Reviewerly/exHarmony huggingface dataset:
Secure Data Communication in Vehicular Networks for Disaster Rescue

Ground recovery automobiles operating in the regions impacted by the catastrophe should coordinate their efforts and communicate a large amount of data with one another. This data should include rescue orders, details on the state of the roadways, and data on prior rescue missions. This will guarantee that you are able to drive safely and effectively respond. Unmanned aerial vehicles (UAVs), which can be used to conduct instant recovery services in places that are destructed and assist in sharing data for ground Vehicular networks, can be utilised when connectivity investments are damaged as a consequence of climate change. UAVs can help share data for field Iot environments and can conduct recovery services in areas that have been damaged. However, in a tragedy scenario involving UAV-assisted IoV, there are possible safety threats on intelligence sharing among machines and UAVs. These threats are the outcome of an unreliable distributed environment, unreliable improper behavior tracing, and minimal information. These issues could compromise the integrity of shared data. The interconnection of the relevant vehicles and unmanned aerial vehicles (UAVs) makes these dangers possible. In this article, we create a lightweight vehicular blockchain-enabled secure (LVBS) data exchange architecture for the Automotive Internet of Things that is helped by unmanned aerial vehicles (UAVs) to assist in hurricane relief. The concerns which have been voiced will be addressed more effectively as a result of this. To begin, we will discuss the new UAV and smart contracts collaboration high - altitude system architectures that we have developed for usage in places that have been affected by natural disasters. A resource consensus method is developed for the lighter vehicular ecosystem in the second step of our project. This not only makes it possible for us to track and record data exchanges in a manner that is both safe and immutable, but it also helps us arrive at a majority more quickly while ensuring that its degree of safety is not compromised. In the end, comprehensive models are tested, which demonstrate that LVBS has the ability to effectively increase the safety of the consensual phase and stimulate the data exchange of a great quality. This is shown by the fact that LVBS has been shown to have this capacity.
Detection of Disease Through Clinical Data Using Light Intensity Based Firefly Algorithm

In this study, we presented a hybrid gene selection model based on a novel multi-filter(MF) ensemble technique and light intensity difference-based Firefly Algorithm. A multifilter (ReliefF, Fisher Score, SU (Symmetrical Uncertainty), CFS (Correlation-based Feature Selection)) is applied to the original micro-array dataset to evaluate the best-ranked feature subsets individually. Ensemble multi-filer coupled with light intensity difference based firefly algorithm to achieve the optimized feature subset and at the last performance, evaluation is done using Support vector machine (SVM) by considering three different kernel parameters. From the experimental analysis, it is noteworthy to assure that the proposed methodology performs better than the existing approach. In this paper ensemble, a multi-filter method is used by implementing the concept of correlation-based redundancy and a Modified firefly algorithm (FA) called Light Intensity Difference Based Firefly Algorithm(LFA) is used as a wrapper to identify the optimal high-ranked feature subsets. So the selected subsets will be more robust for classification purposes. The correlation-based redundancy approach is used for the multi-filter case, where the top n genes of individuals are identified and a new dataset is prepared, which will be considered as input for the wrapper method. The main drawback behind considering this single filter may be trapped into the local optimum and may increase the complexity of the wrapper which may reduce the performance of the proposed model. Using multifilter (MF) not only considers all possible combinations of feature subsets by considering more than one filer but also provides a better opportunity to achieve better classification accuracy.
Anti-Forensic Technique Using Alternate Data Streams for Data Hiding in NTFS

Data hiding techniques are one of the most commonly used anti-forensic methods by attackers or perpetrators. There are many data hiding techniques in various kinds of file system in different OS. Alternate data streams (ADS) in NTFS are one such means by which data can be hidden in Windows OS. In this work, ADS will be used to demonstrate how data can be hidden in it, to include different kinds of data files. The same will be implemented using command prompt. Lastly, we compared the data hiding technique using ADS in Windows 10 and 11 Operating System.
ABSADM - Aspect-Based Sentiment Analysis using Distance Matrix

Sentiment analysis is a natural language processing approach that identifies the emotional tone of a text body. Aspect-based sentiment analysis is a text analysis technique that identifies the aspect and the sentiment associated with each aspect. Different organizations use aspectbased sentiment analysis to analyze opinions about a product, service or idea. In this paper, ABSADM, a novel method to perform aspect-based sentiment analysis is proposed. ABSADM uses the concept of distance matrix to determine the sentiment associated with each aspect. Distance matrix is constructed to calculate the distance between aspects and the words expressing the sentiment related to the aspect. It works at the sentence level and identifies the opinion expressed on each aspect appearing in the sentence. It also evaluates the overall sentiment expressed in the sentence. The proposed method can be used to perform sentiment analysis of any opinionated text.
Application of Needleman-Wunsch Algorithm in Image Comparison

