Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Pretrained Encoder-Decoder Transformer Models for Extreme Text Summarization

Text summarization plays a pivotal role in condensing crucial information from huge volumes of text. This study investigates the utilization of pre-trained transformer models within the domain of text summarization, with a specific emphasis on extreme summarization. It delves into the effectiveness of two prominent models, text-to-text transfer transformers and bidirectional and auto-regressive transformers, when applied to the task of summarization, providing a comparative analysis of their capabilities. The experiments conducted in this study involve the utilization of the XSum and SciTLDR datasets. While fine-tuning T5 and BART on summarization tasks is a standard approach, we delve into the performance of these models without fine-tuning. Additionally, we explore the potential of other pretrained models, such as PREGASUS and distilBART, in generating concise and coherent summaries. This study contributes to the understanding of how pre-trained transformer models can be harnessed effectively for text summarization, especially in extreme summarization scenarios. The findings shed light on the performance, challenges, and potential of these models, opening avenues for further research in the field of automatic text summarization.
UAV Path Planning Algorithm Based on Improved Rapidly-Exploring Random Tree

In this paper, a UAV path planning algorithm based on improved rapidly-exploring random tree(RRT) is proposed to solve the problem of high randomness of sampling points and poor smoothness of planned path in the RRT path planning algorithm. In the process of tree expansion,the detection field is developed to limit the generation range of sampling points, and the generation strategy of sampling points is changed, so that the algorithm has the ability to achieve faster convergence to target and round obstacles. In addition, the improved algorithm also cuts the redundant points of the initial track, and uses the B-spline curve method to improve the smoothness of the track. The simulation results show that the UAV path planning algorithm based on the improved RRT reduces the average path length by 21.75%, the average number of tree nodes by 75.78%, and the average calculation time by 48.04% compared with the traditional RRT UAV path planning algorithm.
BMPA- DSL: Binary Marine Predators Algorithm to Identify Driver’s Different Levels of Stress

In smart cars, checking driver’s conditions is necessary for safe driving. Stress is a destructive emotional state that causes drivers not to make timely decisions and brings irreparable risks. Therefore, detecting drivers’ stress and giving timely warnings can prevent possible accidents. The best way to identify stress is to use bio-signals and intelligent processing algorithms. In the proposed method to identify drivers’ stress, the drivedb database is used. Then, various statistical, frequency, entropy, and morphological characteristics are extracted from the ECG data of this database. To optimize the features, the Binary Marine Predators Algorithm is used, which is a meta-heuristics method inspired by hunting prey by a marine in nature. Using two transfer functions, this algorithm can optimize features more than other heuristics optimizers. Using the proposed method, three states of low, medium, and high stress in drivers have been identified with 94.6% accuracy, which has increased the accuracy by 3-4% compared to the latest research in the field.
Analysis of pros and cons of water diversion projects to environment

Some interbasin water diversion projects in ancient and modern times at home and aboard are introduced with emphasis placed on the pros and cons of them, and some serious problems found in similar projects at aboard are enumerated. Then the urgency of the South to North Water Transfer Project and the importance of eco environment protection are expounded, and some measures for water resources saving consistent with interbasin water diversion are suggested, such as basin based agricultural reform, regeneration and reutilization of industrial wastewater, collection of rainwater, adjustment of water price, and economic principle based saving of water resources. It is also pointed out that the water right based implementation of the interbasin water diversion is a strategic measure for optimal allocation and sustainable utilization of water resources, and that water resources is a kind of valuable merchandise, and the transaction upon consideration between water resources and water right will form the water market.
Unveling Personality Patterns: K-Means Clustering and Dimesion Reduction Analysis for Behavioural Traits Categorization

A person's personality is made up of features of their behavioral patterns, psychological state, motivation to succeed, and cognition personality traits. Individuals particular personalities do not constitute the sole objective in common personality test results, however, the identity that others who interact with them connect to them. Conventional personality descriptors are frequently referring to the Big Five personality traits. In this study, we performed a partitioning approach such as k-means clustering techniques in personality traits categorization which produces the final calculation of cluster centroids and arranging each point in its appropriate cluster. Our goal was to determine the most significant behavioral variables using data dimension reduction methods such as Principal Component Analysis. The total number of extracted principal component is 51 and the total explained variance ratio in the personality dataset is 24.33%.
Identifying secondary crashes using text mining techniques

