Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved ViT Multimodal Anomaly Detection for Electricity equipment

Abnormal condition monitoring and fault diagnosis of power transmission and substation equipment is crucial to the safety and stability of the power system, and its normal operation affects daily use, commerce and industry. The safety inspection of electrical equipment is a necessary task and the introduction of automated inspection methods based on optical and deep learning theory is a promising scientific task. Traditional recognition methods have certain requirements for human time, while the previous generation of intelligent detection methods based on color and texture have lower accuracy, only deal with data of a single mode, and rely more on the quality of data. We propose a ViT-based approach for multimodal power image anomaly detection, taking into account visible, infrared and polarized light image pairs, to achieve improved anomaly detection accuracy in a unified multimodal signal processing framework. This is one of the first applications of anomaly detection algorithms based on the Transformer framework in the field of image anomaly detection for power equipment. This paper discusses the improved network model, discusses the impact of multimodal data input for network recognition accuracy, and looks at future related work that still has potential in this area.
A Bandpass Filter with Switch Function Based on Slotline Resonator and PIN Diode

A novel bandpass filter (BPF) with the switch function is proposed. It consists of three slotline step-impedance resonators, two L-shaped feed lines, and three microstrip bridges. The microstrip bridge is loaded with a PIN diode, controlled to turn on and off. Design the coupling matrix in the ON state to meet performance indicators, and improve the OFF-state suppression (OSS) in the OFF state by loading diodes on each resonator. The final measurement shows that the center frequency of the BPF with the switch function in the ON state is 0.975 GHz, the insertion loss is less than 1.18 dB, the return loss is better than 19.9 dB, and the fractional bandwidth is 18.67%. The OSS of the passband in the OFF state is better than 46.1 dB, and it is better than 20 dB in the range below 1.458 GHz.
Integrating Big Data Analytics and Machine Learning for Predictive Maintenance within Industrial IoT Frameworks

With the proliferation of Industrial Internet of Things (IIoT) devices, there is an unprecedented growth in the volume, velocity, and variety of data generated from diverse sources. This paper presents a comprehensive study on the application of big data analytics for predictive maintenance in IIoT systems. By leveraging advanced machine learning and data analytics techniques, we propose a novel framework, Hybrid-sense that anticipates potential failures and optimizes maintenance schedules, thereby reducing downtime and operational costs. Our approach integrates multi-modal data sources, including sensor readings, operational logs, and environmental factors, to create a holistic predictive model. We employ a hybrid architecture combining both traditional statistical methods and deep learning algorithms to enhance prediction accuracy. The results, validated on real-world industrial datasets, demonstrate a significant improvement in early fault detection and maintenance prediction over existing methodologies. This research not only paves the way for more resilient and efficient IIoT systems but also offers insights into the intricate interplay between big data and industrial operations, emphasizing the transformative potential of datadriven decision-making in the manufacturing sector.
Comparison of Undersampling Methods for Imbalanced Credit Card Fraud Dataset

We use credit cards for daily transactions such as shopping, bill payments, electricity bill payments, recharge of mobiles, set-top boxes, etc. We cannot avoid using cashless payments for an easy life. For online transactions, using credit cards is the most common way. Usually, transactions are legitimate, but there is still little chance of fraud. A class imbalance issue exists as only very few transactions are fraud out of a thousand transactions. We propose a new Attribute-clustering based undersampling algorithm to rebalance the credit card fraud dataset, which is highly imbalanced. The proposed approach enhances fraud detection accuracy for the credit card dataset. For the experiments, a credit card dataset is taken from Kaggle. We have also analyzed five undersampling methods: RUS, ROSE US, SBC, KMUS, and Center_1nn. Performances of all approaches under consideration are compared using seven different classifiers: SVM, Random Forest, RPART, XGBoost, KNN, Naïve Bayes, and LR. We used the F1 score, Precision, Recall, and Accuracy as accuracy metrics for performance comparisons. Experimental results show that the XGBoost is the best classifier with an Accuracy of 99.53% for the proposed method, which is the highest Accuracy among all experiments.
Identification of Inefficient Radios for Efficient Energy Consumption in a Mobile Network

In a mobile network, it is important to identify energy inefficient RRUs (Remote Radio Units) to improve the overall energy efficiency of the network and achieve significant energy and cost savings. Existing solutions can identify inefficient RRUs based on hardware alarms or faults, but not energy consumption in real time for a given region. In this paper, we propose a network energy consumption model and method to identify inefficient RRUs with respect to power consumption in real time. Our method involves ML models trained on the historical data of performance management (PM) counters to predict the RRU energy consumption, on the basis of which the RRUs having a higher divergence between predicted and actual energy consumption are identified. The system is trained and tested with simulated data based on a major network operator.
Internet of Things based Liquified Petroleum Gas Leak Detection and Rapid Alert System

