Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Hybrid Bat Algorithm-Ensemble on Side Effect Prediction: Case Study Metabolism and Nutrition Disorders

Drug side effects are an unwanted response by the body. This side effect becomes a serious problem for health and can cause effects such as nausea, headaches, and even death. Currently, the detection of side effects still uses clinical trials that are time-consuming and costly. In fact, it occurs often that possible side effects go unnoticed due to the limitations of the tested samples. The alternative is new, effective, and efficient detection, namely machine learning. However, one of the challenges in drug detection using machine learning is the high dimension of features, so the Hybrid Bat Algorithm (HBA) for feature selection can be applied to this. This study aims to predict drug side effects with a case study of metabolism and nutrition disorders by using the hybrid bat algorithm-ensemble method. In this study, the feature selection process utilized the hybrid bat algorithm, and an ensemble approach was employed to construct the prediction model using three techniques: Random Forest, AdaBoost, and XGBoost. Based on the experiments that have been carried out, the model created is successful in predicting drug side effects. XGBoost produces the most accurate model with the best performance, with accuracy metrics and F1-Score values of 0.743 and 0.811, respectively.
A Deep Learning Approach to Forest Fire Detection and Monitoring

Forest fires pose a significant threat to sensitive ecosystems worldwide. Early detection and monitoring of these fires are critical for improving response times and aiding firefighting and evacuation efforts. This research paper proposes an approach that utilizes Convolutional neural network (CNN) based transfer learning models in conjunction with drone-derived images to accurately detect and differentiate between fire and nonfire images. This automated system enhances the efficiency of forest patrols and rapid-fire detection without human intervention. Pre-trained CNNs, including MobileNetV2, ResNet50, Xception, and VGG16, were fine-tuned using a dataset containing forest fire and nonfire images. A sigmoid classifier in the classification layer predicts the probability of an image being a fire or nonfire. Experimental results demonstrate the models' ability to identify fires across various scenarios with competitive processing times, making them highly deployable in forest settings.
Porcine sapovirus replication is restricted by the type I interferon response in cell culture

Porcine sapovirus (PSaV) of the family Caliciviridae, is the only member of the genus Sapovirus with cell culture and reverse genetics systems. When combined with the piglet model, these approaches provide a system to understand the molecular basis of sapovirus pathogenesis. The replication of PSaV in cell culture is, however, restricted, displaying an absolute requirement for bile acids and producing lower levels of infectious virus than other caliciviruses. The effect of bile acids has previously been linked to a reduction in the signal transducer and activator of transcription (STAT1)-mediated signalling pathway. In the current study, we observed that even in the presence of bile acids, PSaV replication in cell culture was restricted by soluble factors produced from infected cells. This effect was at least partially due to secreted IFN because treatment of cells with recombinant porcine IFN-β resulted in significantly reduced viral replication. Moreover, IFN-mediated signalling pathways (IFN, STAT1 and the 2′,5′-oligoadenylate synthetase) were activated during PSaV infection. Characterization of PSaV growth in cell lines deficient in their ability to induce or respond to IFN showed a 100–150-fold increase in infectious virus production, indicating that the primary role of bile acids was not the inactivation of the innate immune response. Furthermore, the use of IFN-deficient cell lines enabled more efficient recovery of PSaV from cDNA constructs. Overall, the highly efficient cell culture and reverse genetics system established here for PSaV highlighted the key role of the innate immune response in the restriction of PSaV infection and should greatly facilitate further molecular studies on sapovirus host–cell interactions.
Protocol development for specific detection of SARS CoV-2 variants using a U-bent fiber optic probe

The protocol development to detect and distinguish between two different variants of the novel SARS Corona Virus 2 (SARS CoV-2) is presented using a complementary DNA binding method. The utility of the plasmonic U-bent fiber optic probe platform is explored for the development of sensing mechanism. We envisage that this photonic sensing platform can be used to develop a point-ofcare device to distinguish between multiple variants of a virus in real time.
Variational Autoencoder Frameworks in Generative AI Model

The research provides a comprehensive review of generative architectures built upon the Variational Autoencoder (VAE) paradigm, emphasizing their capacity to delineate latent structures inherent to input datasets. Beginning with a foundational understanding of autoencoders, the paper progresses to discuss advancements and augmentations in the domain. The Variational Autoencoder introduces a pivotal element called the Evidence Lower Bound, facilitating the extraction of latent variables from an underlying probability distribution. An enhancement to the VAE is the ß-Variational Autoencoder, which exhibits superior proficiency in the disentanglement of these latent attributes. Building on this foundation, the ß-Total Correlation Autoencoder further optimizes this capability, demonstrating marked improvements over the ß-Variational Autoencoder in the discernment of latent feature spaces.
DNA Microarray Experiments: Biological and Technological Aspects

