Reviewerly/exHarmony huggingface dataset:
Detecting Phishing Websites using Machine Learning Algorithm

In general, malicious websites aid the expansion of online criminal activity and stifle the growth of web service infrastructure. Therefore, there is a pressing need for a comprehensive strategy to discourage users from going to these sites online. We advocate for a method that uses machine learning to categories websites as either safe, spammy, or malicious. The proposed system is limited to examining the URL itself, rather than the contents of websites. As a result, it does away with both browser-based vulnerabilities and run-time delays. The proposed method outperforms blacklisting services in terms of generality and coverage since it makes use of learning techniques. There are three distinct categories for website addresses. Neutral Web sites provide average, risk-free functionality. For a website, “spam” refers to any attempt to overwhelm the user with advertisements or sites (such as false surveys and online dating sites). Malware is defined as a website designed by hackers to cause harm to computers and steal private data. The experimental data demonstrates a dramatic improvement in performance with the new model compared to the baseline.
Detection of Cancer in Human Blood Sample using Machine Learning

The process of identifying blood problems involves a human being looking at a blood sample under a microscope with their unaided eyes. In this study, a computerized method was created to aid doctors in recognizing various forms of leukaemia. Initial segmentation is performed using K-Mean clustering once the RGB image has been transformed to L*a*b color space. The properties of this clustered image are extracted and divided into various forms of leukaemia. This method is used to recognize the illnesses and provide an early diagnosis. Since images are inexpensive and don't require any expensive testing or lab equipment, they are used as inputs. In order to investigate any changes in colour, texture, geometry, and statistical analysis of the images, this research will make use of features in microscopic photographs. Proposed method will feed the changes discovered in these features into our classifier. Since images are inexpensive and don't require expensive testing or lab equipment, they are used. Leukemia, a disease of white blood cells, will be the system's main focus. The system will make advantage of microscopic picture attributes to analyses statistical changes in texture, geometry, and color.
The Three-Tier Architecture of Federated Learning for Recommendation Systems

Recommender systems are now vital in the Internet age to assist users in finding helpful stuff and reducing information overload. To assist users in finding personalized stuff, a large amount of user-sensitive data used for recommendations may lead to privacy violations. In recent research, federated learning-based recommender systems structures have made tremendous progress in boosting prediction accuracy while providing privacy. However, challenges still need to be concentrated on while employing federated learning 1) Ensuring user privacy and security of data and model privacy. 2) Heterogeneity of data in distributed entities to train a model with the best representation for better analysis, and 3) The communication between the user and server leads to increase overhead and latency. Developing a secured, privacy-protected recommender system that can accomplish high prediction accuracy is crucial and valuable. To address the above issues, a theoretical approach called a three-tier architectural solution is proposed to assure privacy guarantee without sacrificing accurate predictions on the recommendation with less overburden on a server. Further, discussed the future directions of recommendation systems by using federated learning.
Design of Random Number Sequence Generator using Multi-Stage Feedback with Multi-Stage Ring Oscillator

TRNGs, or true random number generators, are crucial to VLSI testing. This article proposes a unique approach for producing real random numbers on an FPGA using the entropy source is the unpredictable jitter of an MSFRO (multi-stage feedback ring oscillator). The traditional ring oscillator is modified by a multi-stage feedback mechanism that increases the entropy source's randomness, frequency, and range of clock jitter. We sample the phase-locked loop (PLL) of the FPGA's clock signal using the clock jitter signal generated by the MSFRO, in contrast to the traditional clock sampling structure. The collected output value is processed by XOR to decrease the variance and increase the unpredictability. MSFRO are offered to show how well the suggested Low Power BIST method performs. Verilog HDL will be used to implement this proposed design, and Modelsim Tool will be used to simulate it. The FPGA Spartan 3 XC 3S 200 TQ 144 and Xilinx Synthesis are the proposed MSFRO.
Integral Clustering and spectral Feature Modeling for Sketch-based Image Retrieval

