Reviewerly/exHarmony huggingface dataset:
A hybrid GA-CS based Meta Heuristic Approach for Web Personalization

Internet data is increasing exponentially every second leading to increase in data on World Wide Web. As a result, it becomes a challenge to extract needed and useful information from it which could be presented to individual users. This is possible by learning the user's behavior of navigating the web. For this user generally uses any search engine to find the required information. There is need to record the keywords used by the user. These keywords will become the basis for the proposed approach. The proposed approach finds the relevant pages based on the keywords used by the user as well as the relevance of the documents which is found by important web pages. Important web pages are decided based on unique visitors, keywords frequency, active time spent by the user on a webpage, hubs and authority values. Finding most relevant content from huge source of the web is a NP complete problem. Metaheuristic Optimization algorithms have been successfully applied for this purpose. In this paper, a hybrid approach based on Genetic Algorithm (GA) followed by Cuckoo Search Algorithm (CSA) has been proposed to find the relevant web pages. Experimentally it has been shown that the proposed GA-CS approach select utmost relevant web pages in comparison to basic algorithms proposed in the literature.
Digital Verification of Optically Variable Ink Feature on Identity Cards

Digital identity verification is a new trend that allows clients to access services and products remotely, simplifies the interaction between client and institution, and saves their expenses. Digital verification suffers many security risks. The system must be able to reveal counterfeit identity cards. We propose a procedure to verify ID cards based on an analysis of the Optically Variable Ink (OVI) security feature. OVI is a type of ink whose colour varies with rotation or changes in the observer or light source position. The proposed method can detect an ID card and an OVI security feature on video frames. OVI visual characteristics are analysed, and we provide methods for their visual verification. We make several recommendations and suggestions for solving various partial problems related to visual OVI verification. Proposed methods can reveal counterfeit ID card in a video by analysing the OVI security feature with an accuracy of 82.82% but with some limitations.
Blockchain Based Verification of Educational and Professional Certificates

Educational Documents and Certificates are a proof of professional achievements for anyone. Without these documents, it is not possible to start your career and hence play a very important role in everyone's professional life. Till date, Colleges, Universities and various educational institutes issue paper based degrees and certificates which are prone to get damaged or may be lost. The other drawback is that this process of issuing of degrees and manual process of verification of those documents by third parties is a time taking and cumbersome process. The employers take a lot of time for verifying these paper based degrees and certificates before they give the job offer. Forgery of paper based documents is also easier and has led to many educational scams. This paper aims to give the solution to the problems mentioned above through Blockchain Implementation. Block chain technology is not only limited to crypto currencies but is also of great applications in various fields like health, supply chain management, finance, etc. It has disrupted the traditional education system. This paper highlights as to how the educational certificates can be verified using the Ethereum platform and smart contracts. The traditional paper certificates would be converted to digital certificates on student's request, their hash value would be calculated using cryptographic hash functions and stored on Blockchain. A unique certificate ID and transaction hash value would be generated which would then be used to verify the certificates through a common platform.
Edge Detection of Satellite Image for Water Body Identification using Marr -Hildreth Algorithm and comparing with Canny edge Detector Algorithm to Enhance Accuracy and Contrast

The aim of this research is to apply the Marr-Hildreth methodand comparing it to the Canny Edge Detector Algorithm to accurately forecast the water bodies from satellite photos. Materials And Methods Which is used to analyze a softwareundertaking by utilizing Image processing algorithms such as Marr-Hildreth and Canny edge detection. The power analysis here used 80% of the pretest power, and the sample number for the two groups is 20. The power analysis was held up with 80% and the sample amount for the two factions is 40. The Results Software effort analysis was done using Marr-Hildreth algorithm and Canny edge detector algorithm with the accuracy of 20 and 20 respectively. By using dominating samples t-tests,the statistically two-tailed significant difference in precision between the two techniques is 0.047 (p0.05).The Conclusion is thatMarr-Hildreth algorithm performs significantly better than the Canny edge detector algorithm.
BLEDNet: Bio-inspired lightweight neural network for edge detection

