Reviewerly/exHarmony huggingface dataset:
Assistive Application for the Visually Impaired using Machine Learning and Image Processing

The task of interpreting visual information poses a difficulty for artificial intelligence due to the intricate and diverse characteristics of visual data. Visual data can be disrupted and deficient, which complicates the process of machines attempting to precisely comprehend and decipher the meaning of an image. In this paper, a new method for image captioning for people who are blind is suggested. This method involves using a CNN-LSTM architecture, where a CNN is utilized to extract visual features from the image, and an LSTM generates a text-based description based on these features. A vast dataset of images and their corresponding captions are used to train the suggested model, and its effectiveness is assessed using the BLEU metric. Our model is validated using the benchmark dataset Flickr8K. The outcomes of the experiment demonstrate that the suggested technique has the capability to produce relevant and precise descriptions, which can help visually impaired people to access visual content. This method has the potential to fill the gap and provide a solution to the challenge of accessing visual media by the visually impaired.
FSD: A novel forged document dataset and baseline

Forgery detection in multimedia is an emerging research area in the field of digital content security. The current research focuses on finding several solutions to digital image forgery detection but a small number of works have been reported for scanned document forgery detection. The reason would be the unavailability of a forged scanned document (FSD) dataset. A scanned document can be manipulated in different ways: deleting a region, copy paste a region of the document either in the same document or in another document. Detection of these manipulations becomes arduous when a post-processing operation is performed on the manipulated region. To advance research in this field, we created a large dataset for the FSD. To generate such a dataset, we collected scanned documents from the FUNSD dataset (Publicly Available). The resolution of each sample of the dataset is of size 755x1000. The samples of the dataset are forged using different post-processing operations. The dataset comprises 6656 instances of manipulated scanned documents with their ground truth masks. To build the baseline for forged scanned document detection, we evaluated state-of-the-art object segmentation algorithms on the FSD dataset. Experiments demonstrate that the FSD well represents forged scanned documents and is quite challenging.
Blockchain Facilitated Protection and Safety in Cyberphysical Systems from Defective Sensors

The cyber physical systems (CPS) are linked to a range of sensors, the data from which is used for decision making and real-time operation. With the introduction of Industry 5.0, which enables human-focused processing, protection has become a vital aspect for real-time cyber physical systems, since a flaw in its operation might have disastrous implications. Thus the data collected from malfunctioning or malicious nodes might stymie decision-making and jeopardise the overall system environment. As a result, it is critical to assure the system’s security and dependability in real time. Furthermore, maintaining track of the data records on which real-time systems operate is critical, since such records will not only help in decision making. Along with, it also aids in the detection of malicious sensor nodes. We propose a blockchain-based security and dependability method to do this. All sensors conform to RAFT consensus in this process, and only transactions accepted by all sensor nodes are kept in the Hyperledger Fabric (HLF) ledger and Vault. This is subsequently delivered to all of the peer nodes that are registered in the private network channel for a certain organisation. This helps discover defective sensor nodes and separate their data, improving system security and dependability. In comparison to the previous study, our technique has a high throughput and low latency in sensor fault mitigation. Additionally, implementing blockchain provides increased security, dependability, and privacy to the sensor cluster and organisational channels.
Comparative Analysis of Transfer Learning Strategies for Automated Identification of Bone Marrow Cell Morphologies

The gradual decrease in the average lifespan of mankind is considered to be caused by various diseases, along with hematological disorders are prominent. Diagnosis of this hematological disorder requires an understanding of the morphology of bone marrow cells. This study introduces a computer vision-aided solution in order to perform an automated evaluation of bone marrow smears from the examiner’s skill and experience. In this regard, we employ the publicly available dataset of 170,000 expert-annotated cell images from the bone marrow smears with twenty-one separate groups. A comparative study of the models developed from our dataset has been performed using eleven customized deep-transfer learning algorithms. The EfficientNetB3 outperforms other existing models. We have achieved the maximum accuracy of 87.2% by EfficientNetB3 compared with other deep learning models. Similarly, the recall in EfficientNetB3 was 87.22%, the precision was 87.11%, and the F1 score was 87.15%. Conversely, DenseNet201 offers the second-most effectiveness, with an accuracy of 86.32%.
Sequential Computational Time-Released Encryption Technique Using Variable Time Delay

