Reviewerly/exHarmony huggingface dataset:
Comprehending SMOTE Adaptations to Alleviate Imbalance in Intrusion Detection Systems

Intrusion detection System (IDS) are an essential component in the world of internet as they identify the malicious activities on the network and alert the network administrators. However, the effectiveness of an IDS is critically hampered due to the inherent imbalanced data problem prevalent in network traffic. An effective solution to this problem has been provided through the technique of Synthetic Minority Over-sampling Technique (SMOTE). This technique brings about balance in the dataset by generating synthetic data points that are similar to the minority class. Many variants of SMOTE have been proposed over the recent years which have been scientifically proven to improve the performance of IDS effectively. This research article provides a comprehensive introduction to the SMOTE (Synthetic Minority Over-sampling Technique) method. It delves into the underlying principles of SMOTE and its effectiveness in improving classification performance when dealing with imbalanced datasets.
Web Application for use in Companies in the Internal Training Process of their Personnel

The times after the pandemic, are causing many changes in the way companies are trying to train their staff, virtualization is one of the main factors that make possible to fulfill this mission, at first many companies hired training services in order to maintain their training standards. In this paper present a customized web application for a company, where the intention is to demonstrate that companies can develop their own platform for courses and to customize their courses and independence in the management of the courses, the results show that it is possible to develop and implement such systems, as well as the level of satisfaction by workers as well as compliance with the proposed courses.
Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques; with Keras API and the Internet Movie Database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy.
E-Health Records Stored Over the Cloud with Automated Medication Reminders for Enhanced Patient Care

This hospital management system aims to develop a user-friendly and efficient system using PHP as the front-end interface and MySQL as the database. The system enables the management of patient information, doctor information, prescription details, and appointment details. The system provides a centralized platform for the management of these aspects, enabling healthcare providers to access and manage the data in real-time from anywhere. The system allows authorized users to add or remove doctor details, manage patient appointments and claims securely. The system has been designed to ensure the protection of personal data to speed up data processing. The system provides various features such as appointment scheduling, patient record management, doctor record management, prescription management, and billing management. Overall, the hospital management system is a reliable and efficient solution that streamlines the management of healthcare facilities.
Design and Fabrication of Adhesive Application System for Magnets in Motors

Electric Motors requires Permanent Magnets in them to function. The following paper explains the design, construction and critical analysis of an Adhesive applicant machine for magnets in rotor casings [1] [2]. Through the integration of both human and mechanical systems, this innovation addresses the problem of work efficacy in an industry. We used a scientific research model to analyze and answer the question. The industry we chose for our model is on a small scale and is very simple to work with and analyze for its full potential. Our main objective is to construct a cartridge-based method to load the workpiece into the machinery and to create the ideal end-effector system for Glue delivery on the specified path [3]. Compared to electromagnets, permanent magnets on a rotor can both simplify the design and improve the efficiency of electric machines. The absence of automated assembly techniques for large machinery is a limitation, too. The strategy for resolving this issue is building a cohesion environment where people and machines work as a unified unit capable of producing more products.
Detection of Malicious URL Websites using Machine Learning Models

In recent years, web applications have become increasingly vulnerable to hacking, with an estimated occurrence of 32,590 attacks every day. However, many web developers and website owners are unprepared to identify and prevent these attacks. Hackers utilize various techniques, including phishing websites to gain unauthorized access or compromise authentic web programmers. This research study examines the web application security and the frequency of attacks on such systems. This study focuses on the most common types of attacks and suggests effective detection methods for preventing them. Recently, the secure coding methodologies and machine learning algorithms are used to detect and block unauthorized access and phishing attacks.In almost all the existing research works, a web application is developed to test several website URLs. The findings suggest that the model is capable of detecting malicious web application attacks. Furthermore, the model compares the performance of several machine learning algorithms for identifying phishing website links and detecting with the best model.
FarmO’Cart: Multilingual Voice-Assisted Machine Learning Based real-time price Prediction to Enhance Agricultural Income