Edge detection is fundamental to advanced computer vision tasks. Although deep learning-based methods can generate excellent results, they tend to be computationally expensive. To address this issue, researchers have explored the development of lightweight CNNs. Physiological studies have shown that there are two visual pathways in the biological visual system. The second visual pathway can modulate the first visual pathway, aiding the visual cortex in extracting edges from images more quickly. The inferior temporal cortex can integrate the edge signals extracted by the visual pathways and generate edges. Inspired by this, we designed a bio-inspired lightweight edge detection CNN, which includes an Encoder and a Decoder. The Encoder consists of a bio-inspired first visual pathway network and a bio-inspired second visual pathway network. The Decoder consists of a compact receptive field enhanced network. Specifically, the bio-inspired first visual pathway network can calculate the rate of change of image pixels. Through adaptive antagonism, signals with different rates of change are mutually suppressed. Meanwhile, the bio-inspired second visual pathway network can guide the bio-inspired first visual pathway network to focus more on the salient parts of the image. In addition, the receptive field-enhanced network can perceive image features distributed along specific directions, helping the decoder generate clearer edges. Our proposed method achieves competitive results on the BSDS500 dataset with ODS=0.805 and on the NYUD-v2 dataset with ODS = 0.757, while also having lower computational costs than most existing methods. Therefore, our approach is suitable for devices with lower computing capabilities.
Domain Blacklist Efficacy for Phishing Web-page Detection Over an Extended Time Period

Phishing domains and web pages are the most common techniques cybercriminals use and a backbone of social engineering techniques causing tremendous losses globally. A domain blacklist is one of the oldest techniques used for phishing detection and has been superseded by more modern and more accurate techniques - in practice and research. Analysis which was conducted using the 10-year phishing data from 2013 to 2022, collected from PhishTank and PhishStats websites, was aimed to calculate and assess the domain blacklist efficacy in capturing phishing web pages during this time period and for the future. The complete process consisted of data collection and consolidation - merging the data from both sources, data cleansing, and blacklist creation, followed by the analysis to calculate and collate the figures and observations. The last step was to review the gathered results and summarize the conclusions. The results show that only a small portion of the phishing domains (≈22 %) re-occur and therefore are an eligible target of blacklist detection. Though, this is not a negligible number, especially when between ≈6% and ≈62% of records (from PhishTank) found in the blacklist were previously unclassified. A casual look at more recent trends doesn't provide a lot of supportive arguments in favor of blacklist as a future-proof technique either. However, the increased use of newly registered domains proves that cybercriminals must tap into the pool of new domains as current solutions utilizing blacklists effectively eliminate the reused domains.
Anomaly Detection Based on Multisource CNN with Handcraft features

Today's latest video surveillance technology has recently been used to monitor human interactions in automated processing systems.They play an important part in security matters. Numerous difficulties problems in separating violent from nonviolent behaviour. Supernatural activities Like crowded areas and camera view. In this article, we recommend a deep novel technique detect violence against this structural system Built on particular traits resulting from artisan techniques and through which violence is detected. These features are related to the representation of the image, the appearance of the image, and their motion speed, and are fed as input to a neural network (CNN), which transforms them into spatial, temporal feature, and feature streams, trained a network through this spatial stream to recognize contextual patterns in each frame of video. This temporal stream consisted of three consecutive frames for learning each dynamic pattern of violent actions. Differential optical flow measurement. Furthermore, we added a distinguishing characteristic with a new kinetic a graphic of energy to depict violence acts distinct from others in a spatio-temporal stream. The approach incorporates different facets of violent actions by combining the outcomes of these streams is also called violence and trained label. They include hockey, movie and VP datasets that are crowded and not. These experimental results demonstrate that the proposed in terms of precision and speed of processing, the violence detection technique outperformed the prior study's results.
Combined Signal Representations for Modulation Classification Using Deep Learning: Ambiguity Function, Constellation Diagram, and Eye Diagram

We exploit deep learning convolutional neural networks (CNN) based on joint image representation and propose an automatic modulation classification algorithm to classify the communication signals. The combined representations include a constellation diagram, an ambiguity function (AF), and an eye diagram. Experimentation results show that combining constellation and eye diagrams achieves superior classification performance compared to having these representations separately. Combining AF and an eye diagram results in improvement at low SNR.
Improved Accuracy for Exploring Text - Based Emotion Recognition in Social Media Conversation Generalized Linear Model Compared with Decision Tree

