Miracle-dz/UniFAR-FTUs huggingface dataset:
Unleashing the Power of Generative Artificial Intelligence: Exploring its Boundless Potential and Overcoming Challenges in Academic Environments

Integrating generative artificial intelligence tools can allow one to manage and develop novel business methods efficiently. With the introduction of self-learning generative artificial intelligence (GAI) tools, the corporate world is trying to figure out the applications and their business implications. Major technology companies like Microsoft, Google, Facebook, etc., have shown enormous interest in investing in ventures developing generative AI tools/models. These tools/models can be experimented with, and their business use is being explored. The study aims to explore the potential uses of generative AI (GAI) in academics through its current capabilities and applications. The study also highlights the potential challenges and concerns arising from using generative AI in academics. The study has used descriptive and quantitative methods to answer the research questions. The study has used a questionnaire based on the Likert scale to measure the significance of indirect variables, viz. perceived ease of use (PEOU) and perceived trust (PT) on direct variable adoption of generative artificial intelligence (AGAI) in academics. The sample size consists of 100 students and 100 university professors. The study Mentions that perceived ease of use (PEOU) positively influences GAI (AGAI) adoption. Also, perceived trust (PT) has a predictive ability of AGIA when controlling for PEOU. The use of generative AI in a few years will become imperative in most human lives, which makes it mandatory to explore the possible ways of its adoption in the mainstream rather than avoiding or restricting its usage in academics.
Hybrid Model and Ensemble for Inflation Forecasting: A Machine Learning Approach

An inflation index is a statistical tool that measures the change in the general price level of goods and services in an economy over a specific period, typically used to gauge the inflation rate. Understanding such an index is vital because it measures the overall price level changes in an economy over time, influencing policy decisions, investments, and the real value of money, and thus, the general economic well-being of society. This article presents a study on the forecast of the official inflation index in Brazil, IPCA, using a hybrid system with bagging. The proposed model combines a linear approach (ETS) with the bagging technique applied to residual forecasting. Thirteen relevant methods mentioned in the literature were evaluated, including widely used models such as ARIMA, ETS, MLP, SVR, RF, and various hybrid combinations. The experimental results show that the proposed hybrid model (ETS+Bagging) outperformed individual models and other hybrid systems. Furthermore, the bagging approach significantly improved the results compared to the ARIMA and ETS models, as measured by metrics such as mean squared error (MSE), root means squared error (RMSE), and mean absolute error (MAE). The results indicate that the hybrid system with bagging, especially the proposed ETS+Bagging model, has the potential to enhance inflation forecasts using IPCA data.
Convolutional Neural Networks for Early Detection of Forest Fires

Forest fires are a major concern for environmental conservation and public safety. Early detection of forest fires is crucial for timely and effective control measures. In recent years, Convolutional Neural Networks (CNNs) have been widely used for detecting forest fires in real-time using images captured by cameras installed in forest areas. The proposed system consists of three main stages: image preprocessing, feature extraction, and classification. The images are preprocessed to enhance the fire-related features and applied CNNs to extract the features from the images. The proposed CNN model is trained by using a large dataset of images and tested the model on various real-world scenarios. The results demonstrate that the proposed system has achieved high accuracy in detecting forest fires, and it can detect the fires with minimal delay. The proposed system can be deployed in forest areas for continuous monitoring and early detection of forest fires, which can significantly improve the response time of the firefighters and reduce the damage caused by forest fires.
Neutron Gamma Discrimination Performance with Plastic Scintillator According to SNR, Vertical Resolution and Sampling Frequency

The hardware design of an acquisition chain for gamma neutron discrimination is based on trade-offs between a large number of parameters. This paper describes the influence of the resolution, sampling frequency and Signal to Noise Ratio (SNR) of a digitizer on the performance of neutron gamma discrimination with EJ276 plastic scintillator. Thus, the study can help the user to select an adapted combination between these three parameters to obtain a good discrimination performance. The obtained results show that the SNR has the most important impact on the minimum sampling frequency required to perform the discrimination. The frequency can be reduced to 250 MHz if the SNR is higher than 4.
Optimal_Operation_of_Electro-Hydrogen_Coupled_Distribution_Systems_Considering_Comprehensive_Demand_Response

