Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data Enabled Intrusion Detection with Honeypot Intelligence System on Apache Flink (BDE-IDHIS)

The importance of data security and data analysis strategies for Big Data has recently altered as a result of an enormous amount of data and its continual expansion. An intrusion detection system (IDS) is a device that keeps track of and examines data in order to find intrusions in networks and other systems. The data analysis process is exceedingly complex and requires a huge volume, variety, and speed of data to detect assaults using traditional methods. In order to achieve the goal of effective system protection, intrusion redirection technology has been used to identify potentially harmful access for traceless forwarding and forward it to a honeypot (HP) scheme. From a unique standpoint, HP method is a form of active defensive system that uses enticement to lure invaders so that more data may be collected. In the Apache Flink environment, this article describes a novel intrusion detection method called Big Data Enabled Intrusion Detection with Honeypot Intelligence System (BDE-IDHIS). The BDE-IDHIS technique seeks to first detect the presence of intrusions thereafter diverting traffic to the HP system for effective security of the actual system. Moreover, the MQTT Honeypot with Decision Engine and Redirection Engine is used in the BDE-IDHIS method. In addition, Apache Flink is utilized in this study to manage huge data settings. A number of experiments are used to test the experimental result analysis of the BDE-IDHIS technique, and the results are then examined from a variety of angles. The experimental outcomes stated the better performance of the BDE-IDHIS technique over other existing techniques.
Optimal Ship Route Search Based on Multi-Objective Genetic Algorithm

Optimization of ship routing depends on several parameters such as ship and cargo characteristics, environmental factors, topography, international navigation rules and crew comfort, etc. Ship route optimization is an essential problem for ship operators to reduce fuel consumption and voyage duration. This work presents an algorithm to plan safe and optimal routes for ships moving inaccessible areas and with unlimited traffic direction. The route obtained is optimized with multiple objectives which makes it more unique and optimal. An evolutionary algorithm is used to create numerous locally optimal routes, and a parallel genetic algorithm is used to construct a final route by optimizing a set of said routes.
Stock Market Graph Prediction using LSTM

There has been a rapid growth in the number of traders and investors in the stock market. A lot of them are beginners and young adults with little to no knowledge about the stock market. So it is essential to help these people out to minimise their losses and increase their profits while investing. A predicted graph of a particular stock in the stock market can help them do so. This paper focuses on the machine learning algorithm: Long Short-Term Memory to predict the graphs of a particular stock. The data is fetched from Yahoo Finance to get accurate results from the historic data. The website that has been made is very accessible for all kinds of users. It provides them with a simple input field to enter the stock to generate the predicted graph of the stock they wish to look for. It also shows the users if a stock is bullish (prices will rise) or bearish (prices will fall) for the particular week and month for their better understanding. The website also features a news section which highlights news and information related to the stock market and also the factors that affect the trends in the stock market.
Multi-Headed CNN and Vision Transformer-Based Diabetic Retinopathy Classification

Diabetic Retinopathy (DR) is a medical condition that can occur as a complication of diabetes and is characterised by blood leakage into the retinal tissues. The condition initially manifests as no symptoms or minor vision problems, which can progress to severe vision loss. DR is classified into five stages: Normal, Mild, Moderate, Severe, and PDR (Proliferative Diabetic Retinopathy). Detecting DR early is crucial to avoid severe complications, including complete blindness. The presence of certain characteristics, including soft exudates, hard exudates, hemorrhages, and microaneurysms in the affected eye, may indicate the presence of diabetic retinopathy. Early detection and prevention measures can control DR and prevent further damage to the retina. Diagnosis of diabetic retinopathy involves monitoring and assessment of fundus images. However, manual detection of DR is a time consuming process and is highly dependent on the expertise of ophthalmologists. It is also challenging for ophthalmologists to detect DR in its early stages from fundus images.Therefore, there is a need for an automated diagnosis system capable of extracting features from fundus images and classifying DR into different stages. In this regard, we propose a multi-headed CNN and vision transformer based model capable of accurately detecting and classifying all early stages of diabetic retinopathy as early as possible. Our proposed model leverages the CNN’s ability to extract local spatial features and the vision transformer’s ability to extract global contextual information. By combining these local and global features, a rich feature representation is created, which is then used to detect and classify all stages of DR.
A study on design and control of Omni-directional mecanum wheels based AGV system

