Reviewerly/exHarmony huggingface dataset:
Systematic Literature Review

Machine learning (ML) applications hold significant promise for innovation within healthcare; however, their full potential has not yet been realised, with limited reports of their clinical and cost benefits in clinical practice. This is due to complex clinical, ethical, and legal questions arising from the lack of understanding about how some ML models operate and come to make decisions. eXplainable AI (XAI) is an approach to help address this problem and make ML models understandable. This chapter reports on a systematic literature review investigating the use of XAI in healthcare within the last six years. Three research questions identified as issues in the literature were examined around how bias was dealt with, which XAI techniques were used, and how the applications were evaluated. Findings show that other than class imbalance and missing values, no other types of bias were accounted for in the shortlisted papers. There were no evaluations of the explainability outputs with clinicians and none of the shortlisted papers used an interventional study or RCT.
Graphical Password Authentication System for Web and Mobile Applications in JavaScript

RESTful API-based web and mobile applications are cross-platform and can be accessed from anyplace or anytime resulting in a smoother and easier user experience. This ecosystem creates a familiar environment for business applications, especially for small businesses. However, an increasing number of such applications creates opportunities to protect passwords from various attacks. Humans choose weak textual passwords due to easiness, which may lead to the most frangible connections in the chain of authentication. The graphical password offers a better approach of authentication for web and mobile applications in the emerging business world since it uses images as input instead of alphanumeric. It also makes it difficult for the attackers to crack. This study devises a system that allows the user or client application to authenticate by tapping the right cues over a series of images that the user selects while registering in the system. The system is implemented as a web service using JavaScript technology by ReactJS on client applications and NodeJS on server end.
Governance of Digital Business in Industry 4.0

The importance of digital technologies for social and economic developments and a growing focus on data collection and privacy concerns have made the internet a salient and visible issue in global politics. The internet has transformed how we do business and created new opportunities for cross-border trade and investment, enabling small businesses worldwide to connect with customers and suppliers in the global market. In this chapter, the author conceptualizes the digital media business in India, which utilizes the internet to create the biggest platform for commerce worldwide. The author discusses regulations that are required in the cyber law and the safe harbour rule of cyber law, which is constantly harnessed by the digital media and e-commerce companies that act as intermediaries to protect them from the actions of the end-user on their platform.
Exploring neural question generation for formal pragmatics: Data set and model evaluation

We provide the first openly-available German QUestion-Answer Congruence Corpus (QUACC), designed for the task of sentence-based question generation with question-answer congruence. Based on this corpus, we establish suitable baselines for question generation, comparing systems of very different nature. Question generation is an interesting challenge in particular for current neural network architectures given that it combines aspects of language meaning and forms in complex ways. The systems have to generate question phrases appropriately linking to the meaning of the envisaged answer phrases, and they have to learn to generate well-formed questions using the source. We show that our QUACC corpus is well-suited to investigate the performance of various neural models and gain insights about the specific error sources.
Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

In data mining and machine learning, feature selection is a critical part of the process of selecting the optimal subset of features based on the target data.There are 2 n potential feature subsets for every n features in a dataset, making it difficult to pick the best set of features using standard approaches.Consequently, in this research, a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm (ASSOA) has been proposed.When using metaheuristics to pick features, it is common for the selection of features to vary across runs, which can lead to instability.Because of this, we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization process.For the selection of the best subset of features, we recommend using the binary ASSOA search strategy we developed before.According to the suggested approach, the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California, Irvine (UCI) repository was used to test the proposed method's performance vs. eleven other state-of-the-art approaches, including binary grey wolf optimization (bGWO), binary hybrid grey wolf and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hybrid GWO and genetic algorithm 4028 CMC, 2023, vol.74, no.2 (bGWO-GA), binary firefly algorithm (bFA), and bGA methods.Experimental results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.
Implications of Onshore Development on Global Software Engineering

Recently software industry has paid significant attention to customizing software products across distributed boundaries.Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process.Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries.This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software.This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays and misunderstandings between the clients and the team involved.This model depends on moving the development process to the client's location.Three empirical studies were conducted to evaluate the proposed model to measure its productivity, time performance, and cost reduction.The proposed model has been compared with two other models: the basic model (BM), which allocates the decision-making process and the development process for teams at the vendor's location, and the local decision-making model (LDec), which assigns the decision-making process for team at the client's location.The results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity, time performance, and cost reduction.The productivity of the proposed model improved by 39% and 10% more than the basic model and the local decision-making model, respectively.In addition, the time performance of the proposed model became faster by 49% and 20.8% than the basic model and the local decision-making model, respectively.Also, it reduced the total cost of the development process by 31% in terms of the salaries of all persons involved in requirements collecting, decision-making, and development.
Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents.The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects.This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area.In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet.Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area.Primarily there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling, wherein two-step models incorporated by Region proposals followed by object detection.We in this research scrutinize one-step State of the art (SOTA) model CenteNet as proposed recently with three different feature extractor ResNet-50, HourGlass-104 and ResNet-101 one by one.We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300 * which depicts performance improvement by 20.2% mAP and SMOKE by with 13.2% mAP respectively.Effectiveness of CenterNet can be justified through the huge improved performance.The performance of our model is evaluated on KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) benchmark dataset with different backbones such as ResNet-50 gives 62.3% mAP ResNet-101 82.5% mAP, last but not the least HourGlass-104 outperforms with 98.2% mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors.We also compare our model with other SOTA techniques.
Reducing Dataset Specificity for Deepfakes Using Ensemble Learning