Hydrogen energy, as flexible and clean secondary energy, has gradually become an important support for flexible accommodation of renewable energy resources in the distribution network. In view of existing distribution system operation optimization study of multi-type hydrogen load flexible adjustment potential shortage, analyze transferable characteristic of industrial and traffic hydrogen load. To improve the consumption capacity of sustainable energy as well as operational economic efficiency in distribution power system, an optimal operation model of electro-hydrogen coupling distribution system has been established considering the comprehensive demand response. The proposed comprehensive demand response strategy is able to well maintain the co-ordination of power supply and demand, improve the ability of the distribution system to absorb sustainable energy, and promote flexible and operational economics of distribution network.
A Positive and Negative Crosstalk Suppression Method for High Current Bridge Configuration

The wide band-gap (WBG) devices have received widespread attention due to their excellent characteristics like low on-resistance, fast switching speed and better thermal conductivity. However, the crosstalk problem restricts the performance of high-speed WBG transistors in bridge-leg converters. What's more, in high current converters, the crosstalk problem will become more severe. To overcome the crosstalk problem in high current converters and fulfill the WBG devices' high-speed potentials, a simple positive and negative crosstalk mitigation method is presented in this article. This method does not require extra auxiliary controlled switches and extra control strategies, and can mitigate the positive and negative crosstalk spikes. What's more, it is simple implementation, and can be easily integrated with the conventional voltage source gate drivers. The effectiveness of the presented crosstalk suppression method is proved by the simulated and experimental outcomes.
High-Frequency Resonance Suppression Based on Active and Passive Joint Damping for MMC-HVDC

High-frequency resonance (HFR) incidents have occurred in several Modular Multilevel Converter-based high voltage direct current (MMC-HVDC) projects recently. The interaction between the impedance characteristic of MMC and the grid is assumed as the main factor of HFR, caused by a large time delay. Complex operating conditions arouse the issue of resonance frequency shifts, making the conventional suppression method ineffective. Thus, an active and passive joint damping suppression method is proposed based on the cross-coupled filter (CCF) and improved C-type filter damper. This method can remove the risk of HFR and provide a sufficient phase margin, enhancing the stability and robustness of the MMC-HVDC system. The effectiveness of the proposed method is verified through simulations.
Probabilistic Power Flow Analysis of Distribution Networks Considering Multiple Photovoltaic Reactive Power Control Modes

The voltage fluctuations caused by the uncertainty of PV generation can be addressed by utilizing the residual capacity of PV due to the voltage regulation ability of reactive power control. To effectively manage voltage fluctuations, appropriate PV inverter control modes should be selected according to different grid conditions. Therefore, different inverter control modes are considered for distribution network power flow analysis. Firstly, the PV generation model is established, and three reactive power control modes for the inverters are proposed. Then, considering the correlation of PV generation, probabilistic power flow analysis is performed for the distribution network under different control modes. Finally, the IEEE 33 Bus distribution network with PV is built in simulation and the results show that droop control is more suitable to manage the fluctuations of PV system and the constant reactive power control can be employed when the system is relatively stable.
Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network for Autonomous Vehicle Drivers

