journal port science research

Abstract A smart parking system based on the Internet of Things (IoT) can assist users in locating parking spaces in crowded regions by offering real-time availability updates online. This technology streamlines the car parking procedure, encompassing the entire process from vehicle entry to payment and subsequent leave. The system utilises NodeMCU, a set of five IR sensors, and a pair of servo motors to accurately detect the presence of cars and determine the availability of parking slots. Two infrared sensors are used to detect cars at the entry and exit gates, while three sensors are employed to detect the availability of parking slots. Servo motors operate gates by responding to sensor readings to initiate opening and closing actions. The data is made available on the Adafruit IO platform, enabling worldwide monitoring. The system utilises a total of five infrared sensors and two servo motors that are linked to the NodeMCU. The NodeMCU is responsible for overseeing the entire operation and transmitting data regarding parking availability and time to Adafruit IO.

Abstract Markov Chains can be a valuable tool for analyzing expected awarded grades in the International Baccalaureate (IB) program for secondary schools. A core principle of the IB program is the emphasis on student progress throughout the two-year Diploma Programme (DP). Markov Chains excel at modeling transitions between states, which in this case, could represent different grade levels (e.g., predicted grade of 4 transitioning to a final awarded grade of 5).
One of the complicated parts in the teaching system is how to conduct the evaluation process. The absence of an ideal evaluation standard is a problem in the teaching system at all [4, 5]. In this study, it is aimed to implement Markov mathematics in analyzing and modeling the IB Diploma Statistical Bulletin of May examination sessions, which is interesting and the most common to any IB student. Markov model will be realized using students’ grades which were contained the awarded grades for each of the six EE groups. The data are examination results from 427452 students in 2211 schools for nine years of the IB Diploma statistical bulletin of May examination sessions 2006 – 2014 [6].

A new image encryption algorithm based on the Arnold transform and URUK chaotic maps is proposed to deal with the issues of inadequate security and low encryption efficiency. Colored images consist of three linked channels used in the scheme. This method uses different keys to break the correlations between adjacent pixels in each channel. First, the plain image is split into RGB channels to encrypt each channel separately. Second, the Arnold transform performs pixel permutation, resulting in scrambled channels. third, the URUK chaotic maps generate three key vectors to perform pixel diffusion, resulting in diffused channels used as input for the following step. Finally, the GWO shuffles each channel independently, to get the minimum correlation between image pixels, which are then merged to obtain a cipher image. This method generates the cipher image with great unpredictability and security. The security is evaluated using various measures. The results demonstrated a high level of security attained by successfully encrypting colored images. Recent encryption algorithms are compared in terms of entropy, correlation coefficients, and attack robustness. The proposed method provided outstanding security and outperformed existing image encryption algorithms.

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze brain tumour images (BT) to understand the disease's progress better. It is well-known that training 3D-CNN is computationally expensive and has the potential of overfitting due to the small sample size available in the medical imaging field. Here, we proposed a novel 2D-3D approach by converting a 2D brain image to a 3D fused image using a gradient of the image Learnable Weighted. By the 2D-to-3D conversion, the proposed model can easily forward the fused 3D image through a pre-trained 3D model while achieving better performance over different 3D baselines. We used VGG16 for feature extraction in the implementation as it outperformed other 3D CNN backbones. We further showed that the weights of the slices are location-dependent, and the model performance relies on the 3D-to-2D fusion view, with the best outcomes from the coronal view. With the new approach, we increased the accuracy to 0.88, compared with conventional 3D CNNs, for classifying brain tumour images. The novel 2D-3D model may have profound implications for future timely BT diagnosis in clinical settings.

