Miracle-dz/UniFAR-FTUs huggingface dataset:
Half Euler Spiral Nanoantenna based Smart City Compatible Thin Film Solar Cell

A theoretical investigation on modification of a simple strip nanoantenna to overcome the polarization mismatch problem is presented. The modified design half Euler spiral nanoantenna (HESNA) is proposed along with its possible application aspects for thin solar cell (TFSC) performance enhancement. The performed study reveals that Half Euler spiral nanoantenna equally responds to the both the orthogonally polarized component of the impinging light with significant local field enhancement as a result of localized surface plasmons (LSP). The generation of LSP is explained with the help of tangential magnetic field component. The nanoantenna design also exhibits relatively larger energy confinement compared to its parent nanostrip design. The presented study on the HESNA describes that the polarization mismatch loss can be reduced by introducing spiral bend on one of the sides of the nanostrip. The study has been performed on CST Studio suite. Half Euler spiral nanoantenna incorporated thin film solar cell exhibits 66% absorption enhancement with 65% increased photocurrent.
Advanced Ship Detection System using Yolo v7

Accurate ship detection is essential for a wide range of maritime applications. This research paper presents an innovative ship detection method that integrates the YOLOv7 (You Only Look Once) object detection algorithm with the Jetson Nano platform. The proposed methodology combines the power of YOLOv7’s real-time and accurate ship detection capabilities with the efficient inference capabilities of the Jetson Nano platform. The YOLOv7 model is trained and evaluated on two datasets: MASATI v2 and ShipRSImageNet. The results demonstrate impressive accuracy and computational effectiveness, with mAP values of 0.76 and 0.93 on MASATI v2 and ShipRSImageNet, respectively. Additionally, precision values of 0.8 and 0.96, and recall values of 0.77 and 0.84 are achieved on the respective datasets. The integration of the trained model with the Jetson Nano platform enables efficient ship detection in real-time on live video streams. This research emphasizes the significance of appropriate model architecture and training strategies, showcasing the potential of the proposed method for accurate ship detection in dynamic marine environments and high-resolution remote sensing images. The findings of this study contribute to advancements in ship detection technology and its applicability in various maritime domains.
Modified Lesk approach for Word Sense Disambiguation in the Marathi Language

The process of computationally determining the meaning of a word in a certain context is known as Word Sense Disambiguation (WSD). One of the most challenging tasks in Natural Language Processing is WSD. In terms of tools and datasets, Marathi is a low-resource language. As a result, there hasn’t been much research undertaken on the Marathi language. One of the most popular methods of the knowledge-based approach for WSD is the Lesk algorithm, which uses WordNet instead of the conventional dictionary as the sense inventory. In this paper, we present a modified Lesk algorithm that considers all the words in the given sentence except the ambiguous word as well as the sense, synonyms, and examples of the words to the left and right of the target ambiguous word as the context for each sense of the ambiguous word. This makes it possible to ascertain the correct meaning of ambiguous words given the context. Nouns, verbs, adjectives, and adverbs can be disambiguated using the proposed method.
Reflection multi‐scale envelope inversion

Sufficient low‐frequency information is essential for full‐waveform inversion to get the global optimal solution. Multi‐scale envelope inversion was proposed using a new Fréchet derivative to invert the long‐wavelength component of the model by directly using the low‐frequency components contained in an envelope of seismic data. Although the new method can recover the main structure of the model, the inversion quality of the model bottom still needs to be improved. Reflection waveform inversion reduces the dependence of inversion on low‐frequency and long‐offset data by using travel‐time information in reflected waves. However, when the underground medium contains strong contrast or the initial model is far away from the true model, it is hard to get reliable reference reflectors for the generation of reflected waves. Here, we propose a combination inversion algorithm, i.e., reflection multi‐scale envelope inversion, to overcome the limitations of multi‐scale envelope inversion and reflection waveform inversion. First, wavefield decomposition was introduced into the multi‐scale envelope inversion to improve the inversion quality of the long‐wavelength components of the model. Then, after the initial model had been established to be accurate enough, migration and de‐migration were introduced to achieve multi‐scale reflection waveform inversion. The numerical results of the salt‐layer model and the SEG/EAGE salt model verified the validity of the proposed approach and its potential.
A Comprehensive Analysis of UAV Collision Avoidance Techniques for Enhanced Aerial Safety