Facial Emotion Recognition (FER) utilizing Deep Learning (DL) is indeed employed in the context of autonomous vehicle drivers. By investigating the facial expressions of drivers, the vehicle system is gain insights into the emotional state of drivers. FER is given to whole safety by identifying critical emotions like fear, anger, or surprise. When the system identifies these emotions, it responds accordingly by altering the driving performance or taking preventive measures for avoiding potential risks or aggressive maneuvers. The training of the DL algorithms namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are done on the preprocessed data. These approaches learn to extract appropriate features in facial images and forecast the same emotions. This manuscript offers the design of Automated Facial Emotion Detection using Arithmetic Optimization Algorithm with Deep Convolutional Neural Network (AFED-AOADCNN) technique for Autonomous Vehicle Drivers. The purpose of the AFEDAOADCNN technique is to detect various kinds of facial emotions in autonomous vehicle drivers. In this introduced AFED-AOADCNN technique, DCNN method is applied for procedure of feature extraction. Next, the AOA is employed for optimum hyperparameter tuning of the DCNN method. At last, quantized neural network (QNN) approach is exploited for the identification and classification of different kinds of facial emotions. The investigational evaluation of the AFEDAOADCNN method is assessed on facial emotion dataset. The experimental validation stated the improved outputs of the AFED-AOADCNN method over recent approaches.
Transforming the Insurance Landscape: How ChatGPT's Proficiency Empowers the Industry

The release of the ChatGPT by OpenAI in 2022 generated interest in academia and industry, with mixed reactions to its capabilities and ethical concerns. Numerous studies have explored its accuracy and adaptability in several industries, such as healthcare, education, finance, and atmospheric science. Still, its applicability in the insurance industry has yet to be comprehensively analyzed. The insurance industry protects financially against risks like property damage, accidents, and health issues. Insurance companies offer policies to individuals and businesses; policyholders pay premiums to cover potential losses. The industry relies on efficient communication, accurate information processing, and risk assessment to effectively underwrite policies and manage claims. In summary, the ChatGPT has the potential to become a game-changing tool in the Insurance industry, driving innovation in customer interactions, process automation, and risk management. As the technology continues to evolve, its' potential to reshape insurers' operations and customer service in the digital era is immense.
Structural and Energetic Insights into SEI Precursors and Their Interactions in Lithium-Ion Batteries

This study employed DFT to probe the structural and energetic attributes of Solid Electrolyte Interface (SEI) precursors, notably ethylene carbonate (EC) and diethyl carbonate (DEC), both individually and in conjunction with ions such as Li<sup>+</sup> and PF<inf>6</inf><sup>−</sup>. Distinct changes in molecular properties such as bond lengths, angles, and dipole moments were observed upon interaction with these ions. Notably, the PF<inf>6</inf><sup>−</sup> anion profoundly influenced the solvation structures and molecular geometry of Li<sup>+</sup>(EC) and Li<sup>+</sup>(DEC). Electron behavior in these solvents was explored with an emphasis on the LUMO energy level, revealing that both Li<sup>+</sup>(EC) and Li<sup>+</sup>(DEC) possess robust electron affinities conducive to SEI formation. Comprehensive solvation energy evaluations indicated a synergistic energy enhancement when using EC and DEC together, although this was significantly attenuated in the presence of the PF<inf>6</inf><sup>−</sup> anion. Further insights from Gibbs free energy assessments underscored the structural stability of the Li<sup>+</sup>(EC) complex, particularly without PF<inf>6</inf><sup>−</sup>. Complementing these findings, adsorption studies on bilayer graphene illustrated that the introduction of lithium amplifies adsorption energy, highlighting its pivotal role in the SEI formation in lithium-ion batteries. Nevertheless, the incorporation of PF<inf>6</inf><sup>−</sup> decreased this adsorption energy, suggesting a mediating effect between Li<sup>+</sup> and PF<inf>6</inf><sup>−</sup>in their interaction with graphene. Collectively, this work elucidates the multifaceted interactions and potential implications of these solvents and ions in SEI layer development.
An Improved LLC Topology with High Fault Tolerance Ability

