Miracle-dz/UniFAR-FTUs huggingface dataset:
Ocean Wave Height Forecasting using Deep Learning Neural Networks and Optimization Techniques

Ocean wave prediction is crucial for many marine applications, including navigation and coastal engineering. Accurate wave height prediction can help reduce the risks. In this paper, we have implemented an interface that can predict wave height based on Neural Network, Optimization models and compared the performances of these models on the Mooloolaba Beach Dataset with regular and irregular waves. The models we experimented with are single-shot LSTM, multi-shot LSTM, CNN-LSTM Hybrid model, Bidirectional LSTM, Artificial Neural Networks(ANN) with different kernels like Laplacian, Polynomial, ANOVA Radial basis, and Multi Additive Regression Trees(MART). These models are designed to capture the Nonlinear and Temporal characteristics of ocean waves. We have also experimented with different optimization techniques like Symbiotic Organisms Search (SOS), Hybrid Symbiotic Organisms Search (HSOS), and Moth Flame Optimization (MFO). The Dataset covers a period of One year and has a sampling Frequency of 1.28Hz. We evaluate the models using six metrics: Accuracy, Precision, F1 score, Mean Square Error, Mean Absolute Error, and Root mean square error. The results show that the ANN model with ANOVA kernel outperformed other models in terms of Accuracy of 94.5%. We have also developed a Web Interface. It helps Fishermen to make informed decisions, reduce the risks associated with uncertain wave conditions, and contribute to a safer fishing experience.
Cybersecurity Fusion: Leveraging Mafia Game Tactics and Reinforcement Learning for Botnet Detection

Mafia, also known as Werewolf, is a game of uncertainty between two teams, which aims to eliminate the other team's players from the game. The similarities between detecting the Mafia members in this game and botnet detection in a computer network motivate us to solve the botnet detection problem using this game's winning strategies. None of the state-of-the-art researches have used the Mafia game strategies to detect the network's malicious nodes. In this paper, we first propose the Mafia detection strategies, which are applied using linear relation and reinforcement learning techniques. We then use the suggested strategies in a network infected by the Mirai botnet, using Mininet, to evaluate the performance of botnet detection. The average results show that the suggested strategies are 11% more accurate than the existing ones for the Mafia game. Additionally, the true positive and true negative detection rates of a network modeled by the proposed Mafia game are 71% and 91%, respectively.
Cancer Networks: Insights Into The DiseaseMechanisms

Biological systems, akin to engineered electrical or electronic circuits rely on modular organization, with naturally interconnected molecular components forming intricate circuits responsible for maintaining cellular processes. Just as electronic circuits are engineered with feedback mechanisms to regulate and stabilize their output, biological circuits employ naturally designed (acquired through evolution) intricate feedback loops to maintain cellular processes. Nature has tuned several specific feedback and feed forward mechanisms over the evolutionary time scale to perform normal biological functions such as signal amplification, attenuation, and homeostasis. In diseases such as cancer, dysregulated feed forward and feedback loops lead to uncontrolled cell growth, resistance to therapy, and disease progression. Adopting this network or circuits perspective offers unique insights into understanding the behavior of biological systems and disease emergence. Intricate understanding of dysregulated biological circuits in cancer extends beyond theoretical speculation. Thus, by integrating knowledge of dysregulated circuits, it is possible to identify vulnerabilities within these circuits and has aided in unravelling the mechanisms driving cancer, design tailored approaches for diagnosis, prognosis, and therapeutic interventions. The present work illustrates the properties of four different cancer networks namely breast cancer, lung cancer, colorectal cancer, and hepatocellular carcinoma. Unique signature of the cancer networks known as network motifs were identified. Specifically, this work explores the prevalence of coherent type 1 feed forward motifs within each network. Such analysis can assist in understanding the diseases mechanism, developing innovative therapeutic strategies that target specific components towards suppressing cancer circuitry.
Early Alzheimer’s Detection through VOC Analysis: A Novel Device Approach

Alzheimer’s disease (AD) is a progressive and degenerative brain disorder that affects memory, thinking, behavior, and cognitive function. Current research is being done on analyzing different mediums such as blood, breath, and bodily fluids, for finding biomarkers related to AD. Several breath volatile organic compounds (VOCs) have been linked in much higher concentrations in AD patients compared to healthy controls. Thus, VOC-based sensing has emerged as a promising approach for non-invasive and early detection of the disease. In this work, we present a portable, cost effective, and highly accurate VOC sensor-based device for non-invasive AD detection and progression monitoring. Our novel device utilizes state-of-the-art photoionization detection (PID) technology to selectively detect specific VOC biomarkers associated with AD, offering high sensitivity and specificity for AD detection. A user-friendly mobile-based application for the analysis and reporting of the data is also developed. The validity of our device is tested using different concentration samples of specific VOCs. This VOC-based device has the potential to address several current shortcomings in AD diagnosis and monitoring.
Analysis of the Solution Error of a Long Baseline Underwater Acoustic Localization Method with Few Acoustic Beacons

