bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Image Enhancement Techniques for Advancing Colon Cancer Diagnosis
Authors: Jitendra Prakash Patil Et al.
Published: December 2023

Colorectal cancer continues to pose a substantial worldwide health challenge, necessitating the development of advanced imaging techniques for early and accurate diagnosis. In this study, we propose a novel hybrid image enhancement approach that combines Total Variation (TV) regularization and shift-invariant filtering to improve the visibility and diagnostic quality of colon cancer images.

The Total Variation regularization technique is employed to effectively reduce noise and enhance the edges in the input images, thereby preserving important structural details. Simultaneously, shift-invariant filtering is utilized to address spatial variations and artifacts that often arise in medical images, ensuring consistent and reliable enhancements across the entire image. Our hybrid approach synergistically integrates the strengths of both TV regularization and shift-invariant filtering, resulting in enhanced colon cancer images that offer improved contrast, reduced noise, and enhanced fine structures. This improved image quality aids medical professionals in better identifying and characterizing cancerous lesions, ultimately leading to more accurate and timely diagnoses.

To evaluate the effectiveness of the proposed approach, we conducted extensive experimentations on a diverse dataset of colon cancer images. Quantitative and qualitative assessments demonstrate that our hybrid approach outperforms existing enhancement methods, leading to superior image quality and diagnostic accuracy. In conclusion, the hybrid image enhancement approach presented in this study offers a promising solution for enhancing colon cancer images, contributing to the early detection and effective management of this life-threatening disease. These advancements hold significant potential for improving patient outcomes and reducing the burden of colon cancer on healthcare systems worldwide.
An Enhanced Query Optimization Implemented in Hadoop using Bio-Inspired Algorithm with HDFS Technique
Authors: Abhijit Banubakode Et al.
Published: December 2023

A more effective method for massive data query optimization using HDFS and the Bio-inspired algorithm. Big Data configuration and query optimization are the two phases of the process. To remove redundant data, the input data is first per-processed using HDFS. Then, utilizing entropy calculation, features like closed frequent pattern, support, and confidence are extracted and managed. The Bio-inspired Horse Herd approach is used to group pertinent information based on this outcome. In the second step, the Big Data queries are used to obtain the same features. The optimized query is then located using the Bio-inspired technique, and the similarity assessment procedure is run. The proposed algorithm, according to this research, outperforms other ones that is unique in use. It is challenging to determine the veracity of this claim without more information regarding the experimental setup and the precise measures employed to assess the algorithm's effectiveness. Furthermore, it is unknown how the proposed algorithm stacks up against other cutting-edge query optimization methods. Finally, the assess has efficiency of using this method, more optimistic query achieved and comparison analysis are proved.
Predicting Arrhythmia Based on Machine Learning Using Improved Harris Hawk Algorithm
Authors: Nitesh Sureja Et al.
Published: December 2023

Arrhythmia disease is widely recognized as a prominent and lethal ailment on a global scale, resulting in a significant number of fatalities annually. The timely identification of this ailment is crucial for preserving individuals' lives. Machine Learning (ML), a branch of artificial intelligence (AI), has emerged as a highly efficient and cost-effective method for illness detection. The objective of this work is to develop a machine learning (ML) model capable of accurately predicting heart illness by using the Arrhythmia disease dataset, with the purpose of achieving optimal performance. The performance of the model is greatly influenced by the selection of the machine learning method and the features in the dataset for training purposes. In order to mitigate the issue of overfitting caused by the high dimensionality of the features in the Arrhythmia dataset, a reduction of the dataset to a lower dimensional subspace was performed via the improved Harris hawk optimization algorithm (iHHO). The Harris hawk algorithm exhibits a rapid convergence rate and possesses a notable degree of adaptability in its ability to identify optimal characteristics. The performance of the models created with the feature-selected dataset using various machine learning techniques was evaluated and compared. In this work, total seven classifiers like SVM, GB, GNB, RF, LR, DT, and KNN are used to classify the data produced by the iHHO algorithm. The results clearly show the improvement of 3%, 4%, 4%, 9%, 8%, 3%, and 9% with the classifiers KNN, RF, GB, SVM, LR, DT, and GNB respectively.
Fusion based Image Enhancement Approach for Brain Tumor Detection
Authors: Deepa Abin Et al.
Published: December 2023

Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14.
Analysis on Leaf Disease Identification using Classification Models
Authors: R. Salini Et al.
Published: December 2023

The Researchers have all been aware of the rising food demand brought on by the population's rapid growth and the high mortality rates caused by medical developments. One of the many farming practises where computerization in agriculture has made significant progress is the identification of numerous plant diseases. The focus of almost every nation has shifted towards mechanising agriculture in order to achieve accuracy and precision and to meet the continually increasing demand for food. Identification of plant diseases is one of the most difficult tasks in agriculture and has a significant effect on crop yield. Artificial intelligence has recently begun to concentrate on smart agriculture science.Ground-breaking methods in plant science through deep learning and hyperspectral imaging to locate and recognise plant diseases has been addressed in this study.
Text Detection Using Transformation Scaling Extension Algorithm in Natural Scene Images
Authors: A.S.Venkata Praneel Et al.
Published: December 2023

In recent study efforts, the importance of text identification and recognition in images of natural scenes has been stressed more and more. Natural scene text contains an enormous amount of useful semantic data that can be applied in a variety of vision-related applications. The detection of shape-robust text confronts two major challenges: 1. A large number of traditional quadrangular bounding box-based detectors failed to identify text with irregular forms, making it difficult to include such text within perfect rectangles.2. Pixel-wise segmentation-based detectors sometimes struggle to identify closely positioned text examples from one another. Understanding the surroundings and extracting information from images of natural scenes depends heavily on the ability to detect and recognise text. Scene text can be aligned in a variety of ways, including vertical, curved, random, and horizontal alignments. This paper has created a novel method, the Transformation Scaling Extention Algorithm (TSEA), for text detection using a mask-scoring R-ConvNN (Region Convolutional Neural Network). This method works exceptionally well at accurately identifying text that is curved and text that has multiple orientations inside real-world input images. This study incorporates a mask-scoring R-ConvNN network framework to enhance the model's ability to score masks correctly for the observed occurrences. By providing more weight to accurate mask predictions, our scoring system eliminates inconsistencies between mask quality and score and enhances the effectiveness of instance segmentation. This paper also incorporates a Pyramid-based Text Proposal Network (PBTPN) and a Transformation Component Network (TCN) to enhance the feature extraction capabilities of the mask-scoring R-ConvNN for text identification and segmentation with the TSEA. Studies show that Pyramid Networks are especially effective in reducing false alarms caused by images with backgrounds that mimic text. On benchmark datasets ICDAR 2015, SCUT-CTW1500 containing multi-oriented and curved text, this method outperforms existing methods by conducting extensive testing across several scales and utilizing a single model. This study expands the field of vision-oriented applications by highlighting the growing significance of effectively locating and detecting text in natural situations.
Detection of Android Malware using Feature Selection with a Hybrid Genetic Algorithm and Simulated Annealing (SVM and DBN)
Authors: E. Padmalatha Et al.
Published: December 2023

Because of the widespread use of the Android operating system and the simplicity with which applications can be created on the Android platform, anyone can easily create malware using pre-made tools. Due to the spread of malware among many helpful applications, Android users are experiencing issues. In this study, we showed how to use permissions gleaned from static analysis to identify Android malware. Utilising support vector machines and deep belief networks, we choose the pertinent features from the set of permissions based on this methodology. The suggested technique increases the effectiveness of Android malware detection.
Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification
Authors: Parthasarathy V. Et al.
Published: December 2023