Drones, otherwise called Unmanned Aerial Vehicles (UAVs), are as of now normal in a wide range of enterprises and have changed jobs like bundle package delivery and surveillance. In any case, given the immense number of UAVs in the skies today, keeping up their safe coexistence with different drones is of the best concern. This paper explains this significant topic by offering a definite investigation of generally applied UAV collision avoidance techniques. Collision avoidance, which immensely affects all parts of the course and flight execution, is a pivotal piece of UAV path planning. Five primary techniques are covered in this paper: Proximity sensors, GPS-based systems, Communication and Tracking, Sense and Avoid Techniques, and Autonomous Decision-Making Algorithms. To reveal insight into every technique's efficiency, the review looks at its advantages and downsides. Understanding and further developing collision and Obstacle Avoidance Techniques is significant for the continued protected and productive activity of these aerial vehicles, and they play a significant part in UAV path planning as UAVs become more coordinated in our airspace.
Disaster Tweet Classification using LSTM: A Comparative Study of Imbalanced Data Handling Techniques

This research explores the impact of three balancing methods (SMOTE, Random Over-Sampling, and AdaSyn) on LSTM model accuracy in disaster tweet text classification. It aims to identify the most effective data balancing technique to enhance the LSTM algorithm’s performance. The study compares LSTM model accuracy using different balancing methods and determines the optimal approach for precise disaster tweet classification. The combination of these approaches enables effective processing of sequential data, enhancing the overall analysis of tweets in disaster scenarios. The LSTM algorithm, without balancing, achieved 81.6% average accuracy across ten tests on disaster tweet data. When combined with balancing methods, accuracy ranged from 81% to 81.1%. These findings demonstrate the LSTM algorithm’s strong performance in classifying disaster tweets, with a slight accuracy reduction when using balancing techniques. The research concludes that using the LSTM algorithm for text classification of disaster tweets without balancing the dataset achieves higher accuracy. However, further research is required for algorithm enhancement and performance improvement.
Implementation of MPPT Algorithms to a Developed Photovoltaic Emulator: Study and Comparative Analysis

Photovoltaic (PV) emulators (PVEs) become a critical tool to test the PV generation system such as the maximum power point tracking (MPPT) converter and the PV system control. The latter ensures a controllable light source environment that mimics the output characteristics of a real PV panel. In this context, this paper deals with the implementation of different MPPT techniques to a developed low-cost PV emulator based on two-diode model. Mainly, The PVE structure is formed by three parts that are the PV model, the control strategy, and the power stage. In an initial phase, the PV emulator system is presented, and its three main components are described. In a second phase, three MPPT algorithms, namely, the perturb and observe (P&O) technique, the incremental conductance (IC) technique and the sliding mode control (SMC) technique are studied and applied to the developed PVE. The acquired simulation results show that the developed PVE can follow accurately the output characteristics of the PV module. Moreover, the studied MPPT algorithms allows the PVE to operate at its maximum power during irradiance and temperature variations. As compared to the P&O and the IC techniques, the SMC provides a good response in MPP tracking without considerable ripples.
Three-Dimensional Spatiotemporal Reconstruction and Feature Analysis of Table Tennis Movement Enhanced by Multi-view Computer Vision

This study provides post-competition multi-dimensional digital analysis for table tennis players through multi-view computer vision technology. The three-dimensional skeleton key point data is used for action recognition, and the target tracking and binocular camera technology are combined to accurately reproduce the motion trajectory in the table tennis competition. By integrating the key point data and three-dimensional coordinates of the skeleton, the three-dimensional reconstruction of the game scene is carried out by using the illusory engine. The experimental results show the potential of this method in the analysis of table tennis technique and tactics, and provide a new digital solution for post-competition analysis.
A Comparative Study of Text-Based Emotion Detection Techniques for Emotion Recognition on Social Media Data

Nowadays, with the rise of the internet social media have emerged as a new platform that provides a large amount of user-generated content that can be analyzed or mined to detect the emotions of the users. However, advancement in technology allows users to share their views through audio/video, speech, and facial expressions but people mostly use text for communication over social platforms. Hence it is required to automatically recognize emotions from these posts and provide responses to users in today’s online world. Emotion Detection from text is a popular topic in Natural Language Processing (NLP) due to its vast applications in human-computer interaction, artificial intelligence, marketing, etc. Researchers from different domains have proposed algorithms for emotion detection from the text; still, no method gave a complete solution. Hence, this paper aims to explore several emotion detection techniques such as machine learning (ML), Deep learning (DL), and hybrid ensemble techniques to detect emotions from social media conversations. Then these different classifiers are analyzed, tested, and compared with each other based on different parameters namely F1-score, recall, precision, accuracy, and confusion matrix. The classifier that gets the highest score among all other classifiers is termed as best text-based emotion classifier.
Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques

Brain tumor classification from MRI image is one of the important areas in biomedical research. However, the interpretation of these images is subjective, and automated detection methods can improve diagnostic accuracy. This study proposes a hybrid model that combines deep learning, machine learning, and traditional image processing techniques to detect brain tumors from MRI images. The proposed model is based on a convolutional neural network (CNN) algorithm and a machine learning model. The first stage is employing the histogram equalisation approach to enhance the image. In the second stage, features of the grey level co-occurrence matrix are extracted from the improved image. Convolutional neural networks are utilised in the third stage to categorise as normal or abnormal. The abnormal brain tumor is segmented using a morphological segmentation algorithm. This study suggests a number of performance criteria, such as accuracy, sensitivity, and specificity, to evaluate the efficacy of the proposeed method. Our proposes algorithm demonstrates superior performance compared to existing methods regarding accuracy, sensitivity, and specificity.
Active Contour-Based Analysis of Stent Characterization in Cardiac Coronary Arteries from 2D Intravascular Ultrasound Images

According to the World Health Organization, there are 17 million deaths from heart failure worldwide, with 8.7 million, or 31%, of them being caused by coronary heart disease. This disease occurs when the coronary arteries are blocked by a buildup of fat, which can lead to heart failure. One way to cure this disease is to perform stent surgery. To prevent more severe symptoms, early diagnosis and monitoring after stenting surgery are needed. To help physicians determine the diagnosis of this disease, a modality that reduces the risk to patients used in this study, the malposition of the stent. Images are created via Intra Vascular Ultrasound (IVUS) using ultrasound waves. Because of its non-invasive, real-time, and inexpensive nature, ultrasound imaging has several advantages over other imaging processes in the medical field, Ultrasound can produce excellent image quality. Doctors can also take advantage of ultrasound imaging to evaluate disease and reduce the higher risk of disease. However, the ultrasound image output still has drawbacks, one of which is the crackling sound which reduces the quality of the results, especially for images with low contrast. There are several methods that can be used to reduce the resulting noise; in this study, the Median Filter and Mn Filter methods are used; the Canny Edge Detector method is used to obtain the boundaries of the edges and contours of the heart artery images generated by the filter; and Active Contour, which adds features for measuring stent radius and area. Evaluation methods such as Mean Square Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are used in this study. The results showed that the software can characterize the stent, which makes the stent contour clearer and easier for the doctor to see, so that the doctor can make a diagnosis more easily.
Implementation of Number Transcription Tool

The creation of a precise speech-based number transcription tool that accurately transforms spoken numbers (0–9) into their corresponding digits on a screen has become extremely important in today's digital world. In order to accomplish error-free and reliable transcription of numerical data, a system using advanced speech recognition technologies was designed and implemented, as revealed by this paper. The system uses a combination of machine learning models that have been combined and trained on large datasets to provide greater transcription accuracy and adaptability to different speech patterns. This paper thoroughly explains the system's technological architecture, highlighting its features and functionalities, with particular emphasis on its capacity to convert spoken numbers into their corresponding numerical representations on a visual interface. The paper also consists of a table which provides a brief summary of the benefits and drawbacks of several models for automatic speech recognition number transcription including hybrid DNN-HMM systems, noise-robust techniques, LAS models and a comparison of our system with these existing systems. The various applications of this system include finance, telecommunications, and data entry, where precise number transcription is of utmost importance.
Performance Analysis of the Blockages Impact on Resource Allocation in Mm-Wave Cellular Systems

The paper explores the impact of blockages on resource allocation in Mm-Wave cellular systems by applying a classical handover (HO) scheme. The scheme adjusts the number of reserved channels dedicated exclusively for the HO requests based on a predefined threshold of the probability of blocking HO calls to ensure efficient resource allocation and meet the Quality of Service (QoS) requirement. The study finds that an optimized and balanced scheme between the blocking probability of HO calls and the blocking probability of originating calls in the mm-wave BS is necessary. The results indicate that a higher number of reserved channels is required to satisfy the minimum threshold of the blocking probability of HO calls
Design and Development of Exploratory Model in AI for Addressing CO2 Emission for a Sustainable Future