The emergence of deep fake videos in recent years has made image falsification a real danger.A person's face and emotions are deep-faked in a video or speech and are substituted with a different face or voice employing deep learning to analyze speech or emotional content.Because of how clever these videos are frequently, Manipulation is challenging to spot.Social media are the most frequent and dangerous targets since they are weak outlets that are open to extortion or slander a human.In earlier times, it was not so easy to alter the videos, which required expertise in the domain and time.Nowadays, the generation of fake videos has become easier and with a high level of realism in the video.Deepfakes are forgeries and altered visual data that appear in still photos or video footage.Numerous automatic identification systems have been developed to solve this issue, however they are constrained to certain datasets and perform poorly when applied to different datasets.This study aims to develop an ensemble learning model utilizing a convolutional neural network (CNN) to handle deepfakes or Face2Face.We employed ensemble learning, a technique combining many classifiers to achieve higher prediction performance than a single classifier, boosting the model's accuracy.The performance of the generated model is evaluated on Face Forensics.This work is about building a new powerful model for automatically identifying deep fake videos with the DeepFake-Detection-Challenges (DFDC) dataset.We test our model using the DFDC, one of the most difficult datasets and get an accuracy of 96%.
Optimal Deep Transfer Learning Based Colorectal Cancer Detection and Classification Model

Colorectal carcinoma (CRC) is one such dispersed cancer globally and also prominent one in causing cancer-based death. Conventionally, pathologists execute CRC diagnosis through visible scrutinizing under the microscope the resected tissue samples, stained and fixed through Haematoxylin and Eosin (H&E). The advancement of graphical processing systems has resulted in high potentiality for deep learning (DL) techniques in interpretating visual anatomy from high resolution medical images. This study develops a slime mould algorithm with deep transfer learning enabled colorectal cancer detection and classification (SMADTL-CCDC) algorithm. The presented SMADTL-CCDC technique intends to appropriately recognize the occurrence of colorectal cancer. To accomplish this, the SMADTL-CCDC model initially undergoes pre-processing to improve the input image quality. In addition, a dense-EfficientNet technique was employed to extract feature vectors from the pre-processed images. Moreover, SMA with Discrete Hopfield neural network (DHNN) method was applied for the recognition and classification of colorectal cancer. The utilization of SMA assists in appropriately selecting the parameters involved in the DHNN approach. A wide range of experiments was implemented on benchmark datasets to assess the classification performance. A comprehensive comparative study highlighted the better performance of the SMADTL-CDC model over the recent approaches.
Hybrid Models for Breast Cancer Detection via Transfer Learning Technique

Currently, breast cancer has been a major cause of deaths in women worldwide and the World Health Organization (WHO) has confirmed this.The severity of this disease can be minimized to the large extend, if it is diagnosed properly at an early stage of the disease.Therefore, the proper treatment of a patient having cancer can be processed in better way, if it can be diagnosed properly as early as possible using the better algorithms.Moreover, it has been currently observed that the deep neural networks have delivered remarkable performance for detecting cancer in histopathological images of breast tissues.To address the above said issues, this paper presents a hybrid model using the transfer learning to study the histopathological images, which help in detection and rectification of the disease at a low cost.Extensive dataset experiments were carried out to validate the suggested hybrid model in this paper.The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images.
Identification and Visualization of Spatial and Temporal Trends in Textile Industry

The research volume increases at the study rate, causing massive text corpora.Due to these enormous text corpora, we are drowning in data and starving for information.Therefore, recent research employed different text mining approaches to extract information from this text corpus.These proposed approaches extract meaningful and precise phrases that effectively describe the text's information.These extracted phrases are commonly termed keyphrases.Further, these key phrases are employed to determine the different fields of study trends.Moreover, these key phrases can also be used to determine the spatiotemporal trends in the various research fields.In this research, the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis.Therefore, an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period.This study collected a diversified dataset of textile research from 2011-2019 and different countries to determine the research trend.This data was collected from various open access journals.Further, this research determined the spatiotemporal trends using quality phrase mining.This research also focused on finding the research collaboration of different countries in a particular research subject.The research collaborations of other countries' researchers show the impact on import and export of those countries.The visualization approach is also incorporated to understand the results better.
Improved Video Steganography with Dual Cover Medium, DNA and燙omplex燜rames