Time-released encryption (TRE) is a cryptographic technique that enables encrypted data to be automatically decrypted or made accessible after a certain period of time. The receiver is usually made known the decryption time or release time of the encrypted text. TRE uses a time delay between encryption and decryption to provide an additional layer of security. TRE produces a higher level of security when combined with other cryptographic algorithms. In this paper, the process of encryption and decryption of plain text is performed with the help of the Sequential computation technique of the TRE algorithm along with sender-induced time delay. An additional layer of security is also added by combining the TRE algorithm with two traditional algorithms i.e; RSA and RSA-OAEP to provide security for the data. Both methods are compared and the results are shown accordingly.
A Comprehensive Review of Innovations in Artificial Intelligence and Enhancing Machine Learning Model Precision and Efficiency

This paper presents a comprehensive review of recent innovations in artificial intelligence and algorithm development aimed at enhancing machine learning precision and efficiency. The review covers various techniques, including deep learning, reinforcement learning, and transfer learning, and their applications in different domains. The paper also discusses recent advancements in hardware and software infrastructure that have improved the efficiency of machine learning algorithms. The goal of this paper is to provide a comprehensive overview of the current state of the art in machine learning and to identify promising directions for future research. Furthermore, the paper highlights the challenges and limitations of existing techniques and provides insights into possible solutions. It also discusses the ethical implications of using machine learning in various domains, such as healthcare, finance, and social media. The review identifies the need for more transparent and explainable algorithms and emphasizes the importance of addressing bias and fairness issues in machine learning.
Unraveling Women’s Involvement in the Digital Realm: An Empirical Investigation

A virtual world in which communication is done through the electronic medium using the computer. This world allows the user to gain knowledge in the form of information. Even though it has a lot of advantages, there are enormous issues when an individual exists in cyberspace. At hand are several challenges to be overcome by individuals to protectively survive cyberspace. Such as various attacks, financial risks, online crimes, and more. In cyberspace, the targeted audience is womanhood of all eons. Educating and promoting awareness about the risk in cyberspace for women in society is the need of the hour. Each individual is facing risk while they are in a digital world. Stakeholders are not given alertness of the threat and its consequences. The paper analyzes the risk and consequences of women’s society, as most victims are from that environment. In this, different risks faced and the consequences affected by women’s civilization, are discussed. Also remedial measures are taken and should be taken are also deliberated. Supporting this, an online survey is taken from various groups of common people to know the status of women’s civilization in the current era.
Zero Trust Architecture Using Hyperledger Fabric

Traditional security measures focused on network perimeter protection are no longer sufficient to defend against cyber threats in the quickly changing digital environment of today. A zero-trust architecture (ZTA), which assumes that every node inside a network is untrusted, has been offered as a solution to this problem. This study suggests leveraging the permissioned blockchain technology Hyperledger to implement ZTA. The approach makes use of the transparent and immutable Hyperledger ledger to protect network transactions and improve network security. Simulations and tests are used to evaluate the proposed ZTA, and the findings reveal enhanced security and efficiency when compared to conventional security measures. Based on ZTA and Hyperledger technologies, our research aids in the creation of reliable, scalable, and effective network security solutions.
Advancing Healthcare: Unleashing the Potential of Cryptography, Blockchain, and Machine Learning

Blockchain technology, a decentralized database, has emerged as a groundbreaking solution for various industries, including healthcare. In the field of healthcare, challenges such as fragmented medical histories and inaccurate data pose significant risks to patient care. Blockchain technology has potential to revolutionize the efficiency and quality of healthcare services. Managing the vast amount of healthcare data securely and ensuring data integrity are paramount concerns. Blockchain technology offers robust authentication, fine-grained authorization, and secure access to individual patient records. Furthermore, it has the potential to substantially reduce costs, optimize resource allocation, and propel healthcare advancements through cutting-edge cryptographic techniques. This paper presents a novel architecture that ingeniously combines blockchain technology and machine learning to enhance healthcare efficiency. The proposed architecture boldly addresses challenges related to data integration, seamless interoperability, and advanced predictive analytics, with the ultimate goal of improving patient outcomes and enabling more informed decision-making. By seamlessly integrating powerful machine learning algorithms into the blockchain framework, intelligent data analysis and sophisticated predictive modeling become feasible, empowering personalized treatment plans and proactive healthcare interventions. This paper provides a comprehensive exposition of the proposed architecture, underscoring its potentially transformative impact on healthcare delivery and outcomes.
Code Smell Detection Using a Weighted Cockroach Swarm Optimization Algorithm

