Reviewerly/exHarmony huggingface dataset:
English to Meiteilon Machine Translation Using Seq2Seq with Long Short-Term Memory

Neural Machine Translation (NMT) is a cutting-edg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sup> strategy that has produced significant advancements in the field of machine translation for translating one language into another when compared to more conventional methods. The people of India speak their distinct regional languages for communication. Given the wide variety of languages in India, translation of language is crucial. However, it is nearly impossible for a human to translate one language into another language. This is achievable through Neural Machine Translation. English is offered as a second additional official language in India. However, India uses English extremely little, creating a communication gap. Numerous web applications translate one language to another language. Some of these are Google Translate, Translate, Deep Learning, and Microsoft Translator. But these applications do not support the regional languages. This paper aims to propose a language translation model using a sequence-to-sequence model with Long Short-Term Memory (LSTM) for translating English to Meiteilon.
Robust Digital Watermarking based on Machine Learning

In the generation of big data and systems administration, it is important to build up a safe and strong digital watermarking plan with high computational effectiveness to ensure copyrights of digital works. However, the greater part of the current strategies focuses on robustness and embedding limit, losing a security, or requiring huge computational assets in the encryption procedure. Digital watermarking has become an important method for ensuring the security of digital copyrights and ensuring the authenticity of data because of rapid advancement and broad usage of technologies related to multimedia and network connectivity. which has been used for practical application such as medical application, currency, digital product identification and many more. The success story of digital watermarking techniques deepened on the embedding of the watermark image and present image. In the current decade, the machine learning feature-based watermarking techniques create new milestone concerning security concern. The machine learning and hybrid digital watermarking performances an especially vital role in optimization and selection of features which improves the performance of watermarking process. In this paper, we have presented countless digital watermarking, methods which is based on various machine learning procedures along with their comparison, and limitation.
Improved Machine Learning Scheme for Review Analysis Using Suggestion Based Approach

For many people, especially potential customers, reading product reviews before purchasing the item becomes a habit. People frequently examine reviews of the current product from customers before making a purchase decision. There is a good possibility that customers will purchase the product if the review is primarily positive; but, if it is primarily negative, they usually choose another option. Although good customer feedback can result in substantial financial gains for organizations, it can also be considered when making decisions about how to create products and what services to offer consumers. Many of the daily activities have been influenced by the Internet's explosive rise. E-commerce is one of the sectors with the fastest development. E-commerce sites typically allow users to post feedback about products they have purchased.
Enhancing Cloud Communication Security Through Forward Secrecy Algorithms

This research study explores the implementation and evaluation of the forward secrecy algorithm in the context of cloud computing. Cloud services have become ubiquitous in various domains, necessitating robust security measures to safeguard sensitive data during transmission and storage. The forward secrecy algorithm presents a promising solution, combining asymmetric and symmetric key encryption to achieve data confidentiality and integrity, even in the face of session key com- promises. Through a comprehensive review of related literature, we identify gaps and limitations in existing approaches to cloud security. Our proposed algorithm is assessed using performance metrics, such as encryption and decryption times, key generation times, and throughput, in a simulated cloud environment with CloudSim. The results indicate significant enhancements in cloud security, with efficient communication and minimal overhead. By addressing challenges related to data privacy, multi-tenancy, compliance, and insider threats, the forward secrecy algorithm offers a robust defense against potential attacks and bolsters the overall security posture of cloud computing environments.
High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application.
An Advanced Feature Extraction Approach for Classifying and Categorizing Complex Data by Using the MMRDL Framework

Nowadays, Machine learning (ML) plays a significant role in processing multimedia based on user search queries. To develop an efficient classification model that processes the high dimensional data, an ensemble model combined with deep feature extraction and robust classifier. Combining the three algorithms generate the stochastic belief space in this work. The planning of these algorithms based on maximum entropy reinforcement learning (ME-RL), and generative adversary model (GAM). The comparison between various unmodeled adversarial methods is suggested to achieve robustness because the malicious behaviors were applied in the simulation framework to reduce the agent's action predictability. Guided Deep Learning (DL), the reinforcement-based approach, can be employed for multi-model categorization. The text and image data applied on a single neural network method may categorize. The proposed system properly understands the belief space policy using feature extraction on text and image datasets. The proposed model shows the maximum entropy depends on the input dataset.
YOLOv8-based Waste Detection System for Recycling Plants: A Deep Learning Approach