The objective of this work is to propose the use of FarmO’Cart, a cutting-edge online marketing platform, as an effective solution to modernize conventional agricultural trading practices by facilitating an electronic exchange that links farmers, retailers, and consumers. The platform provides an option to directly sell or buy agricultural products without the involvement of any middlemen, thus allowing farmers to benefit from their crop production by generating 15-20% returns and reducing the debt ratio among farmers and their suicide rate. This proposed solution, FarmO'Cart, integrates a range of innovative features designed like a multilingual voice assistant, powered by advanced ALAN AI (Actionable Artificial Intelligence) technology, enabling farmers to interact with the platform in their native language and revolutionize traditional agricultural trading practices. Farmers can put their queries or ask for assistance by simply speaking out in their native languages. The platform is also accessible in 130+ languages through the integration of the Google Translate API (Application Programming Interface), ensuring a truly global reach. These features make the proposed solution more usable for the farmer community who may not be able to understand international languages or English in general. The Bcrypt’s hashing algorithm was leveraged to provide enhanced security for user data and passwords and the incorporation of salted hashing and a variable cost factor adds robustness to thwart brute-force attacks and password-cracking attempts. By employing these cryptographic techniques, the platform ensures effective protection of sensitive information. The FarmO’Cart also offers a community platform for farmers to connect and collaborate with Agri-experts & fellow farmers to improve productivity and profitability.
Correspondence based Co-Saliency Object Detection using Deep Learning CNN

In order to find similar salient objects within a collection of images, this study presents an innovative deep end-to-end co-saliency recognition method. The existing methods for characterizing co-saliency primarily depend upon manually created measurements. However, the subjectivity and lack of adaptability of these methodologies results in weak generalization. Additionally, most methods isolate the extraction of characteristics from individual and groups of photographs, ignoring the relation among both of these characteristics that may improve the efficiency of the model. By using a multiple-stage representation for obtaining features from a CNN with high spatial resolution, the suggested method addresses these above issues. This study exploits the learnable consistency using the improved Convolutional auto encoder. At last, final co-saliency maps are generated by fusing the intra-image contrasts and the inter-images stable feature sets. Results from experiments show that the proposed method outperforms with other approaches in terms of efficiency.
An Advanced Keyword Searching Model with Data Security in Cloud Computing

The growing popularity of cloud computing has necessitated the development of more efficient and secure methods of searching and retrieving data from cloud storage. In this paper, we propose an advanced keyword-searching model that ensures data security in cloud computing environments. To ensure data confidentiality and safety, our model employs advanced encryption techniques such as symmetric-key, homomorphic, and attribute-based encryption. Also, this study introduces an optimized indexing technique using a binary search algorithm to reduce search time and improve search efficiency. Furthermore, our model employs a secure multi-party computation approach to enable fast computation between multiple parties while keeping private information private. Using a benchmark dataset, this study demonstrates that the proposed model achieves high accuracy and efficiency while maintaining data security. The proposed model can be used in various applications, such as healthcare, finance, and ecommerce, where sensitive data must be securely stored and retrieved. The proposed model provides an efficient and secure solution for keyword searching in cloud computing environments.
Securing the Cyber Supply Chain: A Risk-based Approach to Threat Assessment and Mitigation

The system of Cyber Supply Chain (CSC) is characterized by its complexity, consisting of several subsystems, each responsible for a distinct set of responsibilities. Securing the supply chain presents a challenge due to the presence of vulnerabilities and threats throughout the system that has the potential to be taken advantage of at any time, considering that any component of the system is susceptible to such attacks. As a result, supply chain security is difficult to achieve. This has the potential to create a significant interruption to the overall continuity of the company. Therefore, it is of the utmost importance to identify the hazards and make educated guesses about their likely outcomes so that organizations can take the appropriate precautions to ensure the safety of their supply chains. By leveraging a range of factors, such as the expertise and incentives of threat actors, Tactics, Techniques, and Procedures (TT and P), as well as Indicators of Compromise (IoC), the analysis of Cyber Threat Intelligence (CTI) offers valuable information on both identified ansignd unidentified cybersecurity threats. In order to increase the safety of the cyber supply chain, the purpose of this article is to investigate and speculate on potential dangers. The CTI and Machine Learning (ML) approaches have been employed by us in order to study and forecast the risks based on the CTI attributes. This makes it possible to detect the inherent CSC vulnerabilities, which enables suitable control. To enhance the overall security of computer systems, it is imperative to implement specific actions, including the collection of CTI data and the adoption of various machine learning techniques. These techniques encompass Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, which are employed in analyzing the Microsoft Malware Prediction dataset to create predictive analytics. This is done in order to illustrate that the technique can be applied to a variety of situations.As input parameters, the experiment takes into account the assault and the TTP, while as output parameters, it takes into account vulnerabilities and indicators of compromise (IoC). According to the findings of the investigation, the most foreseen dangers in CSC are spyware and ransomware, as well as spear phishing. When it came to forecasting vulnerabilities, the predictive models that were produced using the Random Forest algorithm obtained the best accuracy rate of 91%, while the predictive models that were developed using the LR method earned the highest accuracy rate of 86%. In light of the results, the paper strongly advise putting appropriate controls into place in order to combat these dangers. The paper strongly recommend that the ML predicate model make use of CTI data in order to improve the CSC’s cyber security on the whole.
On the Localization Properties of Swarm Intelligence Algorithms