Pneumonia detection and classification are a vital medical imaging task that purposes to automatically recognize and classify pneumonia-related abnormalities in chest radiographs or Chest X-Ray (CXR) imaging. Accurate and prior identification of pneumonia is important for suitable treatment and recovering patient results. The main drive is to recognize whether an X-ray image indicates the presence of pneumonia or not. A binary classification techniuqe is trained to distinguish normal and pneumonia X-rays. Deep Learning (DL) approaches are revealed major success in automating this process, assisting healthcare specialists in analyzing pneumonia more effectively. This article presents a Whale Optimization Algorithm with Fuzzy Wavelet Neural Network for Pneumonia Detection and Classification (WOAFWNN-PDC) technique on CXRs. The purpose of the WOAFWNN-PDC technique is to apply optimal DL approaches for the recognition and classification of pneumonia. In the presented WOAFWNN-PDC technique, Gaussian Filtering (GF) approach is used for the noise removal process. In addition, the MobileNetv3 model is utilized for the feature extraction method. Moreover, the FWNN technique was applied to the classification of pneumonia. Finally, the WOA can be executed for an optimum selection of the parameters related to the FWNN approach. The simulation value of the WOAFWNN-PDC algorithm was assessed on a benchmark medical database. The comparative analysis exhibits better results than the WOAFWNN-PDC technique.
Exploration of Artificial Intelligence Assisted Health Risk Prediction
Authors: Anusha Ampavathi Et al.
Published: December 2023

AI Assisted Health Risk Prediction uses personalized knowledge and information gathered from the responses it creates to solve both simple and difficult problems. Amazing improvements in computing power and AI technologies may revolutionize the process of creating new medications. Due to increasing R&D costs and decreased efficiency, the pharmaceutical industry is currently experiencing difficulties with medication development. The key causes of the high attrition rates in drug approval will be covered in this overview, along with potential methods "The procedure involves gathering data, establishing rules for its utilization, reaching approximate or definitive conclusions, and self-correction. The development of AI can be seen as having two contrasting effects: on one hand, many individuals are concerned about job security, while on the other hand, each advancement is celebrated as a significant stride for civilization. Numerous industries, such as education and business process automation, employ AI. The idea of integrating AI into the drug development process has shifted from hype to optimism. The potential applications of AI in this field are vast." to drug development pipeline, drug development techniques and procedures, pharmaceutical R&D are discussed in this paper.Artificial intelligence (AI)-assisted health risk prediction refers to the use of AIalgorithms and techniques to analyze health data and predict the risk of developing certain health conditions or diseases. The weighted product method is a multi-criteria decision-making process is there are many alternatives, and based on several criteria we must determine the best alternative.RPM1, RPM2, RPM3, RPM4, RPM5.Usefulness, Social factors, Ease of use, Quality of technological service, Compatibility"While no pharmaceuticals have been created using AI methods to date, according to the developments discussed in this study, it is anticipated that it will take another 2-3 years before a drug is developed. It is interesting to note that specialists firmly believe AI will fundamentally alter the pharmaceutical sector and the process of medication development.""However, domain expertise is crucial for training algorithms, which is essential for efficient AI-assisted drug development. While domain expertise allows individuals to process vast datasets, AI algorithms can be trained, algorithms can be defined, and the examined data can be improved to facilitate a faster and more accurate drug development process.""This creates an ideal environment for collaboration between AI and medicinal chemists. Despite the potential of AI to accelerate medication discovery, real studies still need to be conducted. Furthermore, AI can also assist in areas such as gene therapy and other therapies that are not yet widely available in healthcare.". AI opens up the prospect of merging gene therapy, pharmacology, and regenerative medicine.
Analysis of Smart Parking System Using IOT Environment
Authors: Siva Shankar S Et al.
Published: December 2023