Image retrieval using free hand drawn sketch image is addressed in the presented paper. As the volume of image information's are rapidly increasing, retrieval of information from the image data set is observed to be a tedious task. Majority of image retrieval are developed using an image querying system, however, unknown images which is given input as a free hand sketch is less addressed. The recent developments in the sketch-based image retrieval (SBIR) were developed using spectral image representation. However, the problem to the existing SBIR approach using random distribution of feature set builds a larger search overhead and the diverse distribution leads to misclassification. The objective of the proposed work is to develop a faster sketch-based image retrieval system with higher accuracy. This paper presents a new clustering-based approach for spectral feature distribution using cluster gain and information selection approach. The relative distribution and the information gains are considered in the development of the proposed approach for classification. The proposed approach observes a higher retrieval accuracy and classification performance in comparison to the existing spectral feature classification.
Fingerprint Sensor based Biometric Payment Cards

Fingerprinting for payment cards was developed by MasterCard in the 1990s to prevent stolen cards from being used fraudulently. To initiate a transaction, a customer places his or her card on a card reader, also known as a biometric payment device, attached to a point-of-sale terminal and then enters a Personal Identification Number (PIN) to authorize the transaction. The PIN and the fingerprint data on the card interact to provide the customer with sufficient security to be sure that the card will not be used fraudulently. Authentication occurs instantaneously and without the knowledge or use of the card number or PIN. Payment card companies offer privacy and security by taking various steps to secure and protect their networks. They work closely with retailers to build a network of secure networks that provide privacy. Retailers can use these secure networks, also known as the “Visa Net,” to process transactions securely. When a customer uses a payment card, the retailer stores their payment data on a back end, often called the vendor solution, which includes the transaction amounts and merchant numbers. Traditional payment cards need to be more securely authenticated. Even though companies have introduced the Wi-Fi enabled payment cards to increase transaction speed, it has many limitations, and can be easily used for fraudulent activities. Hence the objective of this work is to install a biometric sensor on the payment card to enhance security and reduce the risk of fraud.
Error Level Analysis and Deep Learning For Detecting Image Forgeries

Social media is the major image sharing resource around the internet. On the other hand, the increasing availability of photo-editing software has led to the development of fake images. Since the compression ratio of the original and false images differ, this study has a developed a novel error level analysis model to determine the true compression ratio and detect the fake images. Image data analysis is another way available to determine its authenticity, though the metadata could have been altered. Here, the Deep Learning (DL) models are used to differentiate the real and fake images present in the dataset, as well as other parameters required for error rate analysis. For 10 epochs, the proposed model has produced the best training accuracy of 90.59%.
Software Effort Estimation using ANN (Back Propagation)

Project managers often use Effort Estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, and personnel estimation are basically necessary. For every project of software, getting an accuracy in Effort Estimation has always been difficult. In case of this study, estimating the labor involved in developing software determined using a Back Propagation Model. This model's goal is to investigate the capabilities and potential uses of utilizing Artificial Neural Networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results.
Secure Cloud Storage using a Digest Algorithm

In today's cyberspace, everyone stores data in a cloud, whether it be images, personal documents, or any other, and it all accesses a remote system by using the applications/technology. With the use of cloud networks in the infrastructure, the information uploaded should be maintained secure. The spread of cloud storages helps in strengthening the recent technological developments, and it is expanding the limits of the infrastructure as well. The organizations are slowly shifting their base to the cloud systems, and some service providers help in creating space in the existing storages. Cryptography is a critical tool for preventing this from happening. It protects information and communications by enforcing a set of rules, that enable only those who are supposed to receive the information, to access and handle it. It can protect crucial cloud data while without interfering with data transmission. Thus, a need to improve the current security standards and protocols used in cloud storage and make it much safer for the user, has been noticed. The goal of this research is to provide a reliable cloud service that secures data such that integrity is maintained and is safe from external threats.
Evaluation of Adversarial Attacks and Detection on Transfer Learning Model

In the field of transfer learning, the ability of models to make accurate predictions about future events can be compromised by targeted or untargeted adversarial attacks. Researchers have found that when images are subjected to such attacks, the feature construction process in every network produces noise, posing both challenges and opportunities for the study of convolutional neural networks (CNNs). To address this issue, the researchers developed a unique feature-map that describes the effects of adversarial attacks on individual object feature-maps. This approach was inspired by the observation of adversarial perturbations on image pixel attacks. By calculating the weight of each object's class activation map, a combined activation map is generated using a novel detection technique. The exploit feature-map developed in this study significantly advances the state-of-the-art in adversarial resistance. It was tested using several transfers learning models, including AlexNet, VGGNet19, and ResNet50, in both white-box and black-box assault scenarios. Moreover, the feature-map was shown to be effective in detecting assaults on ImageNet that use various techniques, such as the Fast Gradient Sign Method (FGSM), DeepFool, and Projected Gradient Descent (PGD).
A Blockchain Application for the Verification of Academic Information and Scalable Certification