AIM: The objective of this work is text-based emotion recognition of social media conversation by comparing Novel Generalized linear model (GLM) and Decision Tree Algorithm. Materials and Methods: The Generalized linear model sample size=10 and Decision Tree sample size=10 the estimation has been done iteratively to obtain the accuracy of 98.01% Results: Generalized linear model(GLM) methodology produced better accuracy of 93.01% when correlated with the Decision Tree accuracy algorithm. In this experiment, p=0.828 (p<0.05 ) it is statistically insignificant parameter with a pretest power of 80% is used. Conclusion: For exploring text - based emotion recognition in social media conversation the Generalized Linear Model (GLM) performed significantly with better accuracy than Decision Tree algorithm.
Anomaly Detection for Vertical Plant Wall System using Novel Support Vector Machine in Comparison with Artificial Neural Network for improving accuracy

Aim: New Support Vector Machine and Artificial Neural Network are used to construct a plant wall system for indoor climate management. Materials and Methods: To detect indoor climatic changes in vertical plant wall systems, a Machine Learning Method that compares Artificial Neural Network and Innovative Support Vector Machine was created. The G Power calculator yielded 534 samples. 1068 samples and 80% Pretest Power. Results: Innovative Support vector machine had the highest accuracy in indoor changes at 68.42% and the lowest mean error at 58.58%. Groups are statistically insignificant. Conclusion: New Support Vector Machine technique outperforms Artificial Neural Network in indoor control.
A Format-sensitive BERT-based Approach to Resume Segmentation

In the early stages of a recruitment process, recruiters can spend a lot of time analyzing resumes (CVs) manually. This has led to the development of machine learning methods for the automated analysis of such documents, which currently besides text encompass rich formatting. Since rich formatting is not considered in any of the automated analysis stages and its possible impact has not been studied, this article investigates how to extract, transform, and apply grapholinguistic content. To this end, we propose a format sensitive and BERT-based framework for the essential first step in CV analysis, i.e. segmentation, relating the automatic description of graphic and textual markers, transformed in linguistic variables by means of fuzzification, to identify dependencies and semantic relationships with the recruiters' manual segmentation. Using a training dataset of 150 resumes, our approach achieved an F1-Score of 89% when segmenting 153 new samples.
Transformer-Based Dual-Modal Visual Target Tracking Using Visible Light and Thermal Infrared

Visual target tracking is an essential technology with numerous applications, including video surveillance, motion recognition, and autonomous driving. However, tracking accuracy can be affected in challenging scenarios, such as low-light conditions and occlusion, which make it difficult to extract effective tracking features from a single visible light image. On the other hand, infrared images can penetrate occlusion and are insensitive to light. However, tracking using only infrared images can be influenced by thermal crosstalk and lacks detailed texture information. Therefore, the RGB-T tracking method, which combines visible light and thermal infrared images, can significantly enhance the accuracy and robustness of object tracking in challenging scenarios, especially in autonomous driving. We propose a transformer-based fusion tracker that utilizes dual-modal information and combines test and training branches with target encoding for global reasoning across frames. The proposed method successfully achieves target tracking using visible light and thermal infrared images. The experimental results on the public benchmark show that the proposed tracker has higher overall performance and can meet the requirements for precise and robust tracking of vulnerable road users in autonomous driving tasks.
Human Emotion Recognition using Deep Learning

As the world is developing, so are the needs to understand human emotional behavior in order to decrease the amount of crimes, increase the business sales and various medical purposes. This research paper tests the various algorithms present in today’s field which were used for human emotion recognition by which they achieve certain precision of understanding and recognizing the emotion of the human more correctly. Such systems attract lots of attention in the present world as this system will help the business grow and understand the present mood of their customers. The proposed system of Human Emotion Recognition (HER) consists of three very simple parts , the initial phase is face detection, second is feature extraction and finally recognition and output. Basically the system uses 3 layers of CNN as well as a system of OpenCV algorithms. The dataset which we used is of Keras but the color detection can be done through the Kaegal color model. The AAM (Active Appearance Model) method is used to extract and save various physical features and distance among them as a dataset to understand how faces change. Through understanding the Euclidean distance the output of pictures can be understood. The current recognition rate of this proposed system can be around 91% to 94%.Artificial Neuro-Fuzzy Inference System (ANFIS) is used to further modify the model results.
Toward improvement of credit card fraud detection based on Machine learning Techniques