LLC resonant converters are widely used in various fields due to high power density and high efficiency. However, when a switch failure happens in LLC, the converter is unable to maintain the normal output voltage gain while achieving high efficiency at resonant point. To make it more compatible with fault occasion, this paper proposes an improved LLC topology that ensures the output voltage remains unaffected when a primary switch failure occurs. Moreover, it can operate near the resonant point, maintaining a high level of efficiency. Additionally, an optimized burst control is added to effectively suppress the overvoltage and oscillation of the resonant capacitor during the fault tolerance transition. Simulation verifies the effectiveness of the proposed method.
Brain Tumor Detection using CNN

- Tumor is an abnormal growth of cell in human brain which is fatal if treatment is not provided at right time. Various imaging technique such as MRI, ultrasound are used to detect the tumor. Earlier, initial stage of tumor were detected manually through observation of image by doctors and the results obtained were sometimes inaccurate and process was time consuming due to large volume of data generated by MRI Scan. Automatic classifications methods are required for proper diagnosis of tumors which can be provided by image processing techniques. Image processing techniques involves four stages such as feature extraction, image segmentation, image pre-processing. In this paper a classifier based on a convolution neural network (CNN) is considered. Experimental result shows that CNN provides 97.33% accuracy.
4 Port MIMO Antenna Enhanced Isolation And Gain By Using Frequency Selective Surface

In this research a small 4 port multi input multi out (MIMO) antenna is presented working in the frequency range between 4.6 GHz - 7.3 GHz for fifth-generation (5G) communication. The antenna maintains a gain of 7.75 dBi at 5.2 GHz and 5.3 dBi at 5.8 GHz while having a wider working bandwidth of 2.7 GHz. The suggested antenna system comprises a four-element orthogonally arranged with a frequency selective surface below the height of 15 mm for enhancing the gain. The presented MIMO system has the following dimensions: 50.5 mm x 50.5 mm x 1.6 mm. Radiating elements in the suggested MIMO design are arranged orthogonal to one another to achieve good isolation. Additionally, the isolation between each element is below -22 dB in the entire frequency range. High diversity gain, minimal envelope correlation coefficient are further characteristics of the suggested MIMO antenna design.
Deception Detection in the Digital Age: Pioneering Linguistics and Vision

The cutting-edge methodology for deception detection presented in this research study employs Deep Learning (DL) and Natural Language Processing (NLP) methodologies. Long a challenging and significant area of study, deception detection has applications in security, law enforcement, and interpersonal communication. In this study, a unique method for using neural networks in conjunction with Natural Language Processing (NLP) and speech processing is discussed to distinguish between dishonest and truthful statements. Our approach explores the distinguishing elements in text data and the acoustic properties ingrained in spoken language. This has the intention to provide a deception detection technique with significant potential in real-world circumstances by combining these two modalities. This research opens up new possibilities for things like fraud detection, security screening, and perhaps even bettering human-AI interactions. Our novel technique represents a substantial advancement in the field of deception detection and provides useful insights into the possibilities of AIdriven solutions in dealing with complicated human behaviors.
Smart Surface-Aided Wireless Networks with Joint Active-Passive Beamforming For Enhanced Signal Coverage

Clever mirrored surfaces are a ground-breaking and innovative technique for future value frequency and environmentally friendly cellular transmission. In particular, an is made up of lots of inexpensive inductor, each of which is capable of autonomously reflecting the received signal with a programmable phase change to jointly accomplish multi detached subcarrier without the assistance of radio broadcast networks. In this article, we investigate a separate wirelessly system which employs an IRS to help with transmission between one number of co ap and a handful of single-antenna users. By good agreements the send modulation schemes by effective antenna elements and reflect spatial multiplexing by silent power switches, pursuant to users' particular transmission ratio restrictions, we devise and resolve new issues to reduce the total system throughput. In addition, we examine the dynamical properties of passive directional antennas with indefinitely many expressing components and contrast it with the behavior of the conventional active modulation schemes. According to the findings of the experiment, a Genuineness (openness and self-system is capable of achieving so same class performance as a baseline large network without the use of IRS while using a great deal fewer active signal boosters' network. We also gain information into how to best implement in upcoming wireless networks.
Enhancing Road Visibility by Real-Time Rain, Haze, and Fog Detection and Removal System for Traffic Accident Prevention Using OpenCV