The typical parking experience has been transformed by smart parking systems that use the Internet of Things (IoT) environment to integrate technology to improve efficiency, convenience, and sustainability. In order to monitor and manage parking spaces in real-time, this unique technique makes use of IoT devices, such as sensors, cameras, and networking technologies. As a result of the system's reliable information on parking availability, drivers may find and book parking spaces in advance, which eases traffic and reduces aggravation. Additionally, parking systems with IoT capabilities optimize resource use, lowering carbon emissions and fostering sustainability. The adoption of IoT in parking systems is a crucial step towards building smarter, more connected cities that will enhance both drivers' and parking operators' experiences with parking. There are numerous crucial elements in the process for developing a smart parking system in an IoT context. First, sensors are placed in parking places to gather up-to-the-minute occupancy information. Then, using wireless communication protocols, this data is sent to a central server or cloud computing platform. After that, a data processing and analysis module interprets the gathered data using algorithms and machine learning techniques and presents parking availability information to users via a mobile application or other user interfaces. For effective management and monitoring of parking spaces, the system also includes automated payment methods and interacts with existing infrastructure. Taken as Alternative parameters is Park Smart, Street line, Park Whiz, ParkMobile, Spot Hero. Taken as evaluation parameters is Light Sensor, CCTV coins, SMS, Cost-effectiveness, Timestamp. This demonstrates the rank of the data set Park Smart is on 1st Rank, ParkMobile is on 2nd Rank, Park Whiz is on 3rd Rank, Street line is on 4th Rank and Spot Hero is on 5th Rank. To sum up, implementing a smart parking system employing IoT technology has shown to be a potential way to deal with the problems associated with urban parking. The system increases parking efficiency, lessens traffic congestion, and enhances user experience by utilising IoT sensors, data analytics, and real-time communication. The parking scene in smart cities has the potential to change dramatically, enhancing ease and sustainability.
COVID-19 Regional Safety Assessment Using Evaluation Based on Distance from Average Solution (EDAS) Method
Authors: Ila Chandana Kumari P Et al.
Published: December 2023

The process of assessing the safety and risk level of a particular region or area in respect to the COVID-19 pandemic is known as COVID-19 Regional Safety Assessment. It involves analyzing various factors, such as the number of active cases, testing and reporting capabilities, vaccination rates, healthcare system capacity, implementation of public health measures, travel restrictions, presence of variants of concern, and localized outbreaks. A complete evaluation of regional safety is necessary for public health professionals, legislators, and residents to successfully prevent the spread of COVID-19 and protect public health and wellbeing. Authorities may identify areas of concern, distribute resources wisely, and put targeted measures in place to restrict the virus's spread by performing a thorough examination. In order to restrict the virus's spread and protect the health and welfare of communities, it is crucial for guiding decision-making processes, identifying problem areas, and effectively allocating resources.  The research carried out through regional safety assessments advances our knowledge of the pandemic, guides public health initiatives, and encourages the use of evidence-based decision-making in order to effectively battle COVID-19. Distance from Average Solution-Based Evaluation (EDAS)The evaluation based on distance from the average solution approach assesses the efficacy or quality of individual solutions or data points by comparing each solution or data point to the average or mean solution. This approach is commonly employed in various fields, including optimization, data analysis, and decision-making.In this evaluation method, the average solution serves as a reference point or baseline. It is crucial to remember that the evaluation's specific context and goals may influence the choice of the average solution and distance metric. Additionally, other evaluation criteria or metrics may be employed in conjunction with the distance-based evaluation to obtain a more comprehensive assessment of the solutions. China, Denmark, Germany, Hong Kong, Hungary, Israel, Australia, Austria, Canada, and Efficiency of the government, monitoring and detection, and quarantine Emergency Preparedness, regional resilience, and healthcare readiness .Ranking of the nation based on the Covid-19 Regional Safety Assessment survey. Hungary is shown as occupying the last slot, whereas China is listed as occupying the first spot. It has been noted that China has a significant influence on COVID-19.
Investigating the Impact of Partial Shading on Photovoltaic Panels and Enhancing their Efficiency using the Python
Authors: Hayder Salah Mohammed Et al.
Published: December 2023