The Needleman-Wunsch algorithm is a global sequence alignment algorithm that has traditionally been used for DNA, RNA, and protein sequencing. Image comparison is used in multiple applications like distorted face image recognition and color identification. In this paper, image comparison has been introduced as a new application of the well-known Needleman-Wunsch algorithm. The global sequencing algorithm has been used to align images using pixel values. Two image comparison applications have been demonstrated, illustrating the use of the Needleman-Wunsch algorithm. First, the Needleman-Wunsch algorithm has been used for distorted face recognition. Next, traffic light color identification has been performed using the alignment technique. This work provides an insight into the Needleman-Wunsch algorithm and establishes a new application for it.
Big Data Analytics Architectural Data Cut off Tactics for Cyber Security and Its Implication in Digital forensic

The Internet is very swiftly expanding today than our imagination that we see now. At present more than half of the world’s population involved in the cyber world. There is plenty of data generation with this huge population. These data are of varying degrees and speeds, collectively referred to as Big Data. This rapid growth of the Internet has led to a significant increase in the type and frequency of cyber attacks. There are many well-known cyber security solutions to combat these attacks. Many corporate studies are now focusing on security analytics, such as the application of Big Data Analytics techniques to cyber security. The literature reveals a plethora of Big Data cyber security analytics systems. Here, in this paper we proposed to adopt a new data cut off architectural strategy for big data cyber security analytics. And finally, we give an example of how this proposed data cut off tactics used in the digital forensic investigation process.
Document Signature Recognition and Verification Using Neural Network

For decades, a person’s signature has served as a distinguishing element in identifying them. Manually verifying members signature is sometimes impractical when a significant number of documents, such as bank checks, must be certified in a short amount of time Signatures are a safe and secure way to authenticate and authorise people. As a result, technologies for automated conformation is required. The current thesis work is in the topic of signature authentication systems and it involves extracting some unique characteristics that build a signature hard to forge. It analyzes a specific individual based on the features of signature. Many people nowadays falsify, or utilise someone’s identity documents and photographs in illicit ways. As a result, in order to prevent such illicit use, the system assists in detecting modified photos and documents.
Virtual Air Canvas Using OpenCV and Mediapipe

Recent developments in artificial intelligence have made it feasible to operate a digital environment by using human gestures. Gestures are often used as a kind of nonverbal cueing in the field of computer language instruction. Virtual Canvas is an application in which we can draw by capturing the gesture of hand and fingers through web camera. The techniques used in this project is OpenCV and Mediapipe under the category of Computer vision. The language used in this project is Python since it is easy to make use of the syntax and understanding the basics as well as it can be implemented in any OpenCV supported languages. The project works by monitoring to determine the landmarks of hand, then the id’s of finger tips are taken for modes like selection, drawing and clearing the canvas.
Analysis of Credit card fraud detection techniques using Machine Learning*

Banks have been dealing with an increasing credit card default rate as the number of credit card customers grows. As a result, data analytics may help solve the current problem and control credit risks. Predicting a client’s future status Choosing between defaulters and non-defaulters is a challenging but crucial task for any bank.The primary goal is to lower the number of non-defaulters among defaulters. This is because the bank can afford to treat non-defaulters as defaulters, but not defaulters as non-defaulters, because the underlying assumption is that the bank has a stringent no-NPA policy. There are three parts to this paper. In the first phase, we analyzed and cleaned our initial data set. The second portion is devoted to data pre-processing, in which we perform one-hot coding on categorical variables to convert them to a machine-readable format. The third component focuses on selecting an appropriate assessment metric, modelling data sets, and comparing them.
A GNN Based Multi-Attribute Pedestrian Recognition Framework for Real World Surveillance Scenarios