Nowadays, both the corporate and public research sectors are interested in autonomous vehicles. Levels of unpredictability are the reasons self-driving cars have not yet made it to the consumer market. Multiple sensors are frequently used to address this, which helps the vehicle's system become more robust. The most often used sensors are radars, lidars, and cameras, but their costs can increase quickly, making them unaffordable in some markets. Utilizing fewer but stronger sensors for visualization could help with this. This resolves the issue of the decreased view range caused by rainy weather, a specific failure mode for camera sensors. The state-of-the-art object identification with distance estimation technique, You Only Look Once (YOLOv3), is tested with the Kalman filter and discrete wavelet transform with bilateral filtering as rain, haze, and fog removal strategies. With YOLOv3, filtered movies in daytime and dusk conditions were tested, and the results reveal that the accuracy has not increased sufficiently to be valid for use in autonomous vehicles. The study field has potential and thus indicates that more object identification and distance estimate techniques be considered as future work.
An Improved XGBoost-based Transient Stability Assessment Method for Power System

This study introduces an improved XGBoost-based algorithm, which enhances the XGBoost algorithm by including the EFL loss function to handle the problem of sample imbalance of the transient stability assessment in power system. First, the XGBoost model is strengthened by the addition of the Enhanced Focal Loss (ELF) function, which significantly lessens the negative effects of the imbalance problem between stable and unstable samples on learning. The EFL-XGBoost is then trained utilizing the power system’s operational data during the elimination of the fault issue. The simulation results on the WEPRI-36 node system demonstrate that, in comparison to other machine learning algorithms, the EFL-XGBoost algorithm has better robustness in the case of sample imbalance and has a higher recognition rate for the unstable samples while maintaining a higher overall evaluation accuracy; compared with the Focal Loss function, Enhanced Focal Loss function more significantly improves the recall ratio of model prediction; and compared with the XGBoost algorithm, the EFL-XGBoost has significantly improved the ability of identifying difficult samples.
Sentiment Analysis for Directional Stock Market Prediction: Advance Data Processing by Leveraging Multiple Data Sources

In this research, the author successfully leveraged to classify the sentiment of financial news articles, resulting in effective predictions. The approach combined various approaches and incorporated different variables, harnessing the power to make collective predictions. However, the author encountered a significant challenge in the form of insufficient data for time-series analysis. To overcome this hurdle, the author devised a solution by leveraging data from multiple sources, enabling to expand the dataset and enhance the accuracy of the predictions. The primary objective was to predict the directional movement of the SP 500 index, a crucial benchmark in the financial market. To achieve this, the author constructed a comprehensive dataset encompassing weekly historical prices, financial reports, and news articles specifically related to the SP 500's large-cap companies. This dataset spanned a substantial timeframe from January 1, 2000, to December 31, 2019, allowing capturing long-term trends and patterns. This achievement highlights the effectiveness of the approach in analyzing financial data for sentiment analysis. By incorporating a diverse range of data sources, the author was able to uncover valuable insights and patterns that significantly contributed to high prediction accuracy.
An Investigation of a Matching Circuits for Radio Frequency Energy Harvesting Applications

This paper presents HSMS 286C diode is used as a voltage doubler (VD) circuit, different types of matching circuit has been investigated for a VD rectifier circuit with a load resistance of 3 KΩ. Matching networks for efficient RF to dc voltage conversion. For lumped, transmission line, Rogers 4350 and FR4 substrates have investigated, efficiency of the rectifier matching circuits are 80.5%, 81.2%, 62% and 32.7% respectively. For transmission line matching networks, the maximum conversion efficiency has been attained, and the overall output voltage is greater than 5 volts at an input of 20 dBm with a 3 KΩ load resistance.