This research project focuses on investigating the impact of partial shading on photovoltaic (PV) panels and proposes methods to enhance their efficiency using Python programming. Partial shading can significantly reduce the performance of PV panels by creating imbalances in current and voltage outputs. By leveraging Python's computational capabilities, this study aims to develop simulation models and algorithms that accurately capture the behavior of shaded PV panels. The objectives of this research include building a comprehensive understanding of partial shading effects, developing a Python-based simulation framework, analyzing the impact of different shading patterns on PV panel efficiency, investigating novel techniques to enhance efficiency using Python, and evaluating proposed approaches through simulations and experimental validation. Through this investigation, we aim to contribute to the development of improved strategies for the design and operation of PV systems. By mitigating the negative effects of shading and enhancing PV panel efficiency, we can further promote the adoption of sustainable solar energy solutions. The outcomes of this research have the potential to advance the field of solar energy and facilitate a greener and cleaner future. Also, this paper aims to investigate the impact of partial shading on photovoltaic (PV) panels and explores methods to enhance their efficiency using Python. Partial shading can significantly reduce the overall output of PV systems, leading to suboptimal performance. By analyzing shading patterns and implementing intelligent algorithms, we can optimize PV panel configuration and improve their efficiency. In this study, the Python programming language will be utilized to develop simulation models, perform data analysis, and propose solutions for increasing the efficiency of shaded PV panels.
Design of a Mobile Access Control Application to Optimize Security and Efficiency in the Entry and Exit of University Students in Peru
Authors: Asencios-Trujilo Lida Violeta Et al.
Published: December 2023

The main objective of this research work is to design a mobile access control application to optimize security and efficiency in the entry and exit of university students in Peru. For the development of this work, the RUP methodology was used and among its different stages the purpose of the work and the way in which this application was designed are explained. Also, we know that the technological advance with the passing of them is increasing is beneficial to use for problems that may arise in society, education, etc. in this case for the safety and efficiency of entry and exit of university students. Finally, a well-designed and properly implemented mobile application can be a powerful tool to optimize security and efficiency in the access of university students in Peru, thus contributing to a more effective and convenient educational experience for all involved.
Lane Detection and Traffic Sign Detection using Deep Learning and Computer Vision for Autonomous Driving Research Using CARLA Simulator
Authors: Hithaishi Surendra Et al.
Published: December 2023

Lane identification and traffic sign detection is the most challenging and promising problem for self-driving or autonomous vehicles with unintentional lane departure and ignorance of traffic signs being major contributing factors to motor vehicle collisions around the world. To tackle this problem the proposed work aims to detect both lane and traffic signs for autonomous vehicles. This article proposes semantic segmentation and object detection model for implementing Advanced Driver Assistance System (ADAS) applications. The applications are implemented using a variant of Convolutional Neural Networks (CNN) deep learning model such as SegNet and You Only Look Once (YOLO) algorithm. Due to dynamic and adverse environment conditions, devising and testing a system which yields effective performance in all urban driving scenarios is challenging. Hence the environment is set up virtually with the help of the Car Learning to Act (CARLA) simulator. With aid of developed models, lane and traffic signs were successfully detected and tested under various constraints. Obtained results are evaluated with various performance metrics.  Models are deployed for separate created datasets. The semantic segmentation model developed for lane detection using SegNet gives a mean average precision (mAP) of 93.33%, an overall accuracy of 94.80%, F-score of 93.42% and a minimal error rate of 5.20%. Model developed for Traffic sign detection, a mean average precision of 93.67%, an accuracy of 95.56%, recall of 92.67%, F-score of 93.16% and error rate of 4.44% have been achieved.
Crack Localization and Detection in Small-Scale Reinforced Concrete Beams With Smart Technology
Authors: Ipsita Samal Et al.
Published: December 2023

Reinforced concrete structures form the backbone of modern civil engineering, yet the emergence of cracks poses a significant challenge to their long-term integrity. The integration of smart sensors and data analytics further augments precision by enabling real-time data collection and analysis, allowing for early intervention. Continuous monitoring, facilitated by remