Reviewerly/exHarmony huggingface dataset:
Secure Data Communication Using Padding Key Encryption Cryptography Algorithm

Nowadays, in communications, the main criteria to ensure that the information and communication in the network. The normal two users' communication exchanges confidential data and files via the network. Secure data communication is the most important and crucial problem by message transmission networks. To resolve this problem, cryptography uses mathematical encryption and decryption data on adaptation by converting a data from key into an unreadable format. Cryptography provides a method for performing the transmission of confidential or secure communication. The proposed Padding Key Encryption (PKE) algorithm is used to encrypt the data; it generates the secret key in an unreadable format. The receiver decrypts the data using the private key in a readable format. In the proposed PKE algorithm, the sender sends data into plain text to cipher-text using a secret key to the authorized person; the unauthorized person cannot access the data through the Internet; only an authorized person can view the data the private key. The proposed simulation results provide high security to communicate the receiver for confidential data or files compared with other previous methods.
Person Re-Identification: A Lightweight Feature Extraction Architecture for Image Based Methods

Person re-identification network is widely used in person search method where these networks find correct person match from a gallery of images. Person re-identification has gained more attention among the researchers in recent years because of its use in security and other purposes. Existing methodologies in this area focuses on dealing with various challenges of person re-identification such as occlusion, lighting, camera viewpoint, clothing changes, etc. from different perspectives. In this article, we have used the Siamese model architecture in person re-identification. Different feature extractor models for the twin architecture have been implemented for learning to generate relevant feature vectors. An existing model has been used to improve its performance and proposed a new lightweight model architecture for feature extraction. Additionally, both model's performances have been analyzed by varying different hyper-parameters. Further, pre-trained models have also been used in the architecture and their performances comparison with other models is presented. In the whole process, A subset of publicly available MARS dataset is used while ensuring that it resembles all the qualities of the original dataset. Finally, it is presented that increasing parameters of the model can provide good results although almost similar outcome can be achieved with a decent smaller architecture too.
A Deep Learning Method for Predicting Disease Impact in Indolent Schizophrenia Applied to Psychiatry

According to statistical reports, half of schizophrenics suffer from the disease in its dull form. It is very difficult to define this group of people. You may also see a persistent form of schizophrenia. Therefore, as follows from the definition, dull schizophrenia - this is one of its forms, which does not manifest obvious progress and psychotic manifestations; the clinic appears to be mild personality disorders, derealization, depersonalization and asthenia. In this paper, a deep learning method for predicting disease impact in indolent schizophrenia applied to psychiatry has proposed. As a rule, it coincides with the first year of the disease or several years later. In this case, there are no pathologies; there may even be a decline in negative symptoms, its opposite development. But a new thrust may occur in the middle of the occupation age (45–55 years).
Machine Learning Based Sentiment Analysis and Swarm Intelligence

Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. In this study, we use a variety of machine learning and swarm intelligence optimization techniques to the problem of determining the tone of movie reviews. Profits are increased and product failures are decreased thanks to this study for a wide range of businesses. The effectiveness of these procedures has been measured using MATLAB data from critical assessments of movies. The simulation results demonstrate that the proposed HIRVM scheme outperforms the state-of-the-art sentiment analysis schemes like HKELM, ID3, and J48 with respect to accuracy (96.82 percent), sensitivity (97.1 percent), specificity (91.2 percent), precision (96.2 percent), recall (90.2 percent), and F-Measure (89.5 percent). As compared to conventional methods, the suggested HIRVM significantly reduces both processing time (28.14s) and processing cost.
The Pipeline based Point of Energy Generation and Power Consumption using Fuel Explodes Scheme

generally, the materials used for electrical work can be divided into three maj or categories. They are called Conductors, Insulators and Semi Conductors. Materials that can transmit (carry) electricity from one place to another are called electrical conductors. Materials that do not conduct electricity are also provided as insulators. Materials that have half of these two properties are called semiconductors. That means it has low electrical conductivity. Conductors and insulators are mostly used in the field of electronics. Semiconductors are used in electronics industry. Find out the types and properties of these (conductor, insulator). Wires that carry (conduct) electricity from a point of supply to a load using electricity are called conductors. Objects that work using electricity are called loads. The Electric Fan, Radio, Ionbox, Mixer, Grinder, Bulb etc. Generally all types of metals are used as conductors. Electricity can flow through certain metals very easily..
An Efficient User Protected Encryption Storage Algorithm Used in Encrypted Cloud Data