Automated guided vehicles (AGVs) have emerged as crucial equipment in sectors that demand reliable and effective transportation and material handling systems. The design and control features of AGV systems are discussed in general terms in this study, with an emphasis on important factors and recent developments. The vehicle platform, sensing and perception systems, navigational algorithms, and control architecture are all essential parts of the design of an AGV system. To fit the application's unique requirements, the vehicle platform must be properly developed, taking into account elements like load capacity, maneuverability, and power supply. The AGV can sense its surroundings and maneuver on its own thanks to the choice and integration of sensors including LiDAR, cameras, and encoders. Simultaneous Localization and Mapping (SLAM), one of the most sophisticated perception algorithms, is essential for precisely mapping the environment and locating the AGV within it. Because of navigation algorithms, AGVs can efficiently plan their routes, avoid dangers, and reach their targets. Methods for establishing a path enable smooth, collision-free travel by accounting for factors including distance, time, and dynamic restrictions. Real-time control systems regulate the AGV's speed, acceleration, and steering, enabling accurate and secure operation in a variety of situations. The controls design should enable monitoring of the system and detection of problems in along with enabling integration of perception, navigating, and control modules. This paper examines the mechanical and electrical design of a test prototype autonomous guided vehicle (AGV). The SLAM technique apply in the present work successfully resolves the localization problem in conjunction with the navigation algorithm.
An Autoencoder-based Deep Learning Approach for Classifying Colon and Rectum Adenocarcinoma

Early identification of cancer types plays a pivotal role in administering proper therapeutics, which increases the likelihood of a favourable prognosis for cancer patients. However, distinguishing between colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) samples is complex, and the classification performance of machine learning algorithms in this regard has been poor. To address this challenge, an autoencoder-based deep learning classification framework is proposed. This framework overcomes the "curse of dimensionality" problem in RNA-Seq transcriptomic data by extracting highly significant compressed-domain latent-space features for classifying COAD-READ samples. The performance of the autoencoder-based classification framework was compared with two alternative approaches using different performance measures. It was found that the MLP classifier with the autoencoder model achieves the best classification performance. Furthermore, critical hub genes and survival-associated genes in COAD-READ cancers were analyzed, and functional enrichment analysis was performed.
PSO-tuned support vector classifier based Nifty50 index movement prediction using market positions and sentiment scores

Predicting the movement of the stock price index is a challenging financial time series analysis task. However, an accurate forecast may generate enormous profits for investors. Due to the complexity of stock market data, developing accurate prediction models is arduous. In this context, this study aims to develop an effective model to forecast the direction of daily returns based on the closing price of the Nifty50. The data from multiple social media platforms, including Twitter, StockTwits, Facebook, and YouTube, have been combined for sentiment analysis. The market positions have been used to train the prediction model. Performance comparison of the supervised machine learning models, including support vector classifier and particle swarm optimization-assisted support vector classifier, has been conducted. The model's hyperparameters have been carefully tuned to attain more than 99% prediction accuracy. It has been observed that the novel approach of combining the Nifty50 market positions with social media sentiments resulted in high prediction performance, and the proposed model is robust and dependable to forecast index returns.
Influence of Opinion Formation on Polarization in Social Networks

Online social networks have introduced diverse discussions regarding different topics. At the same time, these platforms have also garnered the idea of shaping opinions. The increase in user engagement in case of arguments has led to the issue of polarization in online social media platforms. In this paper, we have studied the influence of opinion change on homophily in social networks and the variation in polarization. Our own new method has been proposed for analysing the modification of user opinions dynamically in different timestamps. The probability of information sharing varies depending on the opinion similarity and homophily in social networks alters accordingly. Our approach portrays the opinion modification dynamics for different network features using complex networks concepts. We have observed that on social interaction networks like Twitter, opinion, information sharing and homophily perform a significant role in the determination of online polarization. Our approach has been compared with an established approach over external datasets and the results obtained are promising.
Comparative Analysis of Deep Learning and Machine Learning models for Network Intrusion Detection

The increasing prevalence of security breaches and malicious software attacks is a major concern in the digital landscape, sparking continued interest in malware detection. Malware attacks have a significant impact on computer users, networks, businesses, organizations, and governments. Despite the development of multiple intrusion detection systems aimed at protecting data and resources from attacks, the frequent emergence of new threats and attacks poses a challenge for these systems to detect and prevent them from penetrating the network. One such attack is Advanced Persistent Threats (APTs) which can cause significant damage to computer network and organizations. To handle these attacks, the study has developed an APT detection system that uses various Machine learning (ML) and Deep Learning (DL) based classifiers, which can more effectively extract data features from huge amounts of complex data and understand patterns to detect anomalies and potential threats. This study compares and evaluate their performance and efficiency on NSL-KDD dataset. By evaluating using various evaluation metrics, it was found that Extreme Gradient Boosting (XGBoost) is the most effective model among all models, followed by Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN).
Four-layer Architecture for IoT Security in Fog Network

Fog Computing is a term made by Cisco that insinuates extending cloud computing to the edge of a network. Indeed, fog computing supports the operation of Fog/cloud, storage and networking services between end devices and conveyed processing data centers. While depending on the fog network will enhance the performance by eliminating the upper layer between IoT devices and Cloud servers by making users and devices closer to the servers. But as users become closer to the servers and data centers, attackers also become closer too, this will make the fog layer subjected more to attack and the data centers will be dangerous. Now fog computing faces new-fangled security and assurance defies other than those procured from cloud computing. In this paper work, we will list the types of attacks that affect the Fog network and we discuss the available solution, moreover, we will illustrate our new method to protect this layer from the attacks which will introduce a new security layer between IoT devices and fog network to detect and prevent the attacks from reaching the fog layer. In addition, we will protect the fog layer from blocking when an attack is presented. We also discuss some existing methods that have been suggested to address real-time security challenges. The research community should consider prospective future research directions when we look into the issues in research that need to be solved.
A Novel Deep Convolutional Neural Network For Melanoma Detection Using Dermoscopic Images

Melanocytes are the cells in our body which give the skin its brown or tan color. When these cells begin to multiply anomalously, it results in melanoma, which is one of the types of malignant skin cancer. Skin biopsy histopathological examination is one of the key techniques that pathologists use to identify the existence and advancement of melanoma in clinical scenarios. The problem of automatically detecting melanoma from microscopic skin samples is quite difficult. Nonetheless, these issues can be resolved by utilizing a deep learning strategy as a tool for computer vision (CV). In the existing methodologies, the research was done using three-dimensional hyperspectral data of various types of skin. Hyperspectral images have complex characteristics that are beneficial to classify data accurately when compared to the traditional machine learning techniques of computer vision. However, the hyperspectral data of melanoma and other clinical skin datasets are insufficient. In one of the studies, hyperspectral pathology pictures were utilized to segment melanoma using a 3D convolutional network called Hyper-net. In our proposed methodology we use the 3D RGB based images of JPEG format as training and test dataset, and we build the customized VGG16 model in which the dense layers are modified for melanoma detection with the help of fine-tuning. VGG16 is one of the best CV models to date and in our project the training accuracy and validation accuracy we achieved is 98 % and 88 % respectively, that is efficient when compared to most of the existing methods.
Air-Quality Index Prediction Using Auto Ml Library, TPOT

Air pollution nowadays is one of the most important causes of severe diseases. Early analysis of air pollution helps us control pollution levels and keep human health at utmost importance. To solve this problem, a real-time model is required to be constructed that can help us solve this air pollution problem. We're studying how we can use a variety of machine learning models for forecasting air quality index 'AQI' in this paper. The data collected by the air quality sensor are taken and averaged to obtain an AQI. The gathered metrics are analysed using automated library TPOT.
Network Analysis of Research Base Papers: Metrics and Potential use

This paper describes a common challenge faced by researchers who struggle to find good references for their current domain of study due to the exponential increase in research papers being published daily. The proposed solution involves using deep learning techniques with the BERT Model to obtain a summary of the published paper and its citation papers. Science mapping approaches are then applied to ascertain the similarity between the reference papers and the original paper and rank the citations based on their similarity. Various bibliometric analyses such as citation analysis, co-citation analysis, bibliometric coupling, and network metrics are used to further rank the citations and identify unique citations with citation count. Additionally, author suggestions are provided. The end goal is to develop a visualization tool in the form of a dashboard to help researchers focus on relevant papers.
Enhanced extremely boosted neural network (EXBNet) for effective Heart Disease Prediction

Cardiovascular disease is considered to be one of the major causes of deaths around the world. Hence, a reliable, accurate, and efficient approach is needed to diagnose such diseases at an early stage so as to start the relevant treatment procedure. Machine learning and Neural network plays a critical role in processing massive amount of data in the field of healthcare. Artificial Neural Network (ANN) and Deep Neural Network (DNN) techniques have been used in earlier research to predict heart diseases. The proposed methodology presents an effective heart disease prediction using an Enhanced Extremely boosted neural network (EXBNet) that can assist medical professionals. The proposed method, Enhanced XBNET is a variation of XBNET where Gini Index is used to compute the feature importance of Gradient Descent trees. XBNET is an ensemble model that combines both Gradient Boost tree-based models and Feed Forward Neural networks. The experiments were conducted on the data that has been collected using Kaggle datasets. The performance of proposed methodology is compared against various state-of-art methods using different classifiers and are evaluated using the performance metrics Accuracy, Recall, Precision, and F1 score. The research methodology provides an enhanced performance with an accuracy of 100%. The proposed methodology is compared with various state of art algorithms and it has been proved that the proposed method outperforms other algorithms.
Free Space Optical Quantum Communication Channel Modelling to Predict Losses under Diverse Weather Conditions

One of the most rapidly developing communication technologies is quantum communication. Quantum Free-Space Optical Communication (FSO) provides unlimited bandwidth for data transmission. A key barrier to FSO links is associated with the losses caused by atmospheric absorption and scattering of molecules and aerosols. In the present study, we analyze the performance of a free-space classical link for 650 nm, 785 nm, 850 nm and 1550 nm wavelengths for a link range of 0.5 Km and 50 Km. The losses due to atmospheric components such as rain, fog, snow and turbulence are predicted for different wavelengths. Also, we presented similar analysis for free space quantum link using difference frequency generation(DFG) method for both short range (0.5 Km) and long-range link (50 Km) under diverse weather conditions. It is done by combining weak wavelengths of 650 nm, 785 nm, 850 nm and 1550 nm, along with a tunable pump LASER (Krypton) to generate IR B waves (1400 nm - 3000 nm). The total atmospheric losses with DFG is compared with the losses generated without using DFG.
CFD modelling of the heat transfer of photovoltaic modules

In this work the heat transfer of a PV solar module is investigated with CFD simulations. Conjugate heat transfer simulations are created, in which the air flow with the convective heat transfer around the solar panel and the heat transfer inside the PV module are both simulated. The radiative heat transfer is also modelled using the discrete ordinates method. First, the PV panel is simulated without wind flow, where the module induces a buoyant flow of warm air. Then, several cases are investigated with wind flow, considering different wind speeds and wind directions around the panel.
A Deep Learning Fusion Approach for Automatic Diagnosis and Grading of Diabetic Retinopathy

Diabetic retinopathy, a severe complication arising from diabetes, exerts its impact on the retina. Timely identification plays a pivotal role in facilitating successful treatment. In this regard, this study explores the utilization of 3 state-of-the-art CNN architectures and proposes a fusion based approach to enhance the accuracy of diabetic retinopathy classification. The CNN models, namely EfficientNet-B0, MobileNet, and ResNet50 are explored on a public dataset of retinal images, MESSIDOR. The proposed approach achieves better performance than previous methods. On MESSIDOR, it achieves 82% accuracy for referral/non-referral binary classification and 63% accuracy for multi-class classification.
BigBasket Fairness Analysis for Searched Outputs

This paper presents a fairness analysis of the ranked outputs of BigBasket website. Using live data set and by applying rKL, rRD, rND, nDCG. We examine whether the ranking algorithms treat all products,sellers fairly and equally, regardless of their demographic or other characteristics. We analyzed the ranking algorithms using various fairness metrics, including group fairness and individual fairness. We identified the key features and criteria that influence the product rankings. We find that the ranking algorithms are complex and multifaceted, taking into account various factors such as product relevance, popularity, ratings, reviews, and pricing. We applied different measures to several real-time scraped data-set and detected cases of bias. Additionally, We observed that the ranking algorithms are dynamic and adaptive, constantly adjusting to the changing user preferences and market trends. Our findings have significant implications for e-commerce platforms, regulators, and researchers interested in promoting fairness, transparency, and diversity in the online marketplaces.The implementation of this work is available at https://github.com/deep9069/Bigbasket-Fairness-analysis-Btech-Projec.
Automatic evaluation of cracks with semantic segmentation model with U-Net and an Efficient Net-b2

The appearance of cracks in underground infrastructures when presenting gives meaning of weakening, Peru being a seismic country it’s crucial to have the capability to locate the cracks, because it could produce an irreparable problem. It proposes a semantic segmentation model with U-Net and an efficient Net-b2 process with visual evaluation to carry out a detection system without contact, more fast and provide greater safety to workers. The system is based on digital image processing because it is a modern technique that allows combining various techniques in order to identify a particular object, in addition to creating a database with the processed data for subsequent control or monitoring. Our findings are an accuracy of 98.3% between the images labeled as cracks and non-cracks, and it indicates 97.61% by using filtering process according to the morphological operations and surfaces, better results were obtained in extracting the edges of the cracks.