Among the existing underwater acoustic positioning methods, the long baseline positioning system is widely used because of its high positioning accuracy and good realtime performance. The least square method is the main method for solving long baseline underwater acoustic location. This method needs more than 4 reference acoustic beacons to solve the 3D position of the target acoustic beacon, which the number of reference acoustic beacons is large and the laying cost is high. This paper presents a long baseline underwater acoustic localization method with few acoustic beacons. This method uses an analytical algorithm to solve the distance equation, and uses three reference acoustic beacons to solve the coordinates of the target acoustic beacon. In this paper, the influence of the relative position between the reference acoustic beacon and the target acoustic beacon on the position calculation error is analyzed, and the distribution law of the target acoustic signal punctuation set satisfying the conditions is solved under different positioning error ranges. Theoretical analysis and simulation results show that, under the same distance measurement accuracy, the location distribution of reference acoustic beacon has a great impact on the target solution accuracy. In practical engineering, the layout of reference acoustic beacon should be adjusted according to the target trajectory to reduce its impact on the target position solution accuracy.
Enhancing Video Storage Efficiency in the Cloud: Machine Learning-Driven Optimization Strategies

Video storage optimization in the cloud is a critical research area with significant implications for efficient resource utilization, cost reduction, and improved user experiences. This research manuscript explores the application of machine learning techniques for video storage optimization in cloud-based environments. The objective is to develop innovative methods that leverage the power of machine learning algorithms to enhance the efficiency of video storage, enabling effective management, retrieval, and delivery of video content. The manuscript presents a comprehensive analysis of existing approaches, identifies their limitations, and proposes novel techniques to address the identified challenges. The research focuses on leveraging machine learning algorithms for video transcoding, content-aware caching, intelligent data placement, and adaptive streaming, among other areas. Experimental evaluations and performance comparisons are conducted to validate the effectiveness of the proposed techniques. The findings show that the proposed CNN-based video transcoding technique achieved an average storage size reduction of 40%. The content-aware caching technique outperformed baseline methods with an average cache hit rate of 75%. The RL-based intelligent data placement technique achieved an average data popularity of 85%. Lastly, the RL-based adaptive streaming technique obtained significantly higher user satisfaction ratings, with an average Mean Opinion Score (MOS) of 4.6 out of 5.
BEmoFusionNet: A Deep Learning Approach For Multimodal Emotion Classification in Bangla Social Media Posts

Multimodal emotion classification, incorporating both image and text modalities, has gained significant attention due to the exponential growth of multimedia data. This research aims to develop a robust system for multimodal emotion classification in Bangla social media content, utilizing both image and text data. Despite limited resources, understanding emotions in Bangla is crucial for mental health interventions. Unlike previous studies focused on well-resourced languages, this research addresses the gap by specifically targeting regional languages like Bangla, going beyond the traditional positive, negative, and neutral classes. This work presents a multimodal Bangla social post dataset containing 4660 samples. Transfer learning techniques, utilizing pre-trained models like ResNet50, VGG16, and InceptionV3, extract visual features, while deep learning architectures such as BiLSTM and CNN are employed for textual content analysis. Multimodal learning techniques, including feature fusion and decision fusion, are explored to combine visual and textual representations. We evaluated the feature fusion of InceptionV3 and BiLSTM features on our Bangla social media post dataset. Our approach achieved a weighted f1-score of 77.50%.
Enhancing Facial Image Clarity: Deblurring Gaussian blur with UNET++ Architecture

In this paper, we present a comprehensive study on enhancing the clarity of facial images through Gaussian deblurring using the UNET++ architecture. Employing the high-quality FFHQ dataset comprising 512x512 RGB images, we embark on an investigation of varying Gaussian kernel sizes, specifically 3x3, 5x5, and 7x7, applied to blurred facial images. To effectively address this deblurring task, we train three distinct UNET++ models, each tailored to deblur images corresponding to a specific kernel size. Our experimental results showcase the efficacy of our approach, with the trained models achieving an impressive Peak Signal-to-Noise Ratio (PSNR) of 39.143 dB, a Structural Similarity Index (SSIM) of 0.983 and a Multiscale SSIM (MS-SSIM) of 0.998 for a 3x3 Gaussian kernel. This substantiates the potential of employing UNET++ architecture for Gaussian deblurring tasks, offering a promising avenue for enhancing the visual quality of facial images and potentially benefiting a range of applications, from image restoration to facial recognition systems.
Advancing Optical Tomography Image Analysis: Exploring Convolutional Neural Network Model Variants for Retinal Damage Detection