Summary. DNA microarray technologies, such as cDNA and oligonucleotide microarrays, promise to revolutionize biological research and further our understanding of biological processes. Due to the complex nature and sheer amount of data produced from microarray experiments, biologists have sought the collaboration of experts in the analytical sciences, including statisticians, among others. However, the biological and technical intricacies of microarray experiments are not easily accessible to analytical experts. One aim for this review is to provide a bridge to some of the relevant biological and technical aspects involved in microarray experiments. While there is already a large literature on the broad applications of the technology, basic research on the technology itself and studies to understand process variation remain in their infancy. We emphasize the importance of basic research in DNA array technologies to improve the reliability of future experiments.
Detection of Wild Animals using their sound for identification and conservation in the forest by implementing deep learning

Recognition of animals in the forest using sound classification is an emerging trend in the current research. Our study proposed a model to classify the tiger roar sound using optimized CNN. For this, we have extracted the animal sounds from YouTube and applied the Noise Injection and Pitch shift augmentation techniques to increase the sound samples. We use 1200 audio samples of four animal sounds. This model was implemented with MFCC, ZCR, and Mel-Spectrogram feature extractions to enhance the performance. Our model interpretability for practical wildlife conservation and monitoring applications ensures seamless forest department personnel deployment. The primary objective is to accurately identify animals based on their vocalizations using audio features including sounds from tigers, leopards, elephants, and otters. We also classify the sound using SVM, Random Forest, LSTM, and CNN. Apart from all the models, the Optimized CNN achieved an impressive accuracy rate of 91%.
A Comparative Study of HiveQL and SparkSQL Query Performance in a Cluster Environment

This paper conducts a rigorous comparative analysis of query processing in a cluster environment, employing HiveQL and SparkSQL. Despite their shared SQL-like querying capabilities, their unique architectures and optimizations yield divergent performance outcomes. HiveQL relies on MapReduce, introducing potential bottlenecks for complex queries, whereas SparkSQL leverages in-memory processing, contributing to expedited execution. Consequently, an imperative performance and scalability evaluation is undertaken, with a focus on table schemas and query complexity, providing critical insights for technology selection. The paper introduces a comprehensive performance and scalability analysis framework, furnishing nuanced insights into performance nuances based on table schemas and query intricacies, thereby empowering data professionals in optimal tool selection and design refinement within the dynamic realm of big data environments.
Cancer Type Detection based on Gene Expression Data using Support Vector Machine

Detecting cancer types using machine learning and gene expression data is a valuable and difficult problem in bioinformatics and medical research. This procedure involves analyzing gene expression profiles to detect the type of cancer that a patient may have. Genes are crucial in the development, progression, and categorization of many cancer types. It has become challenging to find the most useful and significant gene type with a specified value in a cancer classification task. In this study, we proposed a model for detecting cancer types such as breast cancer, renal cancer, colon cancer, lung cancer, and prostate cancer based on gene type and their specified value using machine learning. In our research, the ICMR gene dataset is used. Using an integration of Minimum Redundancy Maximum Relevance and wrapper-based feature selection method, gene types are selected based on the relationship between their gene expression values and cancer types. The selected features are used for cancer classification using SVM and achieved 96% accuracy.
Fault Detection in Forest Fire Monitoring Using Negative Selection Approach

Forests are the lifelines of any economy. Forest fires are uncontrolled and unpredictable once occur, identified after their wider spread in the forest. Forest fire detection using Wireless Sensor Network (WSN) is a significant application to monitor it. However, in some situations, unreliable/incorrect data may be sent by sensor nodes due to some fault, resulting in significant problems. Such faulty sensor nodes are unknown to the network and that causes major problems. Hence, the Detection of faulty nodes, if any, is essential and a prerequisite. To fulfill this necessity, faulty node, and forest fire detection is carried out in this research. A Negative Selection Approach (NSA) is introduced in the first phase for faulty node detection and a threshold-based approach is introduced for forest fire detection in the second phase of the proposed work. The experimental findings manifest that the proposed methodology performs efficiently in different scenarios. An NSA-based fault detection approach provides better accuracy with an average of 98.18%, an average of 3.56% False Alarm Rate (FAR), and an average of 1.81 % False Positive Rate (FPR).
Counterexample Guided Neural Network Quantization Refinement