Abstract Secondary crashes, roadway clearance time, and incident clearance time are three primary performance measures for traffic incident management. Crash databases, which are the main source of information on secondary crashes, often fail to identify or misidentify secondary crashes. Manual reviews of crash reports can improve the accuracy of crash classification, but they are expensive and time consuming. To improve the identification of secondary crashes, this study developed a text mining approach to distinguish secondary crashes based on crash narratives. To deal with the unstructured nature of crash narratives, a four-step process involving tokenization, counting, vectorization, and normalization was implemented to transform their content into numeric vectors suitable for machine learning. Next, an evaluation of several classification models found the logistic regression model produced the most accurate classifications. A single-word representation of the narratives offered the best performance compared to more complicated schemes and is recommended for future implementation. A review of classification results showed the model is effective at identifying keywords which characterize secondary crashes. Some false classifications may be a consequence of subjective reviewer interpretations and potential mislabeling. The findings demonstrate that the text mining approach provides satisfactory performance and has great potential for identifying secondary crashes.
Kidney Abnormalities Prediction in Ultrasound Images using Transfer Learning Approach

Transfer Learning Algorithm is becoming popular for medical image analysis, because it has high level of flexibility in dealing with Image dataset with minimum number of Images. It makes convenient for researchers to test and train medical images, assign the determined weights thus reducing the computational complexities and pre-processing time. Presence of stones, tumour, cyst and other kidney abnormalities in Kidney Ultrasound Images is a challenging task for a radiologist. There exists a difference of opinion between an experienced physician and a beginner in the field of urology and nephrology. Requirement of an intelligent tool for diagnosis in the medical field has increased in the recent years due to the enormous growth of Artificial Intelligent and Machine Learning algorithms. In this proposed work Pretrained Efficient Net models B1-B5 are used with the pretrained weights of ImageNet data set thus, reducing the pre-processing time taken for random initialization of weights. Ultrasound Kidney Image Dataset are taken as inputs for training phase and testing Phase. Fine Tuning is adapted by changing the hyper parameter setting. Performance metrics such asF1Score and AUC are evaluated. Efficient Net B4 Model gives satisfactory result in terms of AUC as 89.95% and F1 Score 82.9%. Cross validation when carried out by training the models with predetermined weights of CT scan Kidney Images resulted in improving AUC to 90.9% for the prediction of kidney stones, cyst and tumour.
Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer

In the current interconnected and susceptible digital environment, the demand for reliable network attack prediction systems has reached a critical level. This research paper presents a novel approach for network intrusion detection utilizing the Improved Import Vector Machine (IVM) classification algorithm combined with feature selection using Cuckoo Search based Grey Wolf Optimization (CS-GWO). The aim is to improve the accuracy and effectiveness of intrusion detection systems by selecting the most relevant features and employing a powerful classification algorithm. The proposed methodology begins with data preprocessing, ensuring the dataset is appropriately cleaned and normalized. Subsequently, the CS-GWO algorithm is applied for feature selection, leveraging the strengths of both Cuckoo Search and Grey Wolf Optimization. This hybrid approach efficiently explores the search space to identify the optimal feature subset that contributes to accurate intrusion detection. Once the feature selection process is completed, the IVM classification algorithm is employed to classify network instances into intrusion or normal categories. The IVM algorithm is chosen for its ability to handle high-dimensional data and its robustness in dealing with imbalanced datasets commonly encountered in network intrusion detection. To evaluate the performance of the proposed approach, extensive experiments are conducted using benchmark datasets. Various evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the system in detecting network intrusions. The results demonstrate that the combination of IVM classification and CS-GWO feature selection significantly improves the accuracy and efficiency of network intrusion detection. The selected features obtained through CS-GWO contribute to enhanced classification performance, enabling the system to accurately identify and respond to potential intrusions in real-time scenarios.
A Real-Time Double Flexible Job Shop Scheduling Problem Under Industry 5.0

In today's Industry 5.0 environment, there is a strong emphasis on human considerations, sustainability, and resilience. These key factors have significant implications for production environments, and accurately representing them becomes crucial. In this article, we investigate a real-time double flexible job shop scheduling problem (DyDFJSP) with consideration of human factors fatigue and energy consumption. We propose a dynamic strategy based on an enhanced genetic algorithm (EGA) to solve the problem. The proposed algorithm is subjected to a comprehensive experimental analysis on a dynamic scenario, thoroughly examining its effectiveness in solving the DyDFJSP and providing a solid proof of concept for its applicability.
Personality Prediction from Arabic Text: A Machine Learning Approach for Job Candidate Screening