The number of colleges that now offer instruction to college students and the majority of graduates each year has steadily expanded; the requirement to only guarantee degree certificates creates new business options. This study intends to develop money model equalization, in which the graduate and agency pay the same amount for the service because they are the carrier's primary stakeholders. Students need affordable, easy-to-find confirmation of their credentials, while employers need quick, reliable level verification when hiring new employees. As many students graduate each year, the issue of phony credentials is a major problem. Companies that hire much brisker pay a lot of money to get candidates' transcripts and academic certificates established. This issue will be resolved by a certificate of the blockchain era's usage. A single individual cannot alter the data on the blockchain without the consent of all other individuals involved in preserving the records. This tightens up the knowledge.
Cloud Security and Privacy Preservation - A Comprehensive Analysis

The on-demand availability of computer system resources is known as doud computing. The term “the doud” describes the software and databases that run on servers that may be accessed via the Internet. Data centers all throughout the world house cloud servers. Users and businesses can avoid managing physical servers or running software on their own computers by utilizing doud computing. Now a days cloud is used in everywhere many it companies are moved into cloud because less infrastructure is required to maintain the cloud, you need fewer staff and fewer resources to maintain it. Moving to the doud will help you reduce your operating costs over the long term and the cloud has more storage to store the any type of data but there are also some issues like data security and privacy. While the threats and data leaks were a top issue for Sony in the days and weeks that followed the attack, this was not the only problem. A lawsuit was brought by Sony employees who claimed that the theft of their personal data caused them to incur severe economic hardship. Hence, there is a dire need to protect the data from the hackers.
Person Re-Identification using Deep Learning

Person re-identification is a task which is used to search the particular person captured in one camera vision against a set of images captured in other camera visions. When aprobeimageisgiven,theperson re-identification model has to find theclosestmatches to the probe image from gallery images and arrange the gallery images according to the similarity score. This task has challenges because of variations in lighting, pose, background clutter, scale, occlusions and similar clothing. The primary goal of this research work is to enhance the performance of the person re-identification model in terms of identification rate at RankI and mean Average Precision (mAP). To achieve this goal and overcome the limitations, the research objectives were framed to develop an unsupervised person re-identification model to overcome the problem of limited training samples and reduce computation complexity; identify an enhanced feature engineering methodology that results in reduced feature vector size and elimination of unwanted background information from the given image; design and develop a deep neural network model to effectively perform with lesser training samples and also incorporate the spatial relationship between the features; test the models and evaluate the performance using benchmark datasets. In this research work, deep learning model with ResNet50 architecture has been proposed to increase the identification rate.
Studies on Anomaly Detection Techniques

Anomaly detection is well known as outlier detection, which issued to find things or objects that are deviated from the normal pattern orgeneral distribution of the dataset, anomaly detection can be detectedin credit card faults, intrusion network detection. And it helps to find the rare patterns, Decision trees are the foundation for isolation Forest (IF), which are constructed similarly to Random Forests. It is also an unsupervised model because there are no predefined labels in this instance An ensemble of binary decision trees is what isolationforests outlier detection is known as Isolation Tree, each tree in an isolation Forest (isolation Tree). The idea of Isolation Forests is that anomalies are data points that are rare a different. Isolation Forestalgorithm that isolates outliers in the data and finds anomalies this paper deals with the particular to find the dataset. One crucial use for anomaly identification is the detection of credit card fraud. Billion-dollar losses result from a sharp growth in digital frauds, thus numerous approaches for fraud detection have been developed and are being used in a variety of commercial industries The successof anomaly detection depends on choosing the right features, as irrelevant features can produce false results. In anomaly detection certain groups maybe unfairly targeted by algorithms for detecting abnormalities which leading to possible harm.
Surveillance of Forest Areas and Detection of Unusual Exposures using Deep Learning