Due to low resolution images, ongoing image modifications, and the different spatial distribution of unbalanced qualities, predicting a list of pedestrian attribute from surveillance videos is a highly difficult challenge for computer vision (CV).The extensively-used convolutional neural network(CNN) and Transformers process the image as a sequence or grid like structures.They are not tensile enough to process complex and occluded objects.Hence it is preferred to enhance recognition performance by modelling the label connections between different attributes since every pedestrian often possesses a variety of qualities in real time. In this paper, we discuss pedestrian using multi-label categorization of attributes with an unique model based on the graph neural networks (GNN).We implement and demonstrate the comparison with CNN models and Attribute GNN (AttrGNN) architecture which is used to extract graphical level features for visual tasks.We construct our AttrGNN model to alter and communicate data efficiently among all the nodes based on the graphical representation of images. Then we use feature transform to help AttrGNN generate features between the nodes. This paper actively compares between the pyramid architectures of CNN and GNN by extensive experiments on pedestrian attribute recognition(PAR) datasets to prove that GNN outperforms CNN architectures and also illustrate that AttrGNN can be used as an architecture to perform general visual tasks.
An Analysis of how Computer Graphics and Image Processing Are Used in Art Design

In the era of high-speed information, with the widespread use of networks and the rapid advancement of multimedia communication technologies, human society has entered a significant new evolutionary stage. As a result of the informationization of society, human productivity and lifestyles have been greatly impacted. Recent advances have also been made in the area of computer-aided image processing. Photoshop has become a must-have software for computer graphics and image processing professionals. Rich functionality, ease of use, and humanization lead to increased labor productivity in processing computer images and photographs. Photoshop is a very effective tool for graphics and image processing techniques. The software is so robust in its image processing capabilities that it can produce the highest quality graphics and image processing. This technology is commonly used in the workplace. Professionals must act skilfully and understand technology in detail. Photoshop is widely used in graphic design and image editing these days. It is used in a variety of image processing contexts in addition to meeting the relevant processing needs of consumers.
Federated Learning Method for Local Differential Privacy in IoT Networks

The Internet of Things is a young technology with great potential. The fact that the creators of crowdsourcing programmes may easily determine users’ location data, traffic data, motor vehicle details, environmental information, etc., raises major sensitivity personal data privacy concerns for users. The cost of connectivity here between vehicles and the cloud server also considerably rises as the number of cars grows. In this research, we propose federated learning with local differential privacy integration to support machine learning model development in crowdsourcing applications while minimising privacy risks and reducing communication costs. We explicitly recommend using four LDP approaches to disrupt slopes caused by moving vehicles. The proposed Three-Outputs method provides three different output options to achieve a high accuracy when the private budget is constrained. The output options of 3 can be coded with just two bits to reduce transmission expenses. Additionally, when the privacy cost is significant, a perfect piecewise approach is recommended to maximise performance. In addition, we provide a suboptimal method with a simple formula and comparable utility to PM-OPT. Then, we develop a completely new hybrid mechanism by fusing Three-Outputs with PM-SUB. In order to synchronise the cars and cloud server for collaborative model training, an LDP-FedSGD technique is suggested. Extensive experiments on real data sets support the possibility that our suggested methods could safeguard privacy while maintaining value.
Machine Intelligence Based Web Page Phishing Detection

In recent years, advancements in Internet and technologies have led to a major increase in electronic trading during which consumers make online purchases and transactions. This growth results in unauthorized access to users’ sensitive information and damages the resources of an enterprise. In terms of Website interface and uniform resource locator (URL), most phishing Webpages look the image of the particular Webpages. because of inefficient security technologies, there’s an exponential increase within the number of victims. The anonymous and uncontrollable framework of the Web is more at risk of phishing attacks. Existing research works show that the performance of the phishing detection system is restricted. There’s a requirement for an intelligent technique to shield users from cyber-attacks. During this study, a model has been proposed that a URL detection technique that supports machine learning approaches. In this work, phishing websites has been detected using autoML and convolution neural network (CNN) techniques with and accuracy of 98% and 85% respectively.
Comparative Study of Clustering Algorithms in Parallel and Serial Environments

Machine learning has become a core part of computing and has affected countless sectors with better implementations of existing systems. Machine learning algorithms use various methods to organize and learn from data and Clustering is one such method. Clustering as the name suggests, forms different clusters of data from the dataset based on the characteristics. However, clustering datasets could be onerous and might become worse when the number of clusters or if the number of data points is increased. Parallelizing the algorithms is one way by which the time taken can be reduced. Clustering algorithms can be parallelized by optimizing the algorithm to make use of multiple CPUs or multiple cores of a single CPU by sharing the workload. This paper focuses on the performance analysis of parallelized clustering algorithms and other mainstream clustering algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise), K-Means, Mini-Batch K-Means, Mean Shift are the chosen algorithms from different types of clustering to diversify the comparison. This paper will provide a comparative analysis of the performance between the different clustering algorithms by controlling the environment to either be single or multi-threaded.
An Intelligent Classification Model for Camera Captured Unstructured Handwritten Documents with Intra Class Similarities