Personality prediction from text plays a vital role in understanding individuals and aiding decision-making processes. In this study, we present a comprehensive approach for personality prediction from Arabic text specifically tailored for the domain of job candidate screening. Our model incorporates text preprocessing and the extraction of statistical and linguistic features from text, such as word count, average words per sentence, and TF-IDF representation. The feature set is then reduced using a filter-based feature selection method, , and the vector is fed to a Ridge regressor to predict the Big Five personality traits accurately. We did use our model for job candidate screening. Recruiters can be empowered to make more informed preselections, guided by the insights derived from the prediction of personality traits and the suggestions of candidates who best match the job requirements. This improves the screening efficiency. The experimental results demonstrated the effectiveness of our model and its practical applicability in real-world scenarios.
A BlackBox Approach to Profile Runtime Execution Dependencies in Microservices

Loosely-coupled and lightweight microservices running in containers are likely to form complex execution dependencies inside the system. The execution dependency arises when two execution paths partially share component microservices, resulting in potential runtime performance interference. In this paper, we present a blackbox approach that utilizes legitimate HTTP requests to accurately profile the internal pairwise dependencies of all supported execution paths in the target microservices application. Concretely, we profile the pairwise dependency of two execution paths through performance interference analysis by sending bursts of two types of requests simultaneously. By characterizing and grouping all the execution paths based on their pairwise dependencies, the black box approach can derive a clear dependency graph(s) of the entire backend of the microservices application. We validate the effectiveness of the blackbox approach through experiments of open-source microservices benchmark applications running on real clouds (e.g., EC2, Azure).
Database Knowledge Discovery Combining Cluster Analysis and Classification Using Data Mining

Two key data mining methods are clustering and classification. The problem of classification involves categorizing object into one-to-many pre-established categories according to the characteristics of the object. While clustering groups objects based on their proximity or similarity and is a clear learning problem. Every group is referred to as a cluster. In the study, we perform integration to data mining by the clustering and classification techniques using a big database called the “Fisher's Iris Dataset” with the attribute and some examples. Using the Data Mining application Waikato Environment for Knowledge Analysis, We contrasted the outcomes of integrating a clustering and classification technique based on numerous criteria with the outcomes of a straightforward classification strategy (using the J48 classifier). The experiment's findings demonstrate that combining clustering and classification produces effective outcomes with a high robustness and accuracy rate, and the data set contains some values that are missing.
Machine Learning Approaches for Audio Classification in Video Surveillance: A Comparative Analysis of ANN vs. CNN vs. LSTM

This study aims to develop a robust audio classification system capable of accurately analyzing and categorizing audio events in video streams. Leveraging machine learning techniques, we construct a model that can recognize and classify various audio events commonly encountered in surveillance scenarios, including gunshots, sirens, and car horns.Audio samples are extracted from video frames, and crucial audio features are enhanced while removing noise during the preprocessing stage. Feature extraction techniques, such as Mel-frequency cepstral coefficients (MFCC), are employed to capture distinctive audio traits. A labeled dataset comprising 8,732 audio samples is utilized to train a machine learning model, such as a Convolutional Neural Network (CNN) or Artificial Neural Network (ANN) OR long short-term memory networks(LSTM). The model progressively learns to identify and categorize each audio pattern and characteristic. To improve the model's performance and generalizability, diverse methods are applied. Real-world video surveillance datasets are employed in multiple experiments to assess the proposed method's performance. The developed audio classification system is compared with existing approaches to demonstrate its superiority. The results reveal that the implementation of the audio classification system significantly enhances surveillance capabilities by accurately identifying and categorizing audio events in video streams. This capacity improves the overall effectiveness and efficiency of surveillance systems, enabling operators to promptly recognize and respond to potentially harmful situations.
A Cognitive Behavioral AI: Novel Conversational Memory Elements for Technical Understanding of Medical Deep Denoisers

Conversation is a powerful cognitive behavioral mechanism for revealing what is understood. In this paper, we propose conversational memory elements (CMEs) to (technically) comprehend the learning functional by state-of-the-art medical deep denoisers. The gradients of neurons' weights, varying over the epochs, are utilized to build the novel CMEs. These are defined in terms of Jacobian matrices and integrated in between layers of the medical deep denoisers without affecting their neural architectures. A covariance map is then defined using values of CME at the convergence of training a denoiser; while an autocorrelation function is derived considering values of CMEs over the epochs. In turn, these statistical indicators are used to infer gross characterizations of the unknown denoising functional in terms of joint probability function expressed by the hidden weights. The ability of learning is quantified via patterns in the covariance maps, while the autocorrelation function help understanding the stability and convergence of deep denoisers. It is further cross-validated against denoising performance analysis on benchmark synthetic and real medical datasets and found consistent. Besides, the proposed approach assists suggesting optimal convergence as well as a deployment strategy among available medical deep denoisers.
Exploring Face Recognition: A Comparative Study Using Loss-Based Methodologies