Liquefied petroleum gas, or LPG, is widely utilised in homes, businesses, and industries. However, if not handled appropriately, LPG can offer a severe fire and explosion danger. As a result, it is critical to detect LPG leaks quickly and take adequate precautions to avoid mishaps. An Internet of Things (IoT)-based LPG leak monitoring and alarm system has been created to address this issue. The utilization of IoT technology has revolutionized the detection and alert mechanisms for LPG leaks. Traditionally, identifying and managing LPG leaks posed a significant challenge due to the need for human monitoring, high costs, and complicated installation processes. However, with the advent of IoT-based LPG leak detection systems, this problem has been addressed in a reliable and cost-effective manner. Our research focuses on implementing an IoT-based LPG leak detection and alert system that utilizes sensors, microcontrollers, and wireless communication to detect and alert users of any type of LPG leak. This system is highly dependable, easy to set up, and can inform users both remotely and through an in-house alarm. By deploying this system, we aim to mitigate the risks of tragic accidents, fires, and explosions caused by LPG leaks.
Gaining Perspective into Cloud Computing: A Business-Oriented Approach

Computing may be a profoundly examined subject within the technological and financial world, and numerous huge players in the software industry are working for the improvement of computing services. With the help of a literature review, this work has identified that the classification of cloud computing (CC) does not specify how to classify CC based on industry-driven professional articles, as most of this study has been based on academic distributions. This may limit the ability of the research study to provide a consolidated view of the most current advances in this topic. CC has recently captured the attention of scholars, financial professionals, and specialists. Many of these experts have proposed a variety of applications based on various cloud-based techniques, frameworks, and essentials, which have led to different interpretations, specifications, deployment models, and service models. Therefore, a complete characterization and classification of CC are needed to address the above issue. The ultimate purpose of this research is to classify CC in the literature, including the set of security concerns, history, concepts, technologies, and challenges.
An Extendable Zeta Converter Based Grid-Connected Multilevel Inverter With Model Predictive Control Method

This study presents design and analysis of an extendable grid-connected DC-AC n-level multilevel inverter with the minimum component count for renewable energy systems, specifically focusing on photovoltaic (PV) panel applications. The proposed topology utilizes a zeta converter based on a coat circuit to harness potential of PV panels, allowing for the extraction of multiple DC voltage levels. These multi-output DC voltages are subsequently employed to generate a multilevel AC output voltage. In addition, model predictive control (MPC) method is applied to the n-level multilevel inverter instead of complex modulation algorithms in order to provide gate signals of the switches and to secure grid connection. Proposed multilevel inverter topology not only ensures high-quality AC output but also enhances grid integration and compatibility with various renewable energy applications. Simulation studies have been implemented in MATLAB/Simulink. Through design and simulation, this paper demonstrates the robustness and effectiveness of the proposed topology, making it valuable for renewable energy systems.
Perceived Digital Media Technology and Electronic Word of Mouth: Does Customer Satisfaction Matter?

Technological developments in the travel business offer various conveniences for consumers to order travel tickets online via the company's website as a digital platform. However, negative comments resulting from unsatisfactory service from online travel agents can reduce satisfaction levels, resulting in a reluctance to promote travel agents. Therefore, this research aims to determine the influence of perceived digital media technology and electronic word of mouth (e-wom) on satisfaction and willingness to promote online travel services. In addition, it explains the influence of satisfaction on the willingness to promote online travel services. The population in this study were consumers who had purchased tickets online at travel agents in Greater Jakarta, with a sample size of 150 customers. The sampling technique is purposive sampling. This study uses non-parametric statistics with the component-based Partial Least Square method (PLS-SEM). The research results have proven that perceived digital media technology and e-wom each positively and significantly affect satisfaction and willingness to promote the service. Customer satisfaction also influences willingness to promote the online travel agent service. Finally, this research contributes to online travel agents evaluating feedback policies so that they become a strength to compete in the travel agent industry.
GPR B-scan Data Classification through Deep Learning Approach