Deploying neural networks (NNs) in low-resource domains is challenging because of their high computing, memory, and power requirements. For this reason, NNs are often quantized before deployment, but such an approach degrades their accuracy. Thus, we propose the counterexample-guided neural network quantization refinement (CEG4N) framework, which combines search-based quantization and equivalence checking. The former minimizes computational requirements, while the latter guarantees that the behavior of an NN does not change after quantization. We evaluate CEG4N on a diverse set of benchmarks, including large and small NNs. Our technique successfully quantizes the networks in the chosen evaluation set, while producing models with up to 163% better accuracy than state-of-the-art techniques.
Artificial Neural Network-based PI control of battery energy storage system connected with MMC-HVDC system

Offshore wind farm (OWF) has received considerable attention as it is growing exponentially worldwide. Nowadays, a modular multilevel converter-based high voltage direct current (MMC-HVDC) system is a promising topology for connecting the offshore wind farm to the grid because of its modular characteristics and low switching frequency operation. Moreover, it does not require an expensive filter as it can generate almost pure sine waves. The energy storage system is used to improve the transient performance of MMC-HVDC and to manage the surplus energy in the HVDC system. Moreover, it is used to offer auxiliary services to the electrical grid. The lithium-ion battery energy storage system is used widely because of its superior performance in terms of high energy density and stability. A DC-DC boost converter is used as an interface between BESS and MMC-HVDC. This paper proposed a control strategy for a DC-DC boost converter based on artificial neural network-based proportional integral control (ANN-based PI). Firstly, the proportional-integral (PI) control method is used as an expert system; then, the dataset is utilized for training the ANN. To prove the effectiveness of the proposed control method, the system undergoes testing in the presence of a three-phase to ground fault near the grid, which is then compared to the performance of the PI controller. Furthermore, the performance of ANN is measured using MATLAB/Simulink®.
Pretty-Simple Password-Authenticated Key-Exchange Under Standard Assumptions

In this paper, we propose a pretty-simple password-authenticated key-exchange protocol, which is proven to be secure in the standard model under the following three assumptions. (1) DDH (Decision DiffieHellman) problem is hard. (2) The entropy of the password is large enough to avoid on-line exhaustive search (but not necessarily off-line exhaustive search). (3) MAC is selectively unforgeable against partially chosen message attacks, (which is weaker than being existentially unforgeable against chosen message attacks).
MicroNET: Microarray Image Orientation Predictor and Automated Data Extraction

Microarray is a high-throughput platform consisting of several thousands of DNA, protein, or peptides on a chip surface. In the current scenario, researchers have to use a scanner such as GenePix®4000B to scan such chips. However, after scanning these chips, researchers need to manually adjust the orientation angle of the grid to be placed in the microarray image before moving on to the spot intensity extraction step and data analysis. This paper introduces a novel lightweight deep-learning model, MicroNET, to predict the orientation angle and top left coordinates of the grid. After the image is corrected and the grid is placed, we also perform automatic fluorescence intensity extraction. This paper has compared two different configurations of MicroNET. The first one makes use of a ResNet18 backbone while the next one is a modified version of the ResNet18 backbone with lesser parameters. As per our results, the model with the modified ResNet18 backbone predicts the orientation angle with an MSE of 0.379 and the top left coordinates with an MSE of 0.0011. Thus the model with the modified ResNet18 backbone achieves better results with a lesser number of parameters. Hence it can be easily executed on any microarray data extraction software and will enable researchers to prepare microarray datasets easily.
Framework to Drone Part Selection, Verification and Fabrication of UAV for Multiple Applications

Drone technologies have become an essential tool in multiple industries and are a hotspot for researchers to explore the plethora of its applications. Unmanned Aerial Vehicles (UAVs) have revolutionised the technology in farming, defence, photography, industry, payload delivery and disaster management. Their ability to have onboard computing power and a multitude of sensors is incredibly beneficial. However, choosing the parts to build a custom-made drone to cater to the specific requirements of the task is a tedious process and requires much research. This paper intends to serve as a guide to choosing the layout configuration, electronic and hardware components. It discusses all the technicalities and calculations required to choose parts, making the daunting process of parts selection straightforward and methodical. It is novice-friendly and begins with a discussion of the types of drones and their constraints; rigorous calculations are explained to cater to the application’s specific requirements. The paper will serve as a one-stop guide for students, researchers and entrepreneurs to build their own drones.
Accelerated Text Data Augmentation Using a Paraphrase Generation Model with Round-Trip Translation as a Supervisor

Machine learning requires large amounts of data to improve model performance. However, preparing them is costly, so data augmentation is often used to generate new data from existing data. In natural language processing, there is a text data augmentation technique called round-trip translation, which translates text data into another language and then translates it back into the original language to generate a paraphrase of the original text. However, the round-trip translation is computationally expensive and time-consuming because it requires two translations for one text. In this paper, we propose a faster text augmentation method using a model trained to make the round-trip translation. The dataset of this training consists of original texts and the results of their round-trip translation. Text- To- Text Transfer Transformer (T5) is adopted as the model. Experimental results show that the proposed method can augment text data about 4 times faster than round-trip translation while maintaining the same level of accuracy improvement for text classification tasks.
SimIoT: A Simulator for Verification and Profiling of Complex IoT Deployments

The proliferation of Internet of Things (IoT) technologies in various industrial sectors has brought forth new challenges that demand attention for achieving technological maturity. One such challenge is the lack of tools for emulating the diverse components present in IoT architectures, leading to the continuous verification of each component in the chain, which proves to be a complex task. This paper addresses the verification problem in Edge/Fog IoT platforms through comprehensive end-to-end testing. To tackle this challenge, we have developed a modular IoT simulator (SimIoT) capable of efficiently emulating thousands of IoT devices in realistic scenarios, including hospitals, airports, and smart cities. The simulator allows testing of Edge platforms without the need for programming expertise. Furthermore, we demonstrate the feasibility of our simulator by presenting a use case involving the profiling of an open-source IoT platform.
Low-Cost Power Generating and Discharge Brick

The creation of a low-cost piezoelectric power producing brick is a ground-breaking idea introduced by this work in response to the growing demand for sustainable energy sources and the urgent need for creative approaches to urban infrastructure. This ground-breaking brick smoothly incorporates piezoelectric components with conventional building components, enabling the effective absorption of mechanical energy produced by stairwells and pedestrian foot traffic. This invention offers a financially sound and environmentally beneficial method for generating energy by utilizing the piezoelectric phenomenon, which transforms mechanical stress into electrical energy. The study conducted here explores a number of important facets of this project, including the careful selection of appropriate piezoelectric materials, their incorporation into the structural design of the brick, and the development of an optimized blueprint that maximizes energy conversion while preserving the structural integrity of the brick. The primary objective is to establish a seamless synergy between energy production and the built environment, providing a sustainable route to addressing the future energy requirements of metropolitan areas. By doing this, we want to lessen the need for conventional power generating techniques and the pollutants they produce. This project's ability to empower communities is one outstanding feature. The concept of building sidewalks out of these novel piezoelectric bricks gives society the chance to actively participate in energy conservation for future generations while also generating power. This strategy fosters a sense of accountability for sustainable energy practices and helps society live a better lifestyle. In essence, a brighter, cleaner, and more self- sufficient urban future where energy generation becomes a necessary element of daily life is made possible by the low-cost piezoelectric power producing brick. This strategy fosters a sense of accountability for sustainable energy practices and helps society live a better lifestyle. In essence, a brighter, cleaner, and more self-sufficient urban future where energy generation becomes a necessary element of daily life is made possible by the low-cost piezoelectric power producing brick.
Mobile Photography Guidance System via Aesthetic Assessment

Mobile photography guidance has emerged as a highly regarded research area within the field of human-computer interaction. With the widespread adoption of smart-phones, individuals increasingly rely on the built-in cameras of their mobile devices for photography. However, the majority of users lack knowledge on how to capture aesthetically appealing photographs, which poses a significant challenge within the realm of human-computer interaction. To assist users in enhancing their photography skills, we designed a robust and dependable aesthetic evaluation model based on a large-scale dataset collection. This model has been deployed on mobile devices to create a photography software known as “AI Photography Tutor”. This mobile application offers real-time feedback, presenting information about light, color, composition, and overall aesthetic scores to guide users in capturing more artistically appealing photos. To evaluate the performance and guidance effectiveness of this application, we conducted comparative experiments with three other photography applications employing different guidance methodologies. We performed both subjective and objective analyses of the experimental results. The findings from these experiments demonstrate that our proposed photography guidance approach, along with the mobile application developed based on it, significantly enhances the photography skills of smartphone users. Furthermore, results from user satisfaction surveys indicate that “AI Photography Tutor” surpasses other applications in terms of interactivity, usability, applicability, improvement in photography results, and overall satisfaction. We believe that this innovative application holds the potential to become an essential tool for photography enthusiasts, offering robust support for users to capture higher-quality photos.