The field of face verification and recognition systems is rapidly evolving with significant advances being made. To clearly distinguish different face samples, the intra-class and inter-class similarity between samples must be clearly distin-guishable in latent space. The performance of a Deep Learning based face recognition system is primarily governed by the variety and sufficiency of data on which the model is trained and evaluated. The study presents and analysis on generalization capabilities of a Deep Learning model trained using a siamese network with different loss functions. We harnessed transfer learning through a pre-trained Resnet50 model, which acted as an embedding extractor. Our experimentation was conducted using the standard dataset “Labeled Faces in the Wild (LFW)” for both training and testing phases. We employed both offline and online triplet mining strategies within the framework of triplet loss-based training methodology. For contrastive loss-based methodology, the Euclidean distance serves as the metric to calculate the similarity between face image samples. In contrast, for triplet loss-based methodology, cosine similarity is utilized as the metric to ascertain similarity between embeddings. Distinct training instances were established, each implementing either contrastive loss or triplet loss as distinct loss functions. Our experimental findings distinctly favor the triplet loss-based model, surpassing the performance of the contrastive loss-based model and achieving an accuracy exceeding 70%.
Monocular Vehicle Perception Algorithm Based on Improved U-NET

This paper presents a monocular vision perception algorithm for vehicles based on improved U-Net. Vehicle perception is the key to autonomous driving and is used to determine the pose information of surrounding vehicles. Among the vision perception schemes, the monocular vision vehicle perception algorithm based on neural network has the lowest cost, but its accuracy is low due to the lack of monocular image depth information. In this regard, this paper uses ApolloCar3D data set to study U-Net based monocular vision around the vehicle perception algorithm, in the form of point detection to achieve vehicle positioning, and its position perception and correction. The algorithm is based on U-Net network, and the vehicle combination correction algorithm is designed to further improve the accuracy of the perception results. Through the test set test, the perception result of the improved algorithm is better than the public performance of other typical algorithms and has better real-time performance.
Navigating College Campuses with Virtual Assistants

Artificial intelligence plays a crucial role in everyday life. AI research is defined by computer science as the study of intelligent agents. Today, most individuals, whether on purpose or not, use some type of computerized information processing equipment. Artificial intelligence (AI) is already altering our way of life. A gadget that detects its environment and takes steps to increase the chance of reaching a goal. A database’s input can be a selection of users and articles, and the output is a harsh suggestion. The user can contribute input to the system either orally or textually. Voice assistants are software agents that can understand and respond to human speech using synthesized voices. The most popular voice assistants are Apple’s Siri, Amazon’s Alexa, Microsoft’s Crotona, and Google’s Assistant, which are incorporated in smart phones or specialized home speakers. Users may use voice commands to ask their assistants questions, operate home automation devices and media playback, and handle other basic chores like as email, to-do lists, and calendars. Artificial intelligence voice assistants can understand and respond to human speech. Users may use spoken commands to ask their assistant questions and perform other minor chores such as sending emails and browsing websites This study will look at the typical Usage of Voice Assistants in Educational Sector for Navigation Purpose.
Automated Detection and Diagnosis of Skin-Lesion using Transfer Learning based YOLOv7 Approach

Skin disease is a prevalent and growing health issue, its prompt and correct detection is crucial for treatment and therapy for that purpose. This paper, suggests a technique for identifying skin diseases that makes use of the YOLOv7 object identification algorithm’s capabilities. Visual inspection, which takes time and is prone to errors, has traditionally been used to detect skin diseases. As a result, automated skin disease detection utilizing computed methods has drawn a lot of importance in recent years. The goal of the suggested strategy is to offer a precise and efficient remedy for identifying skin diseases. For accurate recognition of various forms of skin diseases, the approach uses a self-labeled Dermnet data collection of skin diseases. In the proposed approach YOLOv7 models were trained via transfer learning, pre-trained templates, and big datasets. The experimental results demonstrate that the suggested YOLOv7-based method outperforms other methods in disease identification and achieves greater accuracy. As a result, dermatologists can quickly identify and treat skin diseases. This offers an accurate and efficient option for skin disease detection.