a network with an enhanced accuracy rate. Results: The Naive Bayes (NB) classifier has 95.58 percent greater accuracy rates than Random Forest (RF), which is 94.675 percent. The statistical significance level of this study is p0.05, or p=0.0481. Conclusion: Naive Bayes (NB) is more accurate than Random Forest (RF) at predicting novel phishing sites in the network.
TAMIL- NLP: Roles and Impact of Machine Learning and Deep Learning with Natural Language Processing for Tamil

Reading information in your mother tongue gives the feeling of enjoying juice of fruit. Researchers are working on regional languages to provide convenient and perfect automated tools to convert the content of knowledge from other languages. There exist many challenges based on the grammar of language. One of the classic regional languages, Tamil which is rich in Morphology, contains more processing challenges. The Natural Language Processing (NLP) technique along with Machine Learning (ML) and Deep Learning (DL) algorithms have been used to overcome those challenges. The accuracy of work is depending on the corpus provided to train the model. Among the reviewed papers using Support Vector Machine (SVM) of ML produced higher accuracy then other ML techniques. As DL techniques for NLP are booming one the researchers are working with different DL algorithms. Most of the NLP with Review Discussion in this paper will direct the researchers doing NLP in Tamil language to move further and to choose the right Machine Learning and Deep Learning algorithm to come out with accurate outcomes.
Fictitious Merchandise Detection Using Blockchain

Nowadays we are seeing that the merchandise (product) we are buying can have (20-25) % chances that it can be fictitious merchandise. And this also affects the sales of the company. This paper discusses an application called "Fictitious Merchandise Detection Using Blockchain" to avoid the situation of Fake Products. Blockchain is a decentralized distributed ledger, any updating cannot be a part of this ledger. Blockchain consists of blocks that have their own hash value produced by an algorithm known as SHA-512 or SHA-256. This is an independent ledger I.e., it does not depend upon third parties. So here we are using a QR code scanner that will scan the QR code provided by the user then, it will match that QR code with the blockchain database. If it is matched, a popup message will be generated "Concrete Merchandise" otherwise "Fictitious Merchandise" will be generated as a popup message.
An Efficient Traffic Regulation Using Real Time Pixel Based Density Identification Techniques

In the present scenario, each and every traffic signal is switched on and off according to the time which is set for it. This causes unnecessary delay in the process of clearing up the traffic jam. This is because the roads that have no vehicles are given a green signal and the sides that have very high traffic are given red. This causes unnecessary congestion in junctions. This can be minimized by converting the traffic signals from time based systems to traffic density based systems. When a traffic signal is density based rather than being time based, the signals are switched on and off according to the amount of traffic present in the roads respectively. This system is used to reduce congestion on roads. Here To control traffic, a camera system is employed. In order to adjust traffic signals and advise alternate routes in the event of a traffic jam, they gather information at their individual locations and work in concert with other cameras in the system. The data obtained from the cameras are processed and the density of each corresponding side is determined and according to the data the traffic lights are switched.
Filin – A Movie Recommendation on Content Basis

Recommendation is important because user don’t want to waste the time in searching for the movie that he like. That’s why recommendation system play important role to get the best movie recommendation. After referring a lot of research paper, we get to know that there are various techniques for recommendation such as content- based filtering, collaborative filtering, hybrid technique etc. In this paper we use content-based filtering that recommend the movie using the past activity and past content that the user watch. The cosine similarity algorithm is used in which the single string converted into vector and then find the similarity between the vectors. You can convert it into hybrid approach by using other technique with content-based filtering.
Accuracy Improvement in the Attendance Marking System using Human Face Recognition with Inception Algorithm and Comparing with Light Gradient Boosting Machine (LGBM) Algorithm

This research is about the human face recognition for attendance marking system using Inception algorithm compared with LGBM algorithm with respective accuracy. For this analysis 38 samples were taken in two groups with 19 samples each. Group 1 uses Inception algorithm and group 2 uses LGBM algorithm. By this research, the dataset is imported and then Inception code is implemented with the help of Google Collab software. The sample size is calculated from the values which are obtained from previous studies with the help of online statistical analysis tools with the pretest power 80% and the alpha value of 0.05. From simulation results, Inception algorithm accuracy is 88% and LGBM algorithm accuracy is 97% with a significance of 0.020 which is less than 0.05 (p<0.05). For the given dataset LGBM algorithm gives better accuracy than Inception algorithm in terms of Prediction for the face recognition.
A Robust Blockchain Assisted Electronic Voting Mechanism with Enhanced Cyber Norms and Precautions