Code smells are a common problem in software development, leading to decreased maintainability, poor performance, and reduced code quality. Addressing code smells is crucial in ensuring that software is maintainable, scalable, and efficient. To detect them, different algorithms are used, including evolutionary algorithms such as Hybrid Particle Swarm Optimization (HPSOM), Genetic Algorithm (GA), Parallel Evolutionary Algorithm (PEA), and the weighted cockroach swarm optimization algorithm (WCSO). WCSO is especially effective for detecting code smells, as it generates rules that combine metrics and thresholds to detect code smells automatically. The main focus of this paper is detecting code smells using WCSO. We empirically evaluated WCSO with other evolutionary algorithms for detecting code smells such as blob, spaghetticode, dataclass, featureenvy, and functionaldecomposition. Our results prove that WCSO outperforms other algorithms, including GA, HPSOM, and PEA. Furthermore, we found that WCSO performs better on a range of open-source applications, including JfreeChart, GanttProject, Apache Ant 7.0, Apache Ant 5.2, Nutch, Log4J, Lucene, Xerces-J, and Rhino. By detecting these code smells, we can take steps to address them, such as refactoring code to make it more modular or reducing code complexity to improve maintainability and performance. In conclusion, this paper establishes the effectiveness of WCSO for detecting code smells in software development and making the software more reliable and efficient.
Digital Verification And Face Recognition

Due to the information technology industry's rapid growth and easy access to affordable, cutting-edge office equipment on the market, the fake of important papers has been an issue in recent years. As a result, authentication and verification processes are very necessary for a variety of important papers, such as those used in the banking sector, the government, and transaction. However, a lot of challenging and tedious steps make document verification very challenging and complicated, which is what motivated us to conduct this study. In this work, we describe a decentralized internet-based program for between peers cloud storage for digital document verification of documents built on Blockchain. By making it simpler to examine, see, and audit the verification process, this application seeks to improve it. The suggested approach includes several cloud-based security measures, digital signatures, peer networking, and proof of work, which makes it simpler and quicker for any organization or authority to validate any uploaded documents with just a single click. Our proposed model completely satisfies all requirements for a digital document verification system by resolving the drawbacks and difficulties in the existing document verification techniques.
Solar Power Forecasting using Stacking Ensemble Models with Bayesian Meta-Learning

Predicting solar power generation is vital for better uses of renewable energy farms. This paper proposes averaging and stacking ensemble models for predicting solar power generation. The machine learning (ML) models include Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest (RF), multilayer perceptron (MLP), support vector machine regression (SVR), and extreme gradient boosting (XGB). In order to improve the performance of the models, two ensemble methods, namely averaging ensemble with linear weighted average and stacking ensemble models, are developed. The models are validated on real-time series of historical datasets from the Asian Institute of Technology (AIT), Thailand. The analysis shows that the stacking ensemble model with Bayesian as meta-learner achieves better prediction accuracy than all tested models.
Analysis of Blockchain-Based Techniques for the Mitigation of DDoS Attacks in IoT Devices

Despite the pervasiveness of the Internet of Things (IoT) as a result of its widespread adoption across multiple industries including healthcare, agriculture etc., these devices continue to suffer from significant shortcomings. Their structural limitations coupled with the lack of security implementations make IoT devices highly vulnerable, especially to Distributed Denial of Service (DDoS) attacks, thus serving as an ideal tool for botnet manifestation. Despite the development of increasingly efficient defense mechanisms, there is a continual rise in the frequency and scale of such attacks which continues to outperform these countermeasures. The inherent characteristics of Blockchain, combined with the significant potential of smart contracts to be used for whitelisting, blacklisting, identity management, and traffic monitoring mechanisms, make this technology stand out as a promising candidate for developing mitigation strategies against different types of DDoS attacks. This paper presents a thorough analysis of IoT devices, their architecture, and their limitations. It aims to comprehensively examine DDoS mitigation solutions in three areas: prevention of device & network compromise, detection-based mechanisms, and reaction-based solutions while studying the integration of Blockchain, with other technologies like SDN, Fog Computing, Machine Learning etc.
Online Aggression Identification Using Ensembled Transformer-based Language Models

The ubiquitous online aggressive content is rapidly increasing with the increase in the popularity of Social media platforms. Social media has been used by a few to vent out and demean an individual or community and can disturb society’s social harmony. Therefore, restraining aggressive online content has become an urgent need. Past studies on aggressive behaviour identification have done an excellent job, but an absolute combat method is yet to be designed. In this paper, an XGBoost ensembling of large fine-tuned Transformer-based Language Models viz., XLNet, RoBERTa, ALBERT, and BERT are proposed to detect Online Aggression. The performance of the proposed approach is evaluated on various metrics such as Accuracy, Micro, Macro, and Weighted aggregation of Precision, Recall, and F1-Score. The proposed approach gave better results (like 13.18% and 7.47% increase in performance concerning Weighted F1-Score and Accuracy, respectively) compared to varied individual and ensemble Transformer-based and Deep Learning models.
Evaluating the Impact of Local Data Imbalance on Federated Learning Performance for IoT Anomaly Detection

The Internet of Things (IoT) has experienced tremendous surge in the number of connected devices, resulting in an enormous amount of data being generated. Anomaly detection in such scenarios is crucial for ensuring the security and reliability of IoT systems. Federated Learning (FL) has been proposed as a viable solution for anomaly detection in IoT, allowing distributed devices to collaboratively learn a global model while keeping their data private. In this paper, we investigate the impact of local data imbalance on the performance of FL-based anomaly detection models in IoT scenarios. In general, data imbalance in FL can manifest either globally, where the collective data of clients is imbalanced, or locally, where the data of individual clients is imbalanced. To specifically examine the impact of the later, we use the X-IIoTID dataset and create three different data distributions, each consisting of 10 client devices. The global data imbalance is kept similar across all distributions but the level of local data imbalance is varied. We then apply FL using a federated averaging algorithm to train an anomaly detection model on each distribution. Our study reveals that despite having the same global data imbalance, an increase in the level of local data imbalance results in a decline in the FL-based model’s performance especially when the client devices train on different sets of data classes. Our findings suggest that addressing local data imbalance is crucial for achieving accurate and robust anomaly detection in IoT using FL.
Exploring Data Distributions using Box and Whisker Plot Analysis

Data analysis plays a crucial role in decision-making processes in various industries. One common approach to analyzing data distributions is through the use of box and whisker plots. In this paper, we explore the use of box and whisker plot analysis as a tool for exploring data distributions. We discuss how this analysis can be used to compare and draw crucial information, determine the spread of data, and visualize the shape of the data distribution. Additionally, we describe a Windows application tool developed in VB.NET. that allows users to explore the data distributions using box and whisker plot. The application includes features such as the ability to import a CSV file, display its contents, select specific columns for analysis, scale down data values, generate box and whisker plots and draw important conclusions. We have applied the application tool in three varied datasets - salary, marks and movies and were able to draw some meaningful conclusions from the data distributions in the datasets.
Comparative Result Analysis of Underwater Image Enhancement methods

Image enhancement is a method of increasing an image's quality by enhancing its characteristics. This research presents a comparative analysis of several enhancing methods for these underwater images. Since there are poor visibility situations, the underwater image has poor contrast and image quality, making object identification difficult. The analysis of gathered underwater images is required since the quality of these images has been impacted and some of the images constitute few major challenges in comparison to images taken in better surroundings. A significant amount of noise occurs in underwater images because of the poor contrast, lack of visibility conditions, distraction of natural light, uneven lighting and minimal color variations, and blurs effect. For all of these reasons, various methods are available to improve these underwater images, and various different approaches are also present in the literature for analyzing, processing and enhancing.
A Keyword Based Searching and Sharing Scheme on the Encrypted Cloud Data

Security is one of the major concerns in the cloud server for users when local storage data is outsourced to the cloud. It gives rise to security concerns involving data confidentiality even after the deletion of data from cloud storage. Data must be encrypted before uploading to the public cloud to ensure data security and protection from unwanted and unauthorized parties so they cannot get access to private data. Sharing encrypted data with other users, specifically with a searching facility in the encrypted data, is challenging. Searching over encrypted data is essential to avoid downloading large data to the local storage and searching in the downloaded data. A cloud storage service can search over encrypted data on behalf of the data users using searchable encryption without knowing the underlying plain texts. While many searchable encryption schemes show provable security, they usually expose some query information, making them less practical. This paper presents a noble scheme that enables sharing and keyword-based searching on encrypted data stored in the cloud storage. It helps users download specific data by searching keywords and avoid downloading all the data from the storage.