The global challenge of carbon dioxide (CO2) emissions and their environmental impact form the core focus of this research endeavor. Through an exploration of various visualizations and analyses, this study delves into the intricate dynamics surrounding CO2 emissions, providing insights into global trends, country-specific trajectories, and the imperative for sustainable practices. Beginning with an overview of worldwide CO2 emissions, the study uncovers the exponential growth trajectory, highlighting the urgency for proactive measures to address this issue. The research further dissects the top 10 CO2 emitting countries over the last decade, emphasizing the importance of collective efforts in emission reduction. Shifting the lens to India's emissions, the study examines the century-long journey of CO2 emissions, capturing the nuanced shifts from the early 20th century to the present day. The graph's portrayal underscores the pivotal role of industrialization, urbanization, and demographic changes in shaping India's carbon footprint. The turning point around 1960 signifies a transformational moment that propelled emissions on a steep upward trajectory. Economic growth emerges as both a driving force and a challenge. The narrative concludes with an exploration of future research avenues, spanning alternative energy sources, policy frameworks, technological innovations, behavioral changes, and inclusive strategies. This study underscores the complexity of the emissions landscape, weaving together historical insights, present-day challenges, and a visionary perspective for the future. As societies navigate the intricate balance between development and environmental responsibility, this research serves as a compass, guiding us toward a world where CO2 emissions are not just numbers but a collective call to action for a sustainable and harmonious future.
Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics

Sustainable features are dependent on vital climatic elements that has a prominent impact on the retention of sustainability provided its metrics are in desired domain. Regression analysis and ensemble learning models are some of the predictive analytics methods which were used to detect the association of every feature on sustainable criteria. Weather samples from Delhi during 1970–2020 is used in the research which considers features like humidity, pollutant level, temperature etc which are gathered from several authenticated sites like pollution management unit of India. After analyzing several elements affecting weather endurability, it is noticed that pollutant level and temperature exhibit the highest significance recording 30% and 44% respectively. Also the R-square metric of 86% and 82% was observed with implementation of analytics models. The major conclusion recorded that random forest outperformed regression model and it established the importance of predictive analytics in predicting sustainability results. The research validated the relevance of climatic tracking for regulating sustainability.
Detecting Fake Reviews Using N-gram Model and Chi-Square

Online product reviews are increasingly important in consumer purchasing decisions. They tend to refer to product or service reviews online before purchasing. Therefore, conducting more in-depth research in detecting fake reviews is essential. This research utilizes the n-gram model, especially bigrams and trigrams. The results of these two n-gram models were selected using the chi-square method. Next, the selected feature results are applied in three machine learning algorithms: Support Vector Machine, Random Forest, and Logistic Regression. SVM and Logistic Regression algorithms are suitable for use in binary classification, while Random Forest has the advantage of dealing with variations in the language and writing style of fake reviews. The best results in this research were obtained using the Support Vector Machine algorithm combined with trigrams and the chi-square method, with a value of K=1000. These results achieved an accuracy level of 92.19%. This achievement far exceeds previous research, which only achieved an accuracy of 88.75%. These results show that the approach of using word context and careful feature selection in detecting fake reviews improved the previous accuracy by 3.49%, even with the same algorithm.
Construction of An Intelligent Sports and Health Management Platform for Colleges from the Perspective of Sports–Medicine Integration

Prevention of chronic diseases among college teachers is a crucial part in the national initiative to build a healthy China. This paper proposes the construction of an intelligent sports and health management platform for colleges, with a view to addressing chronic diseases among teachers from the perspective of sports-medicine integration. This study highlights that developing a sports health management platform is an endeavor with great significance of the era. With the resources of colleges, advanced technologies, such as big data processing, quantitative integration of health information, visualization, and Internet of things (IoT) monitoring, can be integrated to create an intelligent health management platform for college teachers. It is crucial to establish a scientific sports health management service framework for the proper functioning of a sports health management platform. The platform not only addresses the health concerns of college teachers, but also serves as a model for promoting healthy campuses in Chinese colleges.
Practical Random Tree Generation: Information-Theoretic Analysis and Applications to IoT

Tree structures make an appearance in many network related problems, such as network routing. Modelling and simulating the appearance of these data structures can be done using random tree generators. However, there has been very little study on random models that are able to capture the dynamics of networks. We introduce the random spanning tree model, which is a random tree generator that is based on generating a tree from an already existing network topology. The Shannon entropy of this model is then analysed, and upper bounds to it are found. As compression can be beneficial because of the complexity of large trees, we then introduce a universal approach to compressing trees generated using the spanning tree model. It will be shown that the proposed method of compression introduces a redundancy that tends to zero for larger trees. We recommend the use of this random model and the proposed compression algorithm in routing protocols of IoT, as it can contribute to saving routing communication overhead and increasing the overall lifetime of the network.