Retina is a vital component of the eye; it plays a crucial role in visual perception. Any damage to the retina can lead to vision impairment and severe ocular diseases. Early detection and accurate diagnosis of retinal conditions is crucial. In the domain of medical image analysis, especially within the scope of retinal imaging, convolutional neural network (CNN)-based deep learning models have exhibited promising results. This paper involves the detection of retinal conditions such as Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and Drusen. The paper presents a comparative study of CNN models for the detection of these retinal damages using Optical Coherence Tomography (OCT) images. This study focuses on evaluating the performance of popular CNN architectures, like Residual Networks (ResNet), Dense Convolutional Networks (DenseNet), Inception Networks, Visual Geometry Group (VGG). Also, the paper provides a comparative analysis of different depth variants of deep learning models such as ResNet (18, 50 and 101), DenseNet (121, 169, and 201), Inception-V3, Inception-ResNet-V2, and VGG (16 and 19). These models are assessed on a dataset containing OCT images of Normal, DME, CNV and Drusen retinal conditions. The effectiveness of the models are evaluated using evaluation metrics such as accuracy, precision, recall and F1 score. Additionally, visualizations such as confusion matrix, training accuracy versus validation accuracy plots, training loss versus validation loss plots are utilized to provide a better understanding of the model performance. The idea is to explore the impact of depth on the model performance and understand how deeper models demonstrate an enhanced ability to learn complex features and capture intricate details within the retinal data. Exploring these depth variants aims to provide insights into the advantages and potential trade-offs associated with increasing depth of deep learning models for retinal damage detection tasks.
Prediction of Concrete Strength with Artificial Neural Networks Enhanced by Nano-Silica

The investigation is centered to create a methodology utilizing an artificial neural network (ANN) to forecast concrete's crushing resistance at the 28-day mark incorporating nanosilica offering as an alternative to traditional cement. Nanosilica is commonly used construction material known for its ability to enhance the strength of concrete. Nevertheless, accurately predicting this strength requires intricate computations, consumes time, and demands expertise. To address this, an AI model was created using machine learning algorithms, trained on a dataset comprising experimental compressive strength data of nanosilica-concrete mixtures, along with additional information on nanosilica particle properties. The model's efficacy was assessed using an independent dataset, which demonstrated its ability to accurately forecast nanosilica's compressive strength with high precision. The objective of this research was to use the ANN technique to establish a link between various input factors and the resultant 28-day compressive strength in nanosilica-containing concrete. The data utilized for the study was sourced from existing literature.
Tracking Control of Tello EDU Quadrotor Drone Using Image Thresholding

In many industries, it is crucial to have an efficient and precise way of monitoring objects or individuals. Drones can be used for this purpose, such as in agriculture, to observe crop growth and detect potential issues. This makes them a valuable tool in different fields, offering greater accuracy and faster data collection. This research uses image thresholding to implement a Tello EDU RoboMaster TT quadrotor drone tracking control system. The goal is for the drone to autonomously follow different line shapes, rounded at different angles, steadily and safely. The drone's camera captures the line's contours using open-source methods, which are processed using image thresholding and binary masking techniques. A control point is generated at the centre of the contours and compared to the centre of the image to guide the drone's movements in real time. The research has successfully enabled the drone to follow lines of various shapes.
Adversarial Debiasing using Regularized Direct Entropy Minimization in Wasserstein GAN with Gradient Penalty

The promise of machine learning algorithms are overshadowed by the ethical challenges they present, especially when biases in data lead to discriminatory outcomes based on gender or other sensitive attributes. This paper introduces an innovative methodology to address gender bias in machine learning models, particularly when processing textual data. By harnessing the power of adversarial training with the Wasserstein GAN enhanced by gradient penalty (WGAN-GP), our approach strives to minimize gender-specific information in the model’s latent representations. Central to our methodology is the integration of a Direct Entropy Minimization (DEM) regularizer, which further ensures that these representations are devoid of gender biases. The dual objective, achieved through our primary classifier and its adversarial counterpart, not only maintains high classification accuracy but also substantially eliminates gender bias.
Timing of ungulate browsing and its effect on sapling height and the field layer vegetation

Browsing and grazing by ungulates affect the forest structure by altering species composition and affect the growth in individual trees. The purpose with this study is to investigate the ungulate species composition visiting the clear cut, how timing of browsing affects the height increment in deciduous and conifer saplings, and how deer grazing affects the field layer coverage and height. I used seasonal exclosures in ten clear-cuts where I took data on deer visitation using camera traps, field layer vegetation coverage and height, and also height and browsing pressure on individual saplings. Each site had one permanent exclosure, one exclosure which was closed in winter but open in summer, one exclosure which was open in winter but closed in summer and one control plot which was never fenced off. The majority of the ungulates were fallow deer which shoved a higher visitation rate during summer than winter. Permanent exclosures and exclosure which were closed in summer had the highest height increment for deciduous trees. The difference in field vegetation height between 2015 and 2016 showed an effect of deer visitation rate per site but not for treatment. Browsing during summer lowered the height increment in deciduous saplings compared to winter browsing. It’s too early in this experiment to make conclusions on conifers but it seems that the timing is not as important factor as it is for deciduous saplings regarding to height development.
Advancing Arabic Hate Speech Detection via Neural Transfer Learning with BERT