Waste management is increasingly attracting attention due to its role in smart and sustainable development, especially in developed and developing nations. This system consists of a series of interconnected processes that perform various complex functions. Deep learning has recently attracted interest as an alternative computational method to solve various waste classification challenges. Many researchers have focused on this area, yielding significant research results in recent years. Although several in-depth investigations have been conducted on waste detection and classification, the WaRP dataset was created specifically to train and evaluate the proposed algorithms using industrial data from conveyor belt of waste recycling plant. Surprisingly, no research has explored the application of the YOLOv8 model to solve the waste management problem using the WaRP dataset. This experiment makes a notable contribution by detecting waste through a pyramid and direct prediction method, which differs from the traditional model based on anchor boxes. Through experimentation with different YOLOv8 model weights, this research study found that YOLOv8s provides relatively good results with smaller dataset and lower processing time. On the other hand, YOLOv81 achieves a higher mAP50 value of about 59% on the same dataset, but at the cost of high inference time.
Federated Learning and Differential Privacy in AI-Based Surveillance Systems Model

Surveillance systems, particularly AI-based ones, play a crucial role in enhancing public security. However, they have also raised significant concerns about privacy due to the sensitive nature of the collected and analyzed data. This research highlights the importance of data privacy without compromising the accuracy of AI-based surveillance systems. We propose the integration of two privacy-preserving methods, Federated Learning (FL) and Differential Privacy (DP), to enhance the security and privacy of data used in the training and testing processes. Federated Learning enables the training of AI models using distributed data without needing to centralize all the data on a single device. In contrast, Differential Privacy offers a statistical framework that safeguards individual data points throughout the training process. Both methods were implemented individually and combined on the chosen AI model. Our findings reveal a trade-off between privacy preservation and model accuracy when applying these privacy-preserving techniques to an AI surveillance system. While there was a reduction in overall accuracy, privacy preservation capabilities yielded satisfactory results. Notably, the optimal results were obtained when combining Federated Learning and Differential Privacy with a noise multiplier of 0.6 and 50 devices participating in the federated learning process, yielded an overall solid accuracy of 69.33, and maintained acceptable confidence levels across different categories. This configuration underscores the need for a careful balance between privacy protection and model performance in AI surveillance systems, opening avenues for future research on refining these techniques. Our code is available at: https://github.com/slimmyYer211/RMCS-PPCV_01
Auth at Battery Swapping Stations for Electric Scooters: QR Code vs. RFID Interaction Designs

Electric vehicles (EVs) are gaining popularity due to their potential to reduce greenhouse gas emissions and reliance on fossil fuels. With the rising demand for EVs, the development of Battery Swapping Stations (BSS) has become essential. This research delves into the interaction design for BSS, specifically targeting electric scooters in Indonesia. We compared authentication methods at BSS using Quick-Response Codes (QR Codes) and Radio Frequency Identifiers (RFID). Participants were asked to authenticate using both methods, and the study measured the average time taken for each. While RFID proved faster in terms of authentication time, user preferences between the two methods were split, making it inconclusive, which was superior overall.
A Two Level Security Watermarking/Encryption Schema to Ensure the Protection of Medical Images

This research presents a novel watermarking technique specifically tailored for medical imaging applications. Our approach incorporates SVD, DCT, and DWT methods, complemented by AES to ensure the security and integrity of medical images while preserving their hidden information. The process begins with DWT applied to the medical image at the second level of decomposition. The resulting LL2 component is then divided into sub-bands, each sized $8 \times 8$. DCT is then applied to each sub-band, generating $8 \times 8$ blocks, which undergo SVD analysis. To achieve robust watermark extraction, we adopt an adaptive approach by customizing the watermark based on the SVD components. This adaptability ensures efficient retrieval of the watermark, even in the face of diverse types of attacks. To determine the appropriate “$\alpha$” for the watermark embedding process, we utilize the Weber constant. The proposed system undergoes rigorous testing against various attacks, including compression, noise, filtering, and geometric transformation. Through extensive experiments and analyses, we demonstrate the achievement of low distortion and high robustness in the watermarking process, while simultaneously ensuring enhanced security during the encryption phase.
Siamese Networks With Attention for User-Independent Offline Handwritten Signature Verification