The most valuable resource on the planet is no longer oil, but data.The transmission of this data securely over the internet is another challenge that comes with its ever-increasing value.In order to transmit sensitive information securely, researchers are combining robust cryptography and steganographic approaches.The objective of this research is to introduce a more secure method of video steganography by using Deoxyribonucleic acid (DNA) for embedding encrypted data and an intelligent frame selection algorithm to improve video imperceptibility.In the previous approach, DNA was used only for frame selection.If this DNA is compromised, then our frames with the hidden and unencrypted data will be exposed.Moreover the frame selected in this way were random frames, and no consideration was made to the contents of frames.Hiding data in this way introduces visible artifacts in video.In the proposed approach rather than using DNA for frame selection we have created a fake DNA out of our data and then embedded it in a video file on intelligently selected frames called the complex frames.Using chaotic maps and linear congruential generators, a unique pixel set is selected each time only from the identified complex frames, and encrypted data is embedded in these random locations.Experimental results demonstrate that the proposed technique shows minimum degradation of the stenographic video hence reducing the very first chances of visual surveillance.Further, the selection of complex frames for embedding and creation of a fake DNA as proposed in this research have higher peak signal-to-noise ratio (PSNR) and reduced mean squared error (MSE) values that indicate improved results.The proposed methodology has been implemented in Matlab.
Two Layer Symmetric Cryptography Algorithm for Protecting Data from燗ttacks

Many organizations have insisted on protecting the cloud server from the outside, although the risks of attacking the cloud server are mostly from the inside.There are many algorithms designed to protect the cloud server from attacks that have been able to protect the cloud server attacks.Still, the attackers have designed even better mechanisms to break these security algorithms.Cloud cryptography is the best data protection algorithm that exchanges data between authentic users.In this article, one symmetric cryptography algorithm will be designed to secure cloud server data, used to send and receive cloud server data securely.A double encryption algorithm will be implemented to send data in a secure format.First, the XOR function will be applied to plain text, and then salt technique will be used.Finally, a reversing mechanism will be implemented on that data to provide more data security.To decrypt data, the cipher text will be reversed, salt will be removed, and XOR will be implemented.At the end of the paper, the proposed algorithm will be compared with other algorithms, and it will conclude how much better the existing algorithm is than other algorithms.
Translation of English Language into Urdu Language Using LSTM Model

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation.In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion.Machine translation has achieved this goal with encouraging results.When decoding the source text into the target language, the translator checks all the characteristics of the text.To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to automate the work.In this research work, a neural machine translation approach is employed to translate English text into Urdu.Long Short Term Short Model (LSTM) Encoder Decoder is used to translate English to Urdu.The various steps required to perform translation tasks include preprocessing, tokenization, grammar and sentence structure analysis, word embeddings, training data preparation, encoder-decoder models, and output text generation.The results show that the model used in the research work shows better performance in translation.The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten (10).
An Improved Text-Based and Image-Based CAPTCHA Based on Solving and燫esponse Time

CAPTCHA is an acronym that stands for Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA), it is a good example of an authentication system that can be used to determine the true identity of any user.It serves as a security measure to prevent an attack caused by web bots (automatic programs) during an online transaction.It can come as text-based or image-based depending on the project and the programmer.The usability and robustness, as well as level of security, provided each of the varies and call for the development of an improved system.Hence, this paper studied and improved two different CAPTCHA systems (the text-based CAPTCHA and image-based CAPTCHA).The textbased and image-based CAPTCHA were designed using JavaScript.Response time and solving time are the two metrics used to determine the effectiveness and efficiency of the two CAPTCHA systems.The inclusion of response time and solving time improved the shortfall of the usability and robustness of the existing system.The developed system was tested using 200 students from the Federal College of Animal Health and Production Technology.The results of each of the participants, for the two CAPTCHAs, were extracted from the database and subjected to analysis using SPSS.The result shows that textbased CAPTCHA has the lowest average solving time (21.3333s) with a 47.8% success rate while image-based CAPTCHA has the highest average solving time was 23.5138 s with a 52.8% success rate.The average response time for the image-based CAPTCHA was 2.1855 s with a 37.9% success rate lower than the text-based CAPTCHA response time (3.5561 s) with a 62.1% success rate.This indicates that the text-based CAPTCHA is more effective in terms of usability tests while image-based CAPTCHA is more efficient in terms of system responsiveness and recommended for potential users.