The increased use of social media and the internet is leading to an increase in cyberbully vulnerabilities as well as daily usage. Cyberbullying is a deliberate, aggressive behavior that can be committed by an individual or organization. It occurs when people communicate, post, and distribute damaging, false, or unfavorable content online. For individuals impacted, it results in emotional and mental health issues. Therefore, it is imperative to create automated techniques for the detection and prevention of cyberbullying. The majority of the research done on cyberbullying detection in recent years has been on text-based analysis. The two most significant media in incidents of cyberbullying are text and visual. This paper presents An Explainable Multimodal Deep Learning Model for Cyberbullying Detection include three steps The first step involves collecting datasets from different resources, which include images and their captions with binary classes (bullying and non-bullying). The second step applies two techniques of XAI: CNN+GradCam to analyze input images and produce visual explanations, and LSTM+LRP to analyze and interpret input text. The third step employs two techniques of data fusion (early and late). Final step represents the evaluation performance of the EMDL-CBD model based on a set of accuracy metrics

Biomaterials, designed to interact with living systems, play a vital role in various medical applications. Classifying these materials effectively is crucial for understanding their properties and ensuring optimal use. Biomaterials are classified based on their chemical composition, structure, and properties relevant for biological applications. In this paper five types of Biomaterials classification methods are given namely. Chemical, Functional, Source of biomaterials, Structural and Smart levels classification. Each of these five-biomaterial classification method offers unique advantages and disadvantages. Chemical Classification is simple and well-established method, easy to understand and interpret and provides a basic framework for material identification. Functional Classification is directly related to the application of the biomaterial, provides insight into the desired material properties and useful for identifying materials for specific therapeutic needs. Source-Based Classification is straightforward method based on material origin and can be useful for initial categorization and understanding general material properties (e.g., natural materials often biocompatible). Structural Classification provides information about material properties like strength, degradation, and permeability and can be relevant for understanding biocompatibility and material performance. Smart Level Classification captures the advanced functionalities of next-generation biomaterials, provides insights into targeted drug delivery or controlled cell interactions and useful for identifying materials for specific therapeutic applications. The most suitable method depends on the specific context and information needs.

In sentiment analysis, text analysis becomes an important process to derive useful information from the unstructured data. In this work, we study the performance of three advanced machine learning algorithms, Support Vector Machines (SVM), Random Forest, and AdaBoost, for a specific sentiment classification task. Each classifier was trained and evaluated on principal metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1-Score, Precision, and Recall using the Bag of Words model for feature extraction. Those results show that the Random Forest approach beat both SVM and AdaBoost, with an AUC of 0.988, CA = 0.915, and F1-Score = 0.915 SVM demonstrated moderate performance with an AUC of 0.939 and an F1-Score of 0.845, while AdaBoost exhibited the worst performance in all metrics based on that ensemble model-based classifiers for data change predictions. Random Forest may thus be a powerful machine learning technique to implement for sentiment analysis in text.

In this work, was proposed and characterized a new linear operator and having this linear operator establishing, we benefited from it to define class of meromorphic Bazilević functions in the punctured unit disk U^*=U\{0}={z∈C:0<|z|<1}. What's more, we obtain some adequate conditions for functions having a place with this class.

Anemia is a major public health concern and has a significant impact on women and children worldwide. This condition is caused by a lack of sufficient Red Blood Cells (RBCs). Traditionally, anemia diagnosis has relied on invasive techniques that require blood samples, leading to pain and discomfort. This study explores deep learning techniques for automating and enhancing anemia detection accuracy using the Blood Cell Count and Detection (BCCD) dataset. We employ the VGG16 convolutional neural network architecture augmented with the Convolutional Block Attention Module (CBAM) to boost feature representation and performance. The BCCD dataset, consisting of 364 images with 4888 labeled blood cells, was used for training and evaluation. Our enhanced VGG16 model achieved accuracy for RBCs, WBCs, and platelets with values of 0.84, 0.93, and 0.92, respectively, effectively identifying various blood cell types and detecting anemia. Precision-recall analysis and confusion matrix metrics confirmed the robustness of the model, with high precision and recall rates and minimal false positives and negatives. These results suggest that advanced deep learning models with attention mechanisms, such as CBAM, can significantly improve medical diagnostics by providing reliable, efficient, and accurate early disease detection tools. Future development will be directed towards fine-tuning and validating the proposed model on other medical imaging datasets for clinical applications.

A novel image encryption algorithm employing triple chaotic maps has been developed to address the shortcomings of existing methods in terms of security and efficiency. The algorithm leverages the interconnectivity of color channels in images, using distinct keys to disrupt pixel correlations within each channel. The three chaotic maps utilized URUK, WAM, and Nahrain to generate two sets of keys. The first set is used to shuffle pixel positions, creating scrambled channels. Subsequently, the second set is applied to diffuse these scrambled channels independently. A gray wolf optimization (GWO) algorithm is then employed to further optimize the shuffling process, minimizing pixel correlations and enhancing security. The triple chaotic maps of varying orders contribute to the unpredictability and robustness of the cipher image. A comprehensive security analysis, including entropy, correlation coefficients, and attack resistance, demonstrates the superior performance of the proposed method compared to existing image encryption algorithms

With the increasing use of the Internet and social media and the sharing of millions of images daily, the need to classify these images and collect them in groups has increased for easy searching and finding similar and related ones, which in turn facilitates the work of many researches and works in various fields. The study proposes a method to classify images accurately and efficiently by making modifications to the image that improve the accuracy of the image without distorting it or losing its most important features and using the Swin transformer. The Swin transformer effectively captures contextual information at multiple scales, which leads to improved feature extraction. In addition, improving the accuracy of images enhances the quality of the system's work and obtains the expected results. The method we used reached an amazing accuracy rate of over 90%. The new approach outperformed existing methods, indicating its success in recognizing and classifying images. Classification accuracy has important benefits in recognizing images and which category they belong to, thus greatly helping in making more accurate decisions.

This paper explores the genetic and epigenetic impacts of drug abuse, focusing on how chronic drug use can induce mutations in DNA, particularly within protein-coding regions, and alter gene expression through mechanisms such as DNA methylation. The study highlights the significant role of these genetic changes in the brain's reward pathways, which contribute to the development and persistence of addiction. Through a comprehensive literature review, the paper identifies key genetic factors associated with addiction susceptibility and the long-term effects of drug-induced epigenetic modifications. A proposed research framework is presented, emphasizing the need for longitudinal studies to track epigenetic changes over time, targeted genomic analysis to identify critical mutations, and the integration of multi-omics approaches to provide a holistic understanding of drug abuse's impact on the genome. The paper also addresses ethical considerations, including the importance of protecting genetic privacy and preventing stigmatization based on genetic findings. The study concludes that advancing our understanding of the genetic and epigenetic mechanisms underlying addiction can lead to more effective, personalized treatment strategies and inform public health policies aimed at mitigating the effects of drug abuse.

The Swin Transformer possess a hierarchical structure, a robust structure, and an efficient self-attention mechanism. This makes it superior in performance for a wide variety of artificial intelligence and machine training tasks. It has revolutionized the field of digital signal processing and its applications by creating vision in multiple fields. It uses non-overlapping windows which leads to creating a distinct perspective and understanding of the context of the digital signals. However, it possess a very complicated hierarchical structure an complexities together. By reducing calculations and complexities together. This will lead to robust performance that make it a powerful tool for a wide range of computer vision tasks.
In this research, simplified symbol mixing methods were developed for coding structures similar to transformers, by forming various semantics in the text through linear mixing transformation and in combination with nonlinearity in the feed-forward layers. Hence, we were able to successfully propose a Swin Wavelet Transformer (SWT) model in which the self-attention sublayer was replaced by a Wavelet transform. In a second attempt, the wavelet transform was replaced by the multi-wavelet transform. These two proposed models are achieved a better performance from their FNets and BERT counterparts and are highly competitive with traditional and efficient transformers.