In today's technologically advanced world, people can choose from a wide range of tools and appliances to assist them with their daily activities. However, there is a lack of understanding about how to use current technologies to protect forests and animals. The purpose of this article is to focus on this issue and offer a potential solution to one of the challenges confronting the planet's ecosystems and animal inhabitants. The invaders are considered as a significant factor. Trespassers' illegal activities, such as deforestation., trafficking, and poaching, endanger wildlife and forest ecosystems. This study intends to spot trespassers and notify them about the permission required to enter a restricted area of the forest. The proposed model utilizes the camera module that uses TensorFlow and RFID technology. Alerts can be generated by using GPS hardware and software.
Recommender System for E-Commerce Application based on Deep Collaborative Conjunctive Model

Nowadays, recommender systems play a crucial role in human lives. The recommendation process is involved in many items and many users' decision is based on this process. Collaborative filtering technique is one of the widely applied techniques in various types of recommender systems that uses the reviews of products and services. Word2Vec is adopted to extract information from the users' comments made on the items they bought. To group the items into definite sets, the clustering algorithm is used. A deep collaborative conjunctive recommender (DCCR), which is a hybrid approach. It consists of the combination of an autoencoder which extract the user and item features along with a multilayered perceptron which represents the interaction them. By applying together, the modern technique of deep learning and the traditional technique of collaborative filtering, the proposed system extract some set of the features like the features from users, explicit and implicit ratings to items along with user information. The proposed system uses real-world datasets to achieve effectiveness.
Hybrid Variable Selection Approach to Analyse High Dimensional Dataset

In light of the heterogeneous mode of collection and high dimensionality, most real-time datasets collected may contain irrelevant, correlated, noisy, and missing variables. When these high-dimensional datasets are used with machine learning, the resulting prediction about the dataset is inaccurate. A technique called feature selection is being usedin an effort to solve the above problem. FS examines the relevant variable from the entire dataset and selects the variable based on the evaluation strategy used. In nature, there are three types of FS techniques. The first one is the first approach, which is fast, but not effective. Likewise, the wrapper approach is an efficient one but the problem is taking a high computation time. This line of research aims to develop a new kind of FS known as hybrid variable selection in order to get around the drawbacks that are associated with the filter and wrapper approach. The purpose of HVS is to extract from the full dataset an efficient and relevant variable subset using the advantages offered by the filter and wrapper approaches. An empirical method is implemented utilizing the UCI-collected consumer dataset. The proposed methodology is implemented by using three different perspectives: first, the filter technique, next, the wrapper strategy, and finally, a hybrid approach. Further, variable subset captured from the three different approaches is modelled with the ML classifier separately. Here Naive Bayes, ML is applied. A variety of validity measures are used to forecast and compare experimental results. Final analysis results suggest the suggested HVS performs efficiently in comparison to the filter and wrapper technique.
A Survey of Forensic Applications using Digital Image Processing: Image Improvement Case Study

To gather reliable evidence and submit it to the court, forensic applications are used. Due to recent advancements in technology, many crimes now involve the modification of photos. Finding the original evidence and presenting it to the court has been simpler with the development of forensic software. The notion of digital image processing is combined with forensic applications to discover the original evidence with better evidence quality and to extract, analyse the most evidence feasible from the crime scenes. Analysing an image is absolutely necessary to determine the order of occurrences. Because image processing techniques lack a formal basis, forensic findings lose their legal value. To ensure that any image transformation meets high standards of safety and reliability, more rigorous methods should be applied to image processing applications. This paper focuses on how image enhancement techniques of digital image processing are used in the forensic applications.
Developing a YOLO based Object Detection Application using OpenCV

Computer Vision is a technology in which computers are trained using machine learning and deep learning algorithms to recognize and process objects from video/images in the same way that humans do. Since 2012, Convolutional Neural Network and its variations have experienced exceptional improvement, making object detection a crucial application of image processing. The main issue with CNN is that this consumes more time to instruct/train as it has to analyze two thousand regions per picture so for this task in real time it needs approximately 47 sec per picture. This article has employed the YOLO real-time object identification approach to train our machine learning model. YOLO is one of the best CNN representatives that defies new way for object recognition, which is simple and highly efficient. Unlike existing algorithms, YOLO looks at the picture entirely by predicting the bounding boxes and class probabilities for these boxes using neural networks. This results in a faster detection of the image than other algorithms.