Cloud data security needs have become an ongoing issue that needs to be addressed urgently. The data encryption cloud is a valuable tool to ensure data security. The existing proxy server system allows many users to perform search operations to find the problem time lag and encryption of data that is not overflowing with cloud storage. Many researchers have been inspired by this work under the single data owner system and the multi-owner data system. As Protected Data Encryption Algorithm (PDEA), this algorithm is used to store data in a database provided by a single cloud service provider and cloud server to conduct data access with no actual data volume. Use simple data recovery and access procedures. Cloud storage, for companies to store data locally during this process. PDEA stores prefer active cloud storage services to lower prices and use a large amount of data to store. Cloud computing is based on rapid development and builds a secure search code from information recovery. Therefore, dual outsourcing of data in the cloud is cheaper, minimizing long- term storage and maintenance complexity. There are data integrity, reliability, security, and minimum guarantees available on the cloud server.
Construction of Clustered Online Teaching System based on Data Center Technology

With the development of educational informatization, online teaching will become a major trend of future educational development, and online teaching platform, as the carrier of online teaching, plays an important role and attracts more and more attention. Based on data center technology, this paper builds a clustered online teaching system, which aims to provide students with real-time interactive learning opportunities. The system will be able to support a large number of users, who can be distributed in different locations and countries. It can easily access all parts of the course content through an Internet connection. It also helps to save time and money and avoid travel costs. It also reduces the cost of printed materials because electronic media can be used instead of paper media. In addition, it allows students from remote areas or places without Internet connection to participate in online courses as long as they like
Application of Association Rule Algorithm in Distributed New SQL Database Design

In recent years, with the increasingly widespread application of digital technology in various enterprises, the role of databases in various enterprises will also become increasingly important. A lot of useful and critical information is often hidden in a large amount of information stored in the database system. How to obtain this information efficiently and accurately has always been a problem, so data mining technology came into being. This paper aims to study the application of association rule algorithm in distributed NewSQL database design. On the basis of analyzing the classical association rule algorithm, the advantages of NewSQL database and the processing process of SQL, the traditional association rule algorithm is improved and a distributed algorithm is designed. NewSQL database, in order to improve the performance of the algorithm for testing, this paper uses multiple computers to form a local area network to simulate the NewSQL database system. The test results show that when the minimum support is low, the execution efficiency of the improved association rule algorithm is slightly better than that of the traditional association rule algorithm
Deep Learning approaches for Automated Detection of Fake Indian Banknotes

This electronic Counterfeit currency is a major concern for governments, banks, and businesses around the world. The detection of counterfeit currency notes is a challenging task that requires sophisticated techniques to analyse various physical and security features on the notes. Traditional methods of detection are time-consuming and prone to human error. This paper proposes the use of deep learning approaches for the automated detection of counterfeit bank notes. The use of Convolutional neural networks (CNNs) to analyse currency note image features and Recurrent Neural Networks (RNNs) to analyse security features such as serial numbers is investigated. An ensemble of CNNs and RNNs is proposed to improve detection accuracy. The proposed method is tested on a dataset of genuine and counterfeit currency notes. This method outperforms traditional methods of detection in terms of accuracy and precision. Our proposed method can be integrated into existing systems to enhance the security of banknotes and protect against counterfeiting.
Investigating Energy Consumption and Performance Trade-off for Interactive Cloud Data Management