Financial organizations are now expanding their financial facilities and services with the use of various digital platforms. Furthermore, using credit cards along with the rapid progress of e-commerce are becoming a convenience and an important. The Credit Card is a payment method issued by the customer's credit card. The use of credit cards has many advantages, like: Credit cards will improve our life. We would allow customers to buy credit arbitrary without carrying cash in an arbitrary way without the need to provide an easy method of payment for online purchases, either through the telephone, via ATM machines, etc. The main objectives of this research are Analyse and predict all important features or parameters of credit card fraud and performance investigations are carried out on the machine learning algorithms which helps in ascertain and analyse and fraudulent activities also specific objective of the performance investigations is to determine the algorithms efficiency in terms of precision, recall and Fmeasure rate, then Evaluation and comparison of predicted results with other available techniques
Analysis of neural networks trained with evolutionary algorithms for the classification of breast cancer histological images

Biopsy tests used in the identification and confirmation of breast cancer are time-consuming and complex. Thus, neural networks can be applied to aid specialists with a prone to be in local minima, which can be avoided using evolutionary algorithms. In that way, this work analyzed the methods of genetic algorithm, differential evolution, differential evolution with islands and migration, particle swarm optimization, and adaptive particle swarm optimization in the training of neural networks for the classification of histological images stained by hematoxylin-eosin. The differential evolution with islands and migration and particle swarm optimization presented the most promising results, with the first one reaching a maximum AUC of 0.70 for the color features, and the last one with a maximum AUC of 0.71 for the texture attributes, on the evaluated dataset. Through this proposal, we have an important contribution to breast cancer histological image classification that also allows the development of new studies in the future.
Design and Implementation of English Grammar Error Correction System Based on Deep Learning

At present, our English error correction algorithm is slightly general, the error correction ability is also very limited, and its accuracy rate is also low, so it is very necessary to improve. This article will further explore the problem of syntax error correction, and the corresponding algorithm model will also be proposed. Based on deep learning technology to improve the error correction rate of English grammar, put forward the corresponding solution, put forward the Sep2seq-based English grammar error correction system model, and carry out a series of rectifications to improve its efficiency and accuracy. The basic architecture of TensorFLOW is used to implement the model, and the success of the error correction algorithm model is proved, which brings great improvement and progress to the success of error correction.
Performing Effective Generative Learning from a Single Image Only

Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation.
A Survey on the security of IoT communications

One of the technologies that have gained the most traction over the past ten years in a variety of applications is the Internet of Things (IoT). For communication, processing, computing, and monitoring of various systems, the smart items are connected via wired or wireless technology. The items are diverse and have minimal memory and processing power. Implementing IoT systems raises security and privacy concerns because IoT devices are not compatible with traditional security protocols. The authors of this study began by providing an overview of IoT technology and subsequently explaining the means of achieving secure communication. This study examines current protocols and some Lightweight Cryptographic protocols to deal with some of the major problems of IoT. Protocols like Elliptic Curve Cryptography, Simon, Speck, and AES have been discussed along with others.
Pilot implementation MitraTB application to strengthening district based public private mix tuberculosis control in Purwakarta district, Indonesia: A mixed-method study

District-based public private mix (DPPM) tuberculosis in Purwakarta district was strengthened by the MitraTB application. This research is aimed to explore perception of user about MitraTB application and measure their perception of this application in dimensions; design, usefulness, ease of use, and acceptance. This study was exploratory sequential mixed methods research. A qualitative study was first conducted in order to gain an in-depth understanding about user's perception of MitraTB application through in-depth interviews. Data were analyzed through coding and categorizing. Based on qualitative finding, a questionnaire was developed and used in the following quantitative study. A cross sectional study was then conducted in quantitative phase. Data were analyzed using Rasch modeling. The design of the MitraTB application looks simple and attractive to users. This application is useful to make it easier for private practitioners to report TB cases and it is easy to use. Respondents can accept the MitraTB application well. Most respondents have good perception about MitraTB application in dimensions; design (56.25%), usefulness (69.79%), ease of use (55.20%), and acceptance (73.96%). MitraTB application has a good design feature, useful, easy to use, and acceptable. This application facilitates the private sector to be involved in the TB program by reporting TB cases. Follow-up and local regulations are required for the continued use of this application.