Online hate speech poses grave societal dangers, necessitating automatic detection systems. However, limited efforts have focused on Arabic's complex dialects. This study investigates neural transfer learning for dialect Arabic hate speech detection. A public Levantine Twitter dataset with 5,846 expert annotated tweets is compiled spanning normal, offensive, and hate speech. Traditional machine learning models including SVM, logistic regression, and gradient boosting are benchmarked, achieving 80-83% accuracy. However, these models fail to capture nuanced contextual differences between offensive and hateful language. To address this, transfer learning is proposed using the pretrained Arabic BERT model ArabERT. ArabERT leverages BERT's bidirectional representations to model linguistic context. ArabERT is fine-tuned on the Levantine dataset for hate speech classification. Results show ArabERT significantly outperforms machine learning models, attaining 90% accuracy and 94% Fl-score specifically for hate speech detection. Detailed analysis demonstrates ArabERT's contextual modeling enables nuanced discernment between offensive and hateful tweets. The outcomes provide strong evidence that transfer learning approaches like ArabERT are crucial for handling informal multi-dialect Arabic. This work makes three key contributions - introducing an effective neural framework for Arabic hate speech detection, rigorous benchmarking, and providing insights into deep learning strategies. The findings showcase transfer learning's efficacy for low-resource Arabic NLP and pave promising directions for future progress.
Gender Bias in Online News Articles of a Computational Analysis Using AI-Based Critical Discourse Analysis

Various and accurate data processing and analysis have been made possible by computers for many years. In order to examine societal behavior by finding patterns in data, humanities and social science researchers are increasingly using computer technologies like artificial intelligence (AI) and machine learning (ML). Specifically, speculative sentences or clauses are frequently used in news item headlines and abstracts to draw in unwary readers. The dominance of men in the news is highly apparent, and Females are not adequately represented and underexamined in the same news categories as their male coworkers because they are seen as “eye candy” or “inferior” and are thus thought to be so. In this work, gender bias in online news items from two important English news datasets that were used for news classification and recommendation is explored. Additionally, the research discusses views of gender bias in news generated by AI. Through the use of a questionnaire with five different themes, information has been collected from 490 respondents. The data was analysed using support vector machines (SVM), decision trees (DT), random forests (RF), and artificial neural networks (ANN). The experimental results show that the performance of the AI algorithms is influenced by the theme-dependent prediction of gender perceptions in news stories. According to the experiment's findings, ANN consistently shows the most efficacy, with an accuracy level of 88%. This is followed by RF and SVM, which have accuracy levels of 85% and 81%, respectively. This work makes a substantial contribution to the subject by using AI to model behavior among people in civilization.
Does AI Control Influence on Employee Resilience in the Workplace

The power of AI-driven solutions can create a more supportive and adaptive environment in every organization for their workforce. AI can provide immediate access to mental health resources through chatbots and virtual assistants, offering employees a confidential avenue to seek guidance and coping strategies when dealing with workplace stressors. The study aimed at detecting some substantial factors to improve the employee resilience in business organizations. Additionally, it analyzed the impact of AI control on the influencing factors. The study result revealed that leadership and management, training and development facilities, and working environment have extensive impact on the employee resilience in business organizations. Moreover, AI control has been playing significant role to improve the employee resilience in the workplace. The research study identified some important measures by which the business organizations can facilitate more open and constructive communication channels, allowing organizations to gather feedback and address employee concerns promptly.
Transformation-Invariant Over-the-Air Combining for Multi-Sensor Wireless Inference

Deep neural networks offer reliable solutions for many classification and regression tasks; however, their applicability in real-time wireless applications with simple sensor networks is limited due to the significant amount of bandwidth required for data transmission. In this study, we propose a multisensor wireless inference system where an edge device combines features sensed by different sensors. Due to the limited computational capabilities of sensors, the features obtained through the front part of the network are transmitted to the edge device, which uses Lp-norm inspired and LogSumExp (LSE) approximations for the maximum operation to obtain transformation-invariant features. These features can be transmitted in an over-the-air manner, ensuring bandwidth-efficient transmission. We also consider multi-modal network branches for sensors based on their computational capabilities, improving the overall performance by using data obtained from both computationally limited and powerful devices enhancing the usefulness of the overall sensed data.