Ground Penetrating Radar (GPR) has proven indispensable in non-destructive geophysical investigations, offering a window into subsurface structures using electromagnetic waves. Its prowess in identifying underground utilities and hazards, like cavities, has been universally recognized. However, the intricate B-scan data generated by GPR poses significant interpretation challenges. Traditional methodologies, especially those reliant on dielectric constants and earlier machine learning models, often necessitate prior subsurface knowledge, inhibiting their universal applicability. Enter deep learning, which, in recent years, has changed the landscape of data interpretation across domains. Among the various architectures, Convolutional Neural Networks (CNNs) have shown immense potential in deciphering GPR data. This paper champions a novel methodology that harnesses the power of the ResNet18 architecture. Uniquely, our approach begins with a model pre-trained on the WiderFace dataset, renowned for facial recognition, and subsequently fine-tuned for GPR data. This merger of facial pattern recognition with geophysical data interpretation creates a synergy that promises unprecedented accuracy in subsurface detection. In summation, our research showcases how deep learning, especially the fusion of facial recognition insights with geophysical data, can redefine the boundaries of GPR data interpretation, offering enhanced accuracy and adaptability over conventional methods.
Text Prompt Translation for Zero Shot Prediction under Lexical Constraint in Aviation Domain

In real-world translation scenarios, particularly those involving safety in the aviation domain, it is expected that neural machine translation (NMT) can translate with idiomatic expressions and constraints provided by aviation experts. Generally, this topic problem can be addressed by fine-tuning NMT model with parallel corpus. However, parallel corpus is usually lacking and with noise. Conventional pre-training & fine-tuning paradigm may not perform well in this situation. Recently, a series prompt-tuning works have been proposed to solve the few-shot and zero-shot problems existing in downstream task. Inspired by those, we designed a prompt-based methods for NMT. With the help of injected related domain knowledge, the NMT models can strictly follow to word-level constraints. Specifically, in this work, we proposed two well-designed modules that can satisfy translation constraints in the aviation domain, including the preservation of words and observation of terminology. We extensively conduct experiments on a real-world large corpus of aviation maintenance text in English and Chinese and evaluated our method with four metrics, including missing or redundant information, incorrect formats, bad word or sentence patterns, and unconventional expressions. The experiment results show that our prompt methods were able to make the translation satisfy domain-specific lexical constraints in low-resource settings, using a pre-trained Transformer as the backbone model across all tasks.
Deep Learning in Mental Health: An In-Depth Analysis of Prediction Systems

Millions of people worldwide continue to suffer from a variety of diseases that frequently go undetected or untreated, making mental health disorders a major public health concern. In addition to a full examination of the many types of mental disorders, symptoms, diagnostics, and investigations of mental disorders, this review paper gives a detailed description of the mental disorder detection utilizing deep learning-based methodologies. The review starts out by going over the frequency, importance, and difficulties in timely diagnosing mental health illnesses. The system design for employing deep learning to identify mental disorders early on is then further explored. In addition, the comparative analysis component of the article, which is covered in the literature review section, provides a critical evaluation of the datasets and evaluation metrics currently in use for training and evaluating deep learning models for the identification of mental disorders. The advancements in this field, including the identification of depression, anxiety, schizophrenia, and other mental health issues, are demonstrated through a thorough assessment of recent studies and their findings. This work also discusses future possibilities and challenges in the field of deep learning for the diagnosis of mental disorders, highlighting the importance of large-scale, diversified, and representative datasets.
Audio Feedback Segregator using Sentiment Analysis: Role of Computer in Music

Audio feedback segregation using sentiment analysis separates audio feedback based on the outlook articulated within the audio indication. Attitude investigation is an appliance information procedure that analyzes text or speech's exciting tendency. This approach can be used to segregate audio feedback based on whether it is optimistic, pessimistic, or impartial. The segregation process can help identify feedback valuable to businesses and organizations for improving the products or services. The sentiment analysis technique involves training a machine learning model on a dataset of labeled audio feedback to predict the sentiment of new audio feedback. The model can then automatically segregate feedback and store in one database based on the predicted sentiment.
A Holistic Exploration of Natural Language Processing: Challenges, Milestones, and Research Frontiers

Natural Language Processing (NLP) is a rapidly evolving field with diverse applications, from chatbots to translation services. This survey offers a comprehensive exploration of NLP, covering its fundamentals, challenges, literature, structured model comparison, and research gaps. The introduction clarifies NLP's core concepts and applications. We then delve into its multifaceted challenges, spanning complex multimodal understanding to ethical issues like bias and fairness. This sets the stage for literature review, highlighting milestones like BERT and GPT-3. We provide a tabular comparison of influential models, elucidating their features, strengths, and limitations. Beyond reviewing past achievements, we spotlight critical research gaps—multimodal understanding, commonsense reasoning, bias mitigation, multilingualism, and ethics. Addressing these gaps is vital for responsible NLP advancement. In summation, paper offers a comprehensive and holistic view of the expansive field of NLP. It skillfully combines introductory concepts, a navigational guide through the realm of challenges, a detailed literature review, structured model comparisons, and a forward-looking perspective on the pressing research gaps. We aspire for this paper to emerge as an invaluable resource, serving as a guiding light for the research pursuits of scholars, the practical endeavors of practitioners, and the policy considerations of stakeholders within the expansive and dynamic NLP community. Its overarching objective is to steer future research initiatives toward the crucial task of addressing these pivotal gaps, thereby propelling the field into new horizons of knowledge and application.
Recent Advancements in Image Forgery Detection Techniques: A Review