The overall aim of this study is to classify camera-captured handwritten document images with intra-class similarities and unconstrained layouts. We investigate at a dataset of real-time handwritten notes that are frequently utilised for academic assessment in higher education institutions. For the classification of handwritten document images, we introduce a deep learning-based methodology. The models used for integration into the proposed workflow for document classification are ResNet50, MobileNetV2, and Inception ResNetV2. A database of 11676 samples from twelve different classes and three different scripts has been obtained for the purposes of experiments. Depending on the subjects, each class of documents is established by determining keyword-based characteristics. The learning performance of deep learning models is analysed in terms of training, validation, and test sets, which are sampled in the ratio of 80 to 20. The results show that document classification is effective with MobileNetV2, with an accuracy of roughly 99 percent for training and validation datasets and 92 percent for test datasets, with average precision and recall of 86 percent and 82 percent for twelve categories, respectively.
Drone/UAVs Surveillance feeds Video Object Recognition Using Transfer Learning

The objective of this research is to demonstrate the use of a convolutional neural network (CNN) for object detection (OD) on drone videos (CNN). The goal of the study is to determine how well the YOLO V3 (Y-V3) and feature extractor recognise objects in drone-shot video frames the real-world.According to the study, application of several target identification algorithms to “regular” images captured by “normal” cameras has different efficiency impacts on the quantity, precision, and target performance consumption, as well as when applying the technique to image data collected by drones. Unmanned aerial vehicles (UAVs), a very active domain in this field, are crucial to the implementation of any robot’s complete autonomy due to its enhanced capability of OD. We have conducted numerous experiments to address our functional issues to investigate the efficacy of the most sophisticated target detection algorithm in the image data acquired by UAV. In this study, the algorithm’s performance metrics are tallied after being tested on 1099 photos.
Real-Time Word Level Sign Language Recognition Using YOLOv4

Sign Language is a communication language for hearing and speech impaired people. To establish communication between hearing/speech impaired people and normal people, there is a need for an efficacious translation system. This paper proposed a real-time, vision-based sign language recognition system for translation from sign language to text based on deep learning. To achieve real-time recognition and translation, The proposed methodology uses You Only Look once version 4 (YOLOv4), a single stage detector to achieve real-time recognition and translation. For experimentation Indian Sign Language Dataset for Continuous Sign Language Translation and Recognition Dataset (ISL-CSLTR) is used. This dataset is extended further by adding images from additional two signers to increase the already available dataset. Pre-trained network is employed and fine-tuned on the extended ISL-CSLTR dataset. Data augmentation is also employed to increase the accuracy of the system. This proposed method achieves a mean average precision (mAP) of 98.4% and real-time translation. The results obtained makes the system applicable for real world applications which is the main aim of this experiment.
Analysis of the Performance, Scalability, Availability, and Security of Cloud Computing in Different Cloud Environments

The practise of distributing data, programmes, and other resources over several computers connected via a network is referred to as ‘‘cloud computing Cloud computing allows us to perform all of these tasks by allowing us to use remote servers for information transfer, storing, and retrieval. Because of the huge number of Virtual Machines that cloud computing makes open, we are currently ready to do computations in equal. Performance, scalability, accessibility, and security issues with the cloud could turn out to be the most serious issues from here on out. We will examine the difficulties of scalability, accessibility, and security with regards to cloud computing here, as well as the means we have taken to work on the security and reliability of our foundation while working here. Recent breakthroughs and advancements in information technology have led to a rise in popularity and admiration for cloud computing. Access to services is made feasible via cloud computing at any time. Cloud computing utilises numerous remote network sites and servers that are connected to the internet for the purpose of storing, managing, and processing data instead of utilising a server that is a part of a local area network (LAN) or a single computer. In contrast, traditional computing uses a single server and relies on a single computer. Because of the many advantages it provides, such as massive processing power at a low cost, great performance at a scalable level, stability, accessibility, and availability to customers, cloud computing is currently a highly sought-after service. The performance of cloud data, issues with cloud data security, and difficulties with cloud data environments are all examined in this research study. This poll provides some insightful information that can be used in the analysis of current cloud issues and problems. For upcoming cloud computing enhancements and expansion, testing and analyses of the performance and scalability of software services delivered via the cloud are necessary. Three aspects of performance requirements for cloud-based software services are connected to one another: scalability, flexibility, and efficiency. The results section looks at Xlive at 11%, the World Map at 15%, and Cloud Security at 22%.