Voting is the primary right to every citizen and that should be loyal enough to select a correct leader to the respective country. Every person has the duty to choose the proper leader for their nation by exercising their fundamental right to vote. When voting at a polling place that uses paper ballots, citizens must be present in order to deposit their vote. The vast majority of people throughout the world has neglected this need and has avoided doing their civic responsibility. In these cases, electronic voting systems are generally viewed as a viable option. The challenge here is protecting voters' privacy and ensuring their votes get to the right people. This study presented Blockchain Assisted Enhanced Cyber Norms (BAECN), a revolutionary electronic voting system that ensures voters' ballots are cast accurately and without delay. One possible answer is Blockchain technology, which uses a distributed ledger in which many people share ownership of the data. The Bitcoin protocol, also referred as the decentralized Banking network, has incorporated Blockchain technology. To perform a transaction or carry out a transaction over the Blockchain network, gas is the charge that must be paid. One kind of voter fraud in electronic voting systems can be mitigated by the use of Blockchain: the redistribution of voter registration data. The proposed methodology, on the other hand, offers a revolutionary online voting mechanism built on Blockchain technology, which fixes all the issues that were found. This research describes the findings of a real-world use of Blockchain technology implementation of a smart-contracts application that increases election security while also lowering associated costs.
Design and Implementation of Solar Powered Smart Irrigation Using IoT

The farming is one concerning the indispensable assets due to the fact of advent of ingredients but assumes sizeable portion about the economic regulation about each and every United States on America via which include after Today, coast owners hold a snack troubles within agribusiness appropriate according to the fact of non-appearance about downpours after shortage about water. So, between that task, we wish raise external computerized wind law case due to the fact bank lords as like intention maintain time, value then after on Manual inclusion is wished due to the fact the farmland strategies. By construction use over soil humidity sensor degrees involving ground dampness/mugginess may additionally keep checked. At some also is an harmony the trade yet sends signal in conformity over the microcontroller or relying upstairs that the cloud provision framework works. Robotized cloud government litigation uses valves in accordance in accordance with Engines work continue to be robotized effectively by using construction use of regulators yet no assistance over guide intervention among accordance about flirt apparatus ON then OFF.
Movie Reviews using Sentiment Analysis with Natural Language API

Sentiment Analysis is the process of analyzing sentiment of a piece of text by identifying polarity or writer’s attitude towards any topic or product such as positive, neutral or negative. This paper uses sentiment analysis on movie reviews in twitter data, which is the most dominant public social media and its data is accessible using their own Twitter API. Words and misspellings make sentiment analysis for twitter data, harder due to the existence of slang words and bad spelling. This work uses Hadoop for the various features it provides over the API. The proposed work uses the knowledge base approach using lexica analyzer for sentiment analysis of movie reviews. The assigned score for movies is 100. The proposed work get all the positive, negative and neutral comments to judge "how good" or "how bad" the movie is.
A Multi-Criteria Intelligence AID Methodology and IoT Based Data Protection Using Machine Learning

IoT-powered devices have become one of the key companions of humans in recent times. Almost every sector of the modern market is heavily reliant on IoT-powered devices such as smartphones, computers and other smart gadgets. This has resulted in the massive availability of digital data containing the personal information of the users. This has caused a massive issue regarding data privacy on IoT-powered devices in recent times. This has resulted in the application of ML-based technologies for fostering better data protection. The concerned study has focused on the role of the different ML-powered technologies such as federated learning and differential learning models. Thai entre study has also focused on numerical analysis of the issues associated with the mentioned models during data protection. It has also provided survey data regarding different FL-based algorithms such as LM, NN, NM, DT and CM. This study has analyzed the issues and strengths of ML-based data protection on local and global data sets.
Efficient Human Action Recognition using Novel Logistic Regression Compared over Linear Regression with Improved Accuracy

The purpose of this study is to perform efficient human action recognition utilising novel logistic regression, as compared to linear regression, with improved accuracy. There are a total of 30 samples, which are split between the two categories. Each received a total of 15 different samples. The Linear Regression Algorithm is responsible for Group 1, whereas the Novel Logistic Regression Algorithm is in charge of Group 2.The accuracy of the model generated by the linear regression algorithm is (87.77%), which is higher than the accuracy generated by the novel logistic regression algorithm, which is (90.42%). The hypothesis is validated by the use of an independent sample T-test. The mean accuracy detection is +2SD, and the Significance Value is 0.970 (p>0.01). Both of these results indicate that the hypothesis is accurate. As a result, the accuracy of the Novel Logistic Regression Algorithm, which was measured at 90.42%, was discovered to be higher than that of the Linear Regression Algorithm, which was measured at 87.77%.
Design, Analysis and Fabrication of Robotic Aero Car

