Reviewerly/exHarmony huggingface dataset:
Isolation Forest based Fraud Detection for Cryptocurrency Transaction

The growing popularity of cryptocurrencies has brought attention to the need for effective fraud detection mechanisms within this dynamic and rapidly evolving domain. This research explores the application of the Isolation Forest algorithm for detecting fraudulent transactions within cryptocurrency networks. The Isolation Forest algorithm is particularly well-suited for identifying anomalies in datasets characterized by imbalanced classes or rare instances, making it an ideal candidate for detecting infrequent fraudulent activities in cryptocurrency transactions. The proposed methodology involves preparing a dataset of cryptocurrency transactions, including features such as transaction amounts, timestamps, and wallet addresses. By labelling the data with known legitimate and fraudulent transactions, the Isolation Forest model is trained to recognize patterns that differentiate between the two classes. During training, the algorithm constructs a forest of isolation trees, and each transaction’s anomaly score is determined based on its average path length through these trees. A critical step involves setting a suitable threshold above which a transaction is deemed anomalous. This threshold is determined through rigorous experimentation and validation to strike a balance between minimizing false positives and capturing actual fraud cases. The trained Isolation Forest model is deployed in a real-time system, capable of processing incoming cryptocurrency transactions and promptly flagging potential instances of fraud. Performance evaluation of the model is conducted using relevant metrics such as precision, recall, and F1-score, on a separate validation or test dataset from which it is proved that the proposed methodology attains 6% performance improvement than the existing methodology.
The Application of data Mining Technology in Student Management

In order to understand the application of data mining technology in student management, the author proposes an application research based on data mining technology in student management. The author first introduces big data technology into university management work, opens up information channels between multiple business systems, and establishes a university data lifecycle management model. Secondly, the KNN classification algorithm in data mining is used to mine a large amount of data from students’ online activities. Finally, the relationship between traffic volume, duration, and student grades is found, and abnormal data student behavior is intervened, which is applied to student behavior management. The credit passing status of students was compared based on traffic trends. The experimental prediction results show that the accuracy, precision, and recall rates of abnormal prediction are 74.25%, 77.65%, 81.23%, and the F-Score value is 77.56%, respectively. The use of data mining technology has become an important way to solve this problem.
Digital Management System of Archival Information Based on Intelligent Algorithms

With the rapid development of computer technology, communication technology and Internet technology, the traditional archives management mode has also encountered severe challenges. Archival informatization is a modern process of collecting, organizing, storing, developing and utilizing documents based on networks, computers and information technology. With archival resources as the goal and archival work as the basis, following the latest archival management theories, archival informatization has become an important part of modern archival management with the development of archival business. The aim of this paper is to study a digital management system for archival information based on intelligent algorithms. A TextRank-based method for the automatic extraction of key information from digitised archives is proposed to address the need for the extraction of key information from worded archives, and the algorithm is integrated into the archival information digitisation management system, and the modules of the system are experimentally tested, with the experimental results showing the superiority and technical feasibility of the system.
Phishing Website Detection Using Machine Learning Methods

As the internet promotes the growth of ecommerce businesses, the security of financial and personal data becomes a concern. Phishing attacks are becoming more complex and difficult to detect. Several Machine Learning (ML) algorithms that gather data from multiple sources, such as website addresses, search engines, and other internet resources, might be useful in distinguishing a legitimate website from a phishing website. This paper investigates supervised ML techniques such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gradient Boosting (GB), and AdaBoost that are used to detect phishing websites. After training the ML models, the top-performing ML model is deployed using Streamlit so that it can be accessed to check the legitimacy of the website before it is accessed.
Application of ID3 Decision Tree Classification Algorithm in Mathematical data Analysis

ID3 decision tree is the most extensive method. It can be used to classify, identify and predict data information, and the whole system can be divided into several stages according to its nature. It plays an important role in mathematical data analysis. Therefore, based on ID3 decision classification algorithm, this paper studies mathematical data analysis. This paper firstly introduces the decision tree classification algorithm, and paper expounds the ID3 decision tree classification algorithm in data analysis, the application of mathematics algorithm in the application of mathematical data analysis process is introduced, and the performance of ID3 decision tree classification algorithm was tested. Finally, the test results show that for a high-resolution decision tree, if the traditional method is used for classification, it will increase the amount of calculation and time. When the ID3 decision number algorithm is used to improve the model, it can effectively improve the decision accuracy. The mathematical data analysis time based on the ID3 decision number algorithm increases with the increase of the number of data. However, the overall time is within 1-4 seconds, and at the same time, the classification accuracy is more than 93%, which shows that the performance of the algorithm is good.
A Secure Authentication Model with Optimized Key Generation in IoT-Fog-Cloud Computing

With a rise in cloud computing, numerous organizations and businesses units utilized the cloud servers to expand their operations, which leads to lower equipment cost and effective resources utilization. The fog system is operated among the cloud-based IoT, that offered a hierarchy of computing resources for collecting, aggregating, and processing the data from IoT devices. Moreover, the private key is generated to encrypt the data for secure authentication of IoT-Fog-Cloud computing. In this work, hybrid optimization enabled optimal key is generated for data protection. The entities considered for secure authentication-based data protection includes user, fog devices and cloud server. Moreover, the steps like initialization, registration, key generation, authentication and verification as well as data protection is carried out. Furthermore, the secure authentication is done using the security operations like EXOR, hashing, encryption and decryption. In private key generation the proposed Chronological Jelly Fish optimization is utilized.
Information Platform of Business English Teaching from the Perspective of Big data

Our educational and teaching concepts, concepts and methods are constantly updated and developed due to the continuous maturity and improvement of modern educational technology. While educational information plays a supporting role in education, it also emphasizes its leading role, stressing the use of educational information to solve the problems that restrict the development of education in our country. The purpose of this paper is to create a dynamic information platform for business English teaching. Technically with B/S structure and object-oriented technology, the platform is designed based on Struts MVC framework. Through the comparison between the experimental group and the control group, the effect of the business English teaching information platform from the perspective of big data is analyzed. The experimental results show that there is a 95% certainty that there is a significant difference in the performance of the students in the experimental class before and after the experiment, which shows that the business English teaching information platform can help improve the students’ academic performance.
Decentralized Social Media Application Based On Blockchain And NFT Technology

Concerns regarding free expression, censorship, and user privacy have arisen as a result of the rapid expansion of centralized social media platforms. This research paper describes a decentralized social media platform designed to address these challenges by giving users more ownership and power. Our platform supports a democratic and censorship-resistant environment by using the power of blockchain technology and smart contracts, giving users more control over their personal data and user-generated content. Furthermore, we present a novel monetization strategy in which individuals can earn money by selling their content to their followers in the form of non-fungible tokens (NFTs). Our methodology makes use of cutting-edge Web 3.0 technologies such as the Solidity programming language, the React JS framework, and the IPFS (Interplanetary File System) decentralized storage system. According to the conclusions of this study, our decentralized social media network has considerable advantages over traditional centralized platforms in terms of user empowerment, privacy, and content monetization. This article adds to the expanding body of research on decentralized applications and prepares the way for a new era of social media that promotes user sovereignty and fair rewards for content providers.
Ear Based Age Recognition System Using ANN

Biometrics involve the study of specific aspects of an individual’s anatomy or physiology, such as the face, fingerprint, ear, palm print, iris, etc. Among these, ear recognition systems are notably powerful tools, particularly in forensic applications. In conventional biometric facial images are employed for age classification of individuals. One drawback of the existing system, which relies on facial images for age recognition, is that it can be less accurate when there are variations in lighting and facial expressions. Additionally, the facial recognition approach may not be as robust in scenarios where individuals may not have readily available or visible facial images, such as in forensic or security applications. In our proposed Age Recognition system, we utilize ear images and an Artificial Neural Network (ANN) to precisely determine an individual’s age. This work presents a comprehensive analysis of the extraction of soft biometric characteristic, such as age from an individual. This study explores the utilization of ear biometrics and artificial neural networks (ANN) for accurate age estimation. A unique dataset encompassing A variety of age groups, spanning from 10 to over 50 years, forms the basis of our research. Gabor features are employed in a Feed Forward Neural Network (FFNN) to facilitate age estimation. The incorporation of seven Gabor features in the input layer aids in capturing vital ear details, enhancing age estimation accuracy. Seven hidden layers within the FFNN enable the network to discern intricate patterns in the data. The output layer classifies individuals into five distinct age groups, simplifying age estimation. Our paper highlights the potential of ear biometrics in age estimation, with practical implications for security, personalization, and beyond. This work is developed with own dataset, which is divided into five age groups. System has achieved 92.05% accuracy for age recognition for 302 samples. It also achieves an accuracy of 92% for verification and 90% for validation.
Machine Learning Based Ensemble Classifier using Wisconsin Dataset For Breast Cancer Prediction

Subclass of Artificial Intelligence, machine learning integrates several optimizations, probabilistic, and statistical methodologies to help computers learn from prior occurrences and find it challenging to spot patterns in intricate datasets, noised and large datasets. As a result, machine learning in cancer diagnosis and detection has increased. In women, breast cancer is the utmost common malignancy. The study aims to predict breast cancer by comparing widely used machine learning algorithms and methodologies, neural networks, Random Forest, and Boosting, upon Wisconsin Diagnosis Breast Cancer data set to inspect the performance of critical characteristics such as precision and accuracy. This study’s accurate, competitive results can be used for detection and treatment.
Face and Action Detection for Crime Prevention: Balancing Promise and Ethical Considerations

This research paper explores the pivotal role of face detection and action detection technologies in the realm of crime prevention. The application of action detection technology is particularly significant as it serves as an invaluable investigative tool for the detection and prevention of criminal activities, mitigating imminent threats to health and safety, bolstering public protection, and enhancing overall security measures. The overarching objective of this project is to leverage these technologies for crime detection and to augment contemporary security frameworks through technological advancements. Face and action detection technology represent promising innovations in the field of crime prevention, gaining widespread recognition in recent times. This paper underscores the substantial potential of these technologies in curbing violent crime. Nevertheless, it underscores the need for a comprehensive examination of the ethical implications pertaining to discrimination and privacy, emphasizing the importance of striking a delicate balance between security and personal liberties. When implemented thoughtfully and ethically, these systems have the potential to serve as instrumental tools in safeguarding public safety and reducing crime rates, offering a promising avenue for future exploration and implementation.
Application of Artificial Intelligence and Multimedia Technology in Distance Education in the Context of Internet

The application of artificial intelligence technology has become a new high-tech means under the modern information technology and computer network era, it can effectively improve the teaching efficiency and quality. It can also cultivate students' comprehensive qualities such as independent innovation ability, practical operation and information processing. At present, China is vigorously developing intelligent education mode, and many scholars have researched the development of artificial intelligence technology, but because China is still in the initial stage of computer multimedia distance learning, its theoretical basis is relatively poor compared with foreign countries, so it needs further improvement and enhancement. This paper mainly analyzes the application of artificial intelligence and multimedia technology in distance education, and analyzes the development trend of artificial intelligence technology in distance education, which provides reference for the future intelligent distance education mode in China.
Optimized Design of Foundation Pit Engineering Above Underpass Tunnel Based on Neural Network Algorithm

Foundation pit project is a very complex process, involving many aspects, including geological conditions, the surrounding environment and various construction equipment and other factors, there are many uncertainties in the construction process, and therefore, it is necessary to optimize the analysis of these various elements affecting the foundation pit project. In the actual work, there are often various problems. In the actual construction process, due to various factors, the support system often causes large deviation or displacement, so in order to effectively control and reduce the excavation risk of the foundation pit must be optimized analysis and related design work; at the same time, we must also take into account the impact of the change in the stress-strain state of the enclosure structure, so that it can ensure the safety of the premise to minimize the cost of the project. As a result, the obtained values of the scheme category’s design variable of optimal scheme are number of longitudinal blocks of foundation pit excavation/PCS is 6, anti-float plate/m of thickness is 0.7, uplift pile/m diameter is 0.6, respectively.
An English Online Learning System Based on Artificial Intelligence

In order to understand the functions of English online learning systems, the author proposes research on an artificial intelligence based English online learning system. The system is built using the CIS architecture, with software design as the main focus, and is compatible with most models of PC hosts. Design the audio encoding method for English speech testing. Design a question bank based on the difficulty coefficient and use JDBC database connection technology to construct a database access connection method. Secondly, using particle swarm optimization algorithm, an automatic generation algorithm for test papers is established in the system, and an online learning interaction module is added to complete the system design. Finally, the functionality and performance of the system were tested using the English test question bank of a certain university as the database. The experimental results show that when the concurrent users are between 150 and 460, the system’s operation response time remains between 30 and 55 ms, and the application CPU utilization rate remains between 3.2% and 5.9%, with relatively slow growth; When the number of users exceeds 560, the system response time increases to 70-100 ms, and the application CPU usage rate increases to 7.4%-10%, which is relatively significant but still within the normal range.
Parallel Corpora Construction Based on Computer Technology

The paper processes and analyzes the parallel corpus texts of Chinese versions of the English novel Pride and Prejudice, by constructing two parallel corpora of its Chinese versions and the original text as a reference corpus. The tools used to process the data include ctbparser_0.11, PatCount, EditPad Pro, AntConc, SPSS 19, and the like. Two findings are shown as follows: first, either Chinese version of Wang or Zhang is significantly different from the original text, in that the Chinese version is filled with more adverbs, cardinal numerals, common nouns, punctuation marks, and fragment sentences; while the original text is full of more adjectives, conjunctions, pronouns, copulative verbs which are used in progressive tense and passive voice. Second, compared with Zhang’s version, Wang’s is nearer to Austen’s text, especially in the frequency of uses of adjectives, pronouns, copula verbs, and passive marker suo, i.e. by, that is, Wang’s version is more foreignized than Zhang’s. Wang’s version is more foreignized than Zhang’s. Nevertheless, either version has its merits; it can be said in such a way that Wang’s version is slightly quaint; while Zhang’s version is very vivid.
Strengthening Cloud Data Security using Web- Based File Encryption and Decryption with AESCBC- 256

Ensuring robust data security in cloud computing remains a pressing concern, as the proliferation of cloud services introduces vulnerabilities and risks. This research paper focuses on the implementation of web-based encryption and decryption mechanism for files stored in cloud computing environments, employing the AES-CBC-256 encryption algorithm to rectify the backlogs in cloud security. The encryption algorithm that is extensively employed in the realm of computer security and cryptography is AES-CBC-256, which stands for Advanced Encryption Standard with Cipher Block Chaining mode and a key size of 256 bits. Its primary purpose is to provide secure and confidential communication and data protection. When comparing to other encryption algorithms AES-CBC-256 provides a high level of security due to its large 256-bit key size. The key length makes it highly resistant to brute-force attacks. This structure allows for parallel processing and is suitable for various data formats. The proposed work aims to utilize the AES-CBC-256 algorithm for file encryption and decryption, providing a strong level of security with a 256-bit key. This approach adheres to industry standards and offers efficient computational performance, making it a trustworthy option for protecting confidential information.
Self-attention based ResNet model for Cervical Cancer Detection

One of the life taking disease in the trait of cancer is Cervical cancer. It is very sensitive case not only to females but also to males. Therefore, due to lack of comfort to talk about in society and unavailability of medial equipments for treatment bring in the need for computation in action to help the society with correct prediction of cervical cancer cases based on the cervix images and detecting the major area of focus. This study advances a unique method for detecting cervical cancer using images from the ICAR-WHO dataset overcoming a number of difficulties, such as the lack of labeling and dataset availability. To address these challenges, the proposed methodology employs convolution neural networks along with clustering technique to automatically detect and classify cervical cancer images using pseudo labeling. The work relies on standard ResNet50 model and encourages self-learning of the features using attention mechanism for better detection and classification of cervical cancer images tested using different methods. Overall, the proposed method demonstrates promising results for cervix cancer detection and holds potential for improved diagnosis and treatment planning.
Next-Gen Image Shielding: Deep Learning’s Stealthy Image Encryption for Health-Tech IoT

The healthcare sector has undergone a transformation with the advancement of the Internet of Things, facilitating the seamless incorporation of medical imaging devices within the industry's data systems, leading to faster diagnostics and treatments. Maintaining the privacy of patients is crucial and complex, especially when medical images can reveal intimate and private details about a patient's medical history. We explore the usage of advanced Deep Learning methods for encrypting medical diagnostic images, leveraging a cryptographic-based image encryption and decryption framework. The Proposed approach uses a simple key learning model built on ResNet-50 architecture to convert one visual format into another. This model specifically incorporates a domain known as "hidden factors" during the encryption phase. To revert the encrypted image to its original form, we employ reconstructive networks. By extracting relevant data from the encrypted content and enabling direct data mining from the personal data ecosystem, we offer a system that ensures a good return on investment. Additionally, the suggested method incorporates the Enhanced Encryption technique using a stream cipher. We validated the new approach using two publicly available datasets. Preliminary evaluations suggest that the method provides superior security and impressive operational efficiency.