Cloud computing services have become important to shared cloud resources, as it serves them economically as the customer base grows rapidly. Such processing power, bandwidth, and storage, as needed, cloud computing services, can be allocated to multiple types of resources simultaneously. In the current technique, when the traffic clog acknowledgment traffic assignment strategy is presented, it is to amplify the Profit of the designation move between its objectives different server farms and CONA (CONgestion Aware) However, in-network congestion in the data center in the clouds, data transfer failures can considerably degrade application performance over time. To overcome the existing method issues, Traffic Concerned Load Balancing Multipath Routing Protocol Algorithm (TCLBMPR) has been proposed to improve overhead routing, congestion delays, and cloud lifespan. The proposed method is Traffic Concerned Load Balancing Multipath Routing Protocol. This method demonstrates the efficiency of the traffic allocation of resources in the cloud environment. This work supports data transfer in multiple paths without traffic jamming in a very efficient manner. It avoids the traffic jamming condition as the congestion controller in the cloud controller module monitors for traffic in the cloud. The proposed procedure is contrasted and the current methods, for example, CONA, Fair Share, and greedy as far as resource allocation, path availability, congestion rate, time complexity. It demonstrates the ability and efficiency of data centers in the cloud to estimate the effects of jamming and integrate these calculations into traffic allocation issues.
Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score.
Bengali-English Neural Machine Translation Using Deep Learning Techniques

Bengali is one of the most widely spoken languages and one of the hardest to translate due to its extensive vocabulary. Earlier, it was fairly difficult to translate from Bengali to English. Using neural machine translation (NMT), it is now possible to translate from Bengali to English quite flawlessly. In order to carry out the task of neural machine translation, four different Seq2Seq models - Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU)—are studied in this work. We combined four distinct datasets and used the resultant dataset in association with the four models. Our study shows that BiLSTM is the most effective model for the Bengali to English NMT task. Here, the resemblance between the generated output and the real one is assessed using two frequently used performance metrics termed BLEU and ROUGE. We have achieved scores of 47.4, 35.8, 32.0 and 22.8 for BLEU-1, 2, 3 and 4 respectively on BiLSTM. Last but not least, our outcomes are among the finest of other studies performed earlier.
Security of an Audio using Multiple Watermarking

This article presents an audio watermarking technique. The proposed technique can ensure the security of audio by using multiple images as watermarks as it is comparatively difficult to remove more than one watermark. Therefore, the originality of the audio signal can be ensured to a significant level. This technique is developed by the modified use of discrete wavelet transform (DWT) and singular value decomposition (SVD). By that modification, the whole energy spectrum of the watermarks is utilized. By doing so, the watermarks are inserted in parts in various regions of the host audio that will make the removal of the mark images difficult and the modified use of SVD, as well as DWT, ensures the creation of those different regions. The robustness of the technique is tested against some real-life scenarios.
Automated Electric Arm with FPGA Processor and Embedded Systems

The field of robotics makes use of a wide variety of electrical and mechanical techniques, in addition to other approaches including computing, intelligence, and the creation of new technologies that are now in use as well as those that are still in the development stage. Industries all over the world are now in a position to produce a wide variety of technologies thanks to the rapid expansion of robotics and automation. Real-time image and video processing presents a number of significant obstacles, one of the most significant of which is the execution of complicated functions and high computing jobs. On the Xilinx Zynq®-7000 System on-Chip (SoC) device, which consists of Dual-core Cortex -A9 processors and which provides the computing ability to perform with the assistance of software libraries created with Vivado® High-Level Synthesis, a hardware acceleration of various filter algorithms for both image and video processing has been implemented. This was done so that the problem could be resolved (HLS). In these kinds of activities, it is necessary for the manipulator to move along a route that has been specified in advance. This presents a significant challenge for the manipulator's motion or trajectory planning as well as its motion control. It is necessary to do extensive research on the arm dynamics and the control of the limited motion of the manipulators before attempting to position the arm. A fundamentally dynamical challenge lies behind the successful management of industrial robots. The accuracy with which parameter deviations or errors are measured is the foundation for determining a control system's level of effectiveness. Through the development of a novel mathematical technique known as the Single Term Haar Wavelet Series method, the current study intends to perform an analysis of the control system of an industrial robot arm and a reduction in the amount of error that takes place while the robot is working continuously.
A New Method for Learning Decision Tree Classifier