The instantaneous developments in professional image editing tools have made it easier for mischievous individuals to make alterations to digital images and use them for inappropriate purposes, leading to a growth in concern of the reliability of the visual content. The consequences extend to affecting various aspects of life including legal proceeding, journalism and even to a personal level. This review paper emphasizes on surveying multiple image forgery techniques for images that have undergone multiple image transformations; passive and active detection techniques in particular. When of traditional image forgery is combines with higher level image transformation (such as geometric transformation), it conceals and complicates the alterations made to a digital image. This is carried out by applying certain proficient modifications like rotations, resizing, scaling etc. The researchers have carried out a depth-analysis of the image forgery detection techniques as well as its sub-types, already existing algorithms and methods, describing them hereby aiming to provide the details, analysis, advantages, limitations and the potential opportunities for future work. As a result, contributing to a better understanding of the forgery detection techniques to preserve the authenticity of an image.
Robustness Analysis and Evaluation Study of Chinese Text Event Detection Models

Event detection aims to identify trigger words in given text and classify them into corresponding event types, making it an essential task in natural language processing. With the advancement of artificial intelligence, machine learning, and deep learning techniques have been further researched and applied in the field of event detection. However, current event detection methods heavily rely on manually labeled data, which is constrained by high-quality data requirements. When facing challenges such as imbalanced sample distribution, low-quality data labeling, and adversarial data contamination, the effectiveness of models is often compromised, leading to a lack of robustness and poor generalization capabilities. This significantly limits the universal applicability of event detection algorithms. Therefore, the robustness evaluation of text event detection algorithms is of paramount importance. In this paper, we have designed various text perturbation robustness datasets based on the characteristics of Chinese text and developed evaluation metrics specifically for robustness datasets to measure algorithm robustness. Building upon the robustness datasets, we have devised a resilient analysis framework. We have constructed a text event detection algorithm using multi-layer perceptrons, convolutional neural networks, and BERT. By comparing the model’s evaluation results on the robustness test dataset with the high-quality initial dataset, we intuitively reflect the model’s robustness. This approach holds strong theoretical value for further enhancing the model’s generalization capabilities.
DL-Based Secure Wearable Scheme for Remote Patient Data Monitoring in Healthcare 4.0

The paper articulates an exploration into the integration of wearable devices for remote patient data monitoring, which marks a transformative shift in healthcare practices. This transition is underscored by user-centric interfaces and applications, enabling convenient remote monitoring. However, the escalating concern of patient data vulnerability to network breaches presents a substantial impediment. In response, we introduce the proposed scheme that harnesses Machine Learning (ML) and Deep Learning (DL) techniques. The main objective of the proposed scheme is to meet the exacting requirements of healthcare systems, aiming to fortify data integrity and confidentiality. Within this context, the proposed scheme unveils a sophisticated DL-driven detection model, adeptly tailored for discerning and countering data manipulation endeavors through a nuanced multi-label classification paradigm. Notably, the emphasis lies on safeguarding patient data privacy and security, which is paramount in today's information-rich landscape. The narrative seamlessly interweaves elements of wearable technology, sensor networks, advanced DL capabilities, and robust security measures, resulting in an innovative scheme. Finally, the proposed scheme is evaluated against various metrics such as accuracy and loss curve and F1, recall, and precision comparison.
Electric Vehicle-based Virtual Inertia and Damping for Enhanced Frequency Response in Low-Inertia Microgrids

This paper proposes an innovative control solution for the integration of Electric Vehicles (EVs) in low-inertia Microgrids (MGs) with high Renewable Energies (REs) penetration levels. The aim of the control approach is to enhance stability by combining Virtual Inertia Control (VIC) (to provide the needed inertia) with a Fractional Order Proportional Integral Derivative (FOPID) technique (to alleviate the adverse effects of Phase-Locked Loop (PLL) measurement delay on the frequency response). Extensive simulations, conducted within the MATLAB/Simulink environment, demonstrate the critical role of the EVs integration in enhancing the MG stability. The presented results show that the deployment of the VIC improves significantly the frequency response, while the inclusion of the FOPID controller yields substantial reductions in frequency deviations. The proposed control strategy exhibits notable resilience even under severe load variations and RE fluctuations. The optimized performance of the FOPID controller, achieved through the Zebra Optimization Algorithm (ZOA), demonstrates its effectiveness in improving the MG stability.