Signatures are one of the most relevant and convenient methods for user verification, despite their vulnerability to skilled tampering. In this work, we propose a user-independent offline signature verification technique based on the Siamese architecture. For the feature extraction step, we use an improved residual network by incorporating channel wise attention. Experiments conducted on the CEDAR database have validated the effectiveness and strength of our approach.
Video Data Analytics Dashboard for Anti-drone system

Mini unmanned aerial vehicles (UAVs), commonly known as small drones, have witnessed significant advancements in recent years and found applications in diverse fields such as search and rescue, wildlife monitoring, and border patrol. However, their misuse for illegal surveillance, smuggling, and potential involvement in terrorist activities poses serious concerns. Additionally, these mini-UAVs can disrupt the operations of commercial and military aircraft and infringe upon personal privacy. Hence, the development of an effective anti-UAV monitoring system is imperative to safeguard sensitive locations.In this paper, we propose a layered dashboard architecture specifically designed for an anti-drone system. Our software solution presents an intuitive interface that efficiently processes and visually represents video data captured by surveillance sensors. It incorporates AI-powered functionalities, including object detection, target tracking, and classification of small drones. To evaluate the effectiveness of the multi-layered solution, we deployed it within a no-fly zone, where it successfully identified and tracked drones in near real-time. The resulting dashboard offers a user-friendly interface and exhibits significant potential in enhancing the monitoring capabilities and prompt response to potential drone threats.
Super-Resolution of UAVs Thermal Images Guided by Visible Images

Thermal imaging of unmanned aerial vehicles (UAVs) can sometimes suffer from a lack of information due to their small sizes. Therefore, the ability to understand and analyze such images of drones will be limited. Nevertheless, high-resolution (HR) visible images are often available and could be useful for improving the resolution of thermal images from UAVs. In recent years, Deep learning has been increasingly used in several computer vision tasks such as super-resolution (SR), where it has shown promising results for image resolution enhancement as it allows for creating high-quality detailed images. In this paper, we propose a Guidance Super-Resolution Network (GSRNet), that improves the spatial resolution of thermal UAVs images by taking advantage of the textures of the visible images. We adapt a Convolutional Neural Network (CNN) model that has an encoder-decoder architecture to translate visible images into thermal images as well as an auto-attention mechanism to allow the network to selectively focus on relevant structures of the image while ignoring irrelevant parts. Moreover, to preserve the low frequency information such as the brightness level and the body that are present in the low-resolution (LR) thermal image, we propose to merge the letter image with the translated one, such that the obtained HR thermal image when downsampled it equals the original LR one. Experimental results on the custom UAVs image dataset prove the higher performance of the proposed model on both qualitative and quantitative evaluations when compared to several state-of-the-art (SOTA) methods.
Digital Image Forgery Detection with Focus on a Copy-Move Forgery Detection: A Survey

The importance of ensuring the authenticity and reliability of digital images has grown significantly, primarily due to the ease of modifying such images with the progress of digital image editing tools. Consequently, there is a growing emphasis on the development of techniques for detecting image manipulation. One particular area of focus in digital image authentication is copy-move forgery detection. This paper presents a survey and a comparative study on copy-move forgery detection techniques in digital images, databases, and evaluation metrics. The study aims to provide insights into the effectiveness of different methods in detecting copy-move forgeries. The paper discusses prominent detection techniques, including block-based, keypoint-based, transform domain, hybrid methods, deep learning, and GAN approaches. The findings highlight the strengths, weaknesses, and key similarities and differences among the approaches. This study contributes to the understanding of the state-of-the-art in copy-move forgery detection and provides guidance for future research in this field.
A Robust Medical Image Watermarking Approach Using Beta Chaotic Map, DWT, and SVD