Decision Tree (DT) induction is one of the popular data modelling techniques that commonly used in many real-world supervised learning problems. DT is a top-down recursive divide-and-conquer algorithm. It divides the datasets until all the instances of a sub-dataset belongs to the same class value. There are several advantages of DT e.g. it is easy to implement and easy to understand, it takes little prior knowledge, non-linear relationship don't affect the tree performance. DT can be used for both classification and regression tasks. The traditional DT methods use information gain, gain ratio, and gini values to select the root/best node. DT can be scalable to deal with Big Data e.g. RainForest and BOAT (Optimistic Decision Tree Construction) algorithms. In this paper, we have proposed a new method for decision tree construction where instead of using the traditional informative feature selection technique, we have considered each of the input feature to build decision tree and used these trees as an ensemble classifier. Each of the tree predicts the test data and returns the majority voting. We have tested the performance of proposed method with the well-known C4.5 classifier using datasets taken from UC Irvine Machine Learning Repository. The proposed method outperforms C4.5 classifier by almost 30%.
An Estimation Analysis of Secure Encryption Function Using Quantum Entanglement Method in Modern Communication

Information transmitted over the Internet is not guaranteed to be secure. In hacking, information is stolen every nanosecond. Hence frauds, accidents, security threats and economic losses are very high. Information can usually be transferred via wire, fiber or wireless communication. It is customary to send information encrypted so that it cannot be understood by a third person. There is software for coding. A key is used to strengthen encryption, similar to how banks use passwords for email documents. The key is a long alphanumeric combination. Keys are required not only for encryption but also for decryption. We can only use de-identified information. Just like the password is not sent with our email, this key is not sent with the encrypted information. After encrypting the information and sending it in the usual way, the key is sent using highly secure quantum communication technology. Even more security can be ensured by using a technique called ‘Quantum Entanglement’, a particularly high-security quantum technology.
Optimization of Temperature Difference Power Generation Energy System Based on Hybrid Multiple Swarm Evolutionary Algorithm

At present, there is still a gap between the development level of temperature difference power generation technology in China and developed countries, and the development is relatively slow. Especially in terms of implementation, China lacks relevant technology and market support. As an environmentally friendly and energy-saving technology, solar power generation plays a very important role in energy technology and also has potential applications in military, aerospace and other scientific research fields. The purpose of this paper is to study the optimization of temperature difference power generation energy system based on hybrid multiple swarm evolutionary algorithm. A temperature differential power generation energy system using solar heat absorbers is designed. A hybrid multi-group evolutionary genetic algorithm with simulated annealing has been introduced to optimize the location layout of the thermoelectric modules of the temperature differential power generation energy system, and the results show that the optimized temperature uniformity degree is higher and the system efficiency is better.
Dynamic Hand Gesture Recognition using Sequence of Human Joint Relative Angles

Hand gestures have been used as a natural and spontaneous form of nonverbal communication since the beginning of humanity. The interest in this field of study is expanding as a result of recent research endeavors. The method for dynamic hand gesture identification in this paper is based on a 3D skeletal model and uses depth pictures. The series of spatiotemporal changes in the relative angles of several skeletal joints with respect to a reference joint is used to suggest a new gesture representation. Over a predetermined number of frames, a series of significant Joint Relative Angles (JRA) between two skeletal joints is calculated. We identified a collection of 12 dynamic gestures with 98.6% accuracy using machine learning algorithms to analyze this sequential data.