Now-a-days people are moving towards the instant things which saves their time and cost. It also suits in transportation field. Car is used for land transportation. In this paper, aero car technology has been proposed. Aero car is a personal air vehicle with a capacity of two passengers. As vertical take-off and landing (VTOL) is possible in aero car, there is no need of runway. Also aero car can be operated on the road as well as at the lower altitude of Earth’s atmosphere. In this paper, prototype of aero car has been designed, analysed, fabricated and tested successfully. It is a new technology to solve many problems in society during emergency situations such as accidents, natural calamities etc. During emergency situations, aero car acts like an airborne ambulance. Using aero car, travelling above the rivers, hills, heavy traffic area and watershed will be easier. Therefore we strongly recommend that this aero car can play major role in future transportation.
FPGA Implementation of Novel Triple Data Encryption Standard for High Speed Network Security Comparison with DES

Aim: This research presents the implementation of Novel Triple Data Encryption Standard (Novel Triple DES) on Field-Programmable Gate Arrays (FPGAs) for high-speed network security. The proposed implementation is compared with the traditional Data Encryption Standard (DES) to demonstrate the improved security and performance of Novel Triple DES. Materials and Methods: The proposed Novel Triple DES implementation on FPGAs is tested using a Xilinx Virtex-7 device and compared to the traditional DES algorithm using simulation tools and performance metrics, such as encryption/decryption time and time delay analysis. The implementation requires a computer with VHDL synthesis tool and Modelsim software. The encryption and decryption processes are implemented in VHDL, while the key generation is executed using Modelsim. A time delay analysis and power consumption was conducted using 10 sample data sets, with an equal number of samples from two groups, and the results were compared using G-power with a 95% confidence interval. The research was conducted with alpha and beta set at 0.05 and 0.2. Results: In the proposed Novel Triple DES implementation, it was found that the time taken to encrypt a text data was 33.6 nanoseconds and the power consumed was 0.254 Watts. In comparison, the traditional DES algorithm had a time delay of 44.3 nanoseconds for text data encryption and consumed 0.467 Watts of power. The significance value obtained was 0.0249 which is (P< 0.05). Conclusion: These results demonstrate a significant improvement in terms of both processing speed and energy efficiency for the Novel Triple DES implementation compared to the traditional DES algorithm.
An Effective Approach for Newspaper Article Classification using Multi-Class Support Vector Machine in Comparison with Binary Classifier to improve Accuracy

Aim: An efficient approach to classifying newspaper articles using a multi-class Support Vector Machine. Materials and Methods: Accuracy stands as result for the classification of text analysis. Factual texts merely attempt to inform, whereas virtual texts try to amuse or combative readers by inventive language and imagination. The rate of correct classification of novel texts is low and classification occurs in the areas of text analysis and classification of multiple articles. The binary classification and the separation of data points into classes. The multiclass SVM is used for splitting the multiple into severely binary classification. The Novel Text Classification is checked by sample size (N = 42) Support Vector Machine obtained with G-Power taking value equal to 80%. Results: Accuracy is the outcome, Support Vector Machine accuracy rate is 82.71%, which is relatively higher than the Binary Classifier (BC) with 71.48%. Significance value accuracy becomes 0.101 (p>0.05). Conclusion: SVM works and gets more accurate than the Binary Classifier. And this research is evaluated to predict accuracy for a system that is proposed Support Vector Machine is higher than existing comparison utilizing Binary Classifiers.
An Automatic Method to Prevent and Classify Cyber Bullying Incidents Using Machine Learning Approach

Deep learning models for CyberBullying detection on social media, as a relatively new field of research and application, need the identification, investigation, and analysis of a broad range of human-based expressions. Natural language processing encounters a categorization challenge when trying to identify whether a remark, post, message, or picture represents bullying. A thorough investigation of the meaning of words is also required. Previous attempts to identify CyberBullying focused mostly on manual feature extraction approaches. These tactics not only take a lot of time and effort, but also often misread the intended meaning of a communication. This removes the need for any further feature extraction approaches. Deep learning was used to identify CyberBullying in social media data that included both textual and visual aspects. Because of the large volume and diversity of user-generated information on current social media platforms, detecting CyberBullying in real time has become more challenging. The rapid transmission of information makes real-time regulation of online speech problematic. CyberBullying is a widespread issue that may take many forms. To stop CyberBullying, researchers used deep learning models to analyse social media content, modality, and language. This research found that embeddings with deep learning architectures accelerate representation learning and feature selection compared to typical machine learning methods.
Fuzzy Arc Weights - Shortest Path Problem Solution by Cheetah Chase Algorithm

In modern design with state of the art technology, the domain of shortest path problem is one of the mainstream topics in theoretical approach of graph. A variety of applications of the SPP are like google maps, road network, logistics frameworks and design path planning of VLSI and so on. The suggested method uses the Cheetah Chase Algorithm whose foundation is the concept of swarm intelligence used to dynamically allocate nodes between the start and destination nodes in an effort to find a more effective resolution for the SPP that lies in between the start and end positions. A fuzzy-based Cheetah Chase Algorithm with various fuzzy arc weights is presented in this paper for solving shortest path problems. According to the findings, the fuzzy arc weights-based enhanced CCA algorithm achieves higher convergence rates than the other metaheuristic algorithm.
Detecting Cyberbullying Behavior in Cyber Data using Bagging Classifier and comparing its Capability over Support Vector Machine Algorithm

Aim: The suggested research will attempt to perform novel cyberbullying detection by classifying between offensive and non-offensive tweets from Twitter and from its dataset using Bagging Classifier and Support Vector Machine. Materials and Methods: A dataset of abusive and non-offensive tweets is used to develop the Bagging Classifier. Bagging Classifier uses and develops a machine learning method to identify tweets as offensive or not. The sample size was calculated to be 40 per set, and the quality was verified and recorded using Gpower of 80%. Results: The accuracy was maximum in classifications of offensive and non-offensive tweets using Bagging Classifier (94.2%) with a minimum mean error and compared to Support Vector Machine (89.4%). The differences between the classifiers are statistically negligible (p=0.44). Conclusion: In the classification of offensive and non-offensive tweets, the study shows that the Bagging Classifier algorithm outperforms the SVM approach.
Empirical Assessment of Artificial Intelligence Enabled Electronic Voting System Using Face Biometric Verification Strategy

Each and every citizen has a primary concern to vote for the nation, in which it is the basic need to all countries in the globe. But the fact is, every election around 20% votes are not polled from the voter side. This is due to the conditions of rural village problems, lack of time, health issues and so on. So that the need for an electronic voting system is formed, but the major issue in the system is security. All the voters have a question in their mind is the concern of privacy and security issues. This paper is intended to design a novel face based biometric methodology to authenticate to place their valuable vote in the correct manner. The goal of this methodology is to create an electronic voting mechanism that will make elections more reliable and convenient. Voters prefer to submit their ballots in specific locations for voting, such as polling booths and are willing to wait in a long line to do so. Fake votes cast by ineligible voters might generate just as many issues. For that reason, this work proposes an electronic voting mechanism that has the potential to be quite helpful. The government may be able to conserve the budget normally spent on elections thanks to this technique. This proposed scheme introduced a face recognition approach to identify the respective voter and authenticate them to vote further, only if the respective person is eligible to vote with correct identity. The logic of Artificial Intelligence (AI) is applied to check the process, that is the face matching with the trained samples and the resulting section provides the proper proof to the efficiency of the proposed scheme in detail. As a whole, the system is being created to facilitate the work of the Independent Electoral Commission of India and to lessen the burden on people who work there.
Analysis of Handwritten Equation Recognition System for Comparing Decision Tree and support Vector Machine Algorithm

The primary objective of this study is to compare and contrast the performance of the decision tree and support vector machine methods in the context of handwritten equation recognition and to present a fresh approach to this problem. Twenty iterations were performed using a (N=10) for SVM sampling and (N=10) at 80% g-power datasets gathered from a variety of web sources to obtain data in the decision tree technique. For implementation, the additional test will be utilized. Findings indicate that, contrasted with the Decision-making Tree Method (83.8%), the Support Vector Machine approach (91.0%) is more accurate. The statistical significance of the innovative handwritten equation recognition difference of 0.032 (p0.05) indicates that the study's results are statistically significant. The outcomes proved that when it comes to handwritten equation recognition, the support vector machine technique is superior to the decision tree algorithm.