In today’s era of rapid computer network advancements, an enormous volume of messages is exchanged daily, touching several sectors, including the medical field. While the Internet provides flexible communication capabilities, it also presents many security challenges including data theft, data duplication, data leakage, and copyright protection. One potential solution to address this issue is the application of watermarking. This paper presents a robust watermarking algorithm for medical images that combines the Beta Chaotic Map (BCM), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD). Initially, the watermark undergoes encryption using BCM. Subsequently, the medical image is decomposed into four sub-bands (LL, LH, HL, and HH) through DWT. The low-frequency region LL contains the embedded watermark information, which is obtained by computing the embedding singular value using SVD. In the extraction process, the watermarked medical image is decomposed using DWT, and the watermark is extracted by reversing the SVD. Finally, the watermark is decrypted by applying BCM. To ensure the invisibility of the watermark in the host image and robustness against various attacks (e.g., JPEG compression, JPEG2000 compression, sharpening, noise, and filtering), the algorithm employs metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Experimental results validate the algorithm’s effectiveness through tests based on Normalized Correlation (NC).
Hybrid approach for speaker recognition based on formant and pitch extraction

Human voice is an ideal data source for identifying people in many applications. Because of the increasing need for security in different public places, voice biometrics may be a good solution, as we can easily take voice records. This paper provides a brief overview of the approaches utilized in recognizing speakers, and then presents a novel approach for recognizing speakers in degraded smart-home conditions. The suggested approach includes a pre-processing phase, a feature extraction phase, and a classification phase, where the feature extraction phase consists of formant extraction to get the spectrum energy maxima of speech audio, dynamic time warping (DTW)to find an optimal alignment between two provided temporal sequences under definite restrictions, and refinement process to improve the results of the DTW system output. The experiments are carried out on a database containing 1,248 samples in order to validate the suggested approach. The latter has good results as regards the state of the art with 94.5% accuracy.
Comparative Analysis of Deep Learning Architectures for Masked Face Recognition: A Study of Performance and Robustness

Face recognition technology has been significantly impacted by the COVID-19 epidemic, among other parts of daily life. Facial recognition technologies, which were formerly employed for security, access control, and identity verification purposes, have actually become less reliable as a result of mask use. Although deep learning-based methods have demonstrated considerable promise in this field, it is still unknown how well various architectural approaches perform. The performance of five well-known deep learning architectures—ResNet50, VGG16, InceptionV3, MobileNetV2, and Xception—is compared in this study. We evaluate the architectures using a sizable dataset (LFW) made up of masked faces of various racial and gender compositions. According to our findings, the ResNet50 has a higher accuracy score of 98.3% compared to the Vgg16 architecture’s 98.2%, Xception’s 98, MobileNet’s 97.6 and Inception’s 97.4%. In order to determine the impact of various model elements, such as the quantity of convolutional layers, the quantity of parameters, and the time inference, we also perform an analysis study. Our findings show that a key factor in getting high accuracy for masked face recognition is the network’s depth.
A green isolated word sign language recognition system based on geodesic metric space

This paper presents a new isolated word sign language (IWSL) recognition strategy to attempt an ecological and friendly solution based on the deaf brain pipeline. Two major steps are proposed in this research field.The first one is based on hands shape-motion extraction and the second is based on its analysis without introducing deep learning frameworks (DLF) to carry out less data, less time execution and less carbon footprint consumption.So, we focus on developing an energy-saving system offering a green IWSL recognition system based on geodesic distance as an elastic measure while preserving the same DLF performance. Results indicate that the new IWSL recognition system is a promising solution as it trades off energy and accuracy compared with existing systems.
An object recognition method based on deep BCNN with Reinforced Dense Blocks

In the computer vision field, object recognition becomes a very active field of interest. In this research, we proposed a very deep learning approach namely Boosted DenseNet. Our method used the BCNN architecture boosted by conducting reinforced dense blocks. These reinforced dense blocks consisted of a similar convolutional layers numbers as BCNN boosted by adding MBA Activation and Concatenated Rectified Linear Unit functions. In addition, our Boosted DenseNet was improved by applying boosted convolutional layers which offer a deeper network with the same number of parameters. Besides, our method has been reinforced by adding Generalizing Pooling layer. Generalizing pooling, which combined pooling operations within a hierarchical tree structure, replaced the max-pooling layer in reinforced dense blocks. Experiments on CFAR-10 and Pascal VOC 2007 have outperformed the state-of-the-art approaches and demonstrated the robustness of our model for object recognition tasks.