This study explores the use of Artificial Bee Colony (ABC), Differential Evolution (DE), Firefly Algorithm (FA) and Grey Wolf Optimization (GWO) swarm intelligence based optimization methods on four search spaces including Sphere, Rosenbrock's saddle, Rastrigin's and Ackley's function. The results showed that GWO had faster convergence with higher precision and was suitable for simple optimization problems. In contrast, DE optimization required more time to converge but managed to locate the minimum point, making it suitable for complex problems. The application of these optimization methods can have significant benefits in the field of Geographic Information Systems (GIS) and landslides. Overall, the findings of this study provide a basis for further research and exploration of the potential applications of ABC, DE, FA and GWO algorithms in the context of GIS and landslides, which can ultimately improve decision-making efficiency and accuracy in these fields.
New Fuzzy IBE System using Odor Detection

Till date, all the fuzzy identity-based encryption (IBE) cryptosystems for generating public keys used biometrics namely finger print, iris biometric identification, voice-based identification, and other set of identification types. This work is a concept for combining both fuzzy IBE system with human odor thresholds as identities. Naturally human body develops a one-inch layer of odor on skin, which will be used as identity for public keys generation and by using sample-left algorithm the size of public keys is minimized.
Design and control based on the concept of an inverted pendulum

The availability of affordable electronic components has made the learning of experiment-based control theory accessible to a wide population. In this paper, the concept of an inverted pendulum on a self-balancing robot will be experimentally demonstrated. The self-balancing robot is mathematically modeled through equilibrium equations, and a PID controller is used to control the tilt angle. The conducted experiment and measurements show that this approach gave useful and efficient results. This research has shown that in a simple and inexpensive way it is possible to practically create a device that can serve to understand the control theory and all those small details that the experiment carries with it.
The combined use of IoT and Blockchain in Logistics: a comparative experiment

In the last decades, thanks to their ubiquitousness, IoT devices have become part of our lives by introducing innovative services that simplify common activities and, therefore, enhance people daily activities and duties. In addition, their usefulness has been further amplified by the combination of the IoT services with various technologies and innovations. For instance, the Distributed Ledger Technologies (DLTs) and the blockchains are supporting the IoT domain with their useful services and properties, e.g., trust and automation, since some years. However, the increasing interest in such a combination and the race towards the most significant innovation in the sector is shifting the focus away from some of the most important issues and questions. One of them regards the usefulness of the adopted technologies and their effectiveness with respect to similar alternative solutions. For example, the most frequently used approach to save data in a DLT foresees the simultaneous use of an off-chain database to store the actual data, and a DLT platform itself to guarantee some properties, e.g. to verify the immutability of such data. Such a common practice is adopted by almost every work on the domain but the real benefits brought by this approach are not usually measured. For this reason, the present paper is focused on an evaluation of two approaches: one based on the combination of DLT and off-chain databases, and the other one based only on the usage of the DLT. A realistic logistics use case has been introduced to compare the performances of each approach.
Security and Privacy Enabled Framework for Online Social Networks using Blockchain

Users may easily deal with data overload concerns while navigating Online Social Networks (OSN) through suggestion engines. In addition, it increases a number of privacy-related issues, particularly for users who would rather remain anonymous on OSNs. Furthermore, certain vendors of services sell consumer data to other organizations without the consumer’s awareness as a result of the exploitation of online consumer data, which undermines user confidence in the vendors of services. Such issues demand a solution that gives consumers of technology the independence and authority they sorely need over their data. As blockchain technology develops, numerous research organizations are working with distributed techniques to find a solution to the privacy invasion and hacking problem that OSN users face. A blockchain-based hybrid framework for confidentiality maintenance is proposed, to overcome these restrictions. To guarantee the confidentiality and anonymity of user data, two smart contracts, SCSGI and SCSTI are created. While the SCSTI retains information, the SCSGI controls transfer of user information. To identify rebellious nodes, a Graph Attention Network (GAT) was incorporated into the blockchain-enabled platform. The research and trial findings reveal that the suggested approach exemplifies efficacy and confidentiality of consumers in the system.
Systematic Literature Review on Deep Learning Techniques in Electronic Health Records

Nowadays there is a tremendous amount of digital information stored in Electronic Health Records (EHR). The EHR incorporates a wealth of information consisting of clinical notes, patient information, medications, procedures, laboratory test reports and so on. Thus, it has also led to huge impetus on exploiting the EHour R to aid clinical decision support systems. There is a growing research body in this direction to develop useful insights from FHR The typical EHR data is irregularly sampled, heterogenous, multi-modal lacking annotations with noisy and missing information. An additional challenge of need for privacy also means very limited publicly available datasetsfor benchmarking. Machine learning techniques are at the forefront of this effort in providing tools to analyse the huge amount of heterogeneous data. Recent advances in deep learning techniques have immensely contributed to growing applications using EHR Towards this, overview of recent advancements and techniques employed to analyse EHR data is introduced. A review of the literature and discuss the challenges of different approaches. In addition to that birds-view summary of the methods to aid future research improvements and trigger innovative applications in healthcare.
Evaluation of passive OS fingerprinting methods using TCP/IP fields

An important part of network management is to keep knowledge about the connected devices. One of the tools that can provide such information in real-time is passive OS fingerprinting, in particular the method based on analyzing values of specific TCP/IP headers. The state-of-the-art approach is to use machine learning to create such OS classifier. In this paper, we focus on the evaluation of this approach from several perspectives. We took two existing public datasets and created a new one from our network and trained machine learning models to classify the 4 most common operation system families based on selected TCP/IP fields. We compare different models, discuss the need to round TTL values to avoid over-fitting, and test the transferability of models trained on data from different networks. Although TCP/IP-related characteristics of individual operating systems should be independent on where the device is located, our experiments show that a model trained in one network performs much worse in another one, making model creation and deployment more difficult in practice. A good solution may be to combine data from multiple networks. A model trained on a combination of all three datasets exhibited the best results on average across the datasets.
Edge Detection Using Vector Quantization And Local Entropy Measures Applied To Spectrogram Component Extraction

In this paper, an approach to detect edges of signal components in multicomponent, stationary and nonstationary signals, independent of their time or frequency support, by application of K-means algorithm and local entropy measures to the time–frequency (TF) plane is proposed. The approach is based on the use of a K-means algorithm as an edge detector rather than object detector, that can maintain continuity of the edge on stationary and nonstationary signal components. Local entropy measures are applied to select appropriate K-means class to represent the edge as well as to estimate the continuity of the edge. When common edge detectors are applied, the continuity of the edge can be compromised by different occurrences such as the lack of contrast in an image or the detector working in one direction, so different methods like smoothing filters or histogram equalisation are often used as a corrective measure. The proposed K-means based method does not require additional smoothing or equalizing methods and no blurring or loss of information is present, which results in a robust method of detecting continuous edges of the useful information content in the time-frequency plane for both stationary and non-stationary signals. Since the K-means based algorithm does not depend on the contrast present in the image, the edges can se preserved in greater percent than commonly used edge detectors. Simulated signals are corrupted by different levels of Additive White Gaussian noise (AWGN) of 0dB, 3dB and 5dB. The results show the proposed method correctly detects components of an image with a continuous edge. The level of continuity of the proposed method is compared to the level of continuity of commonly used edge detectors, where results show the proposed method outperforms common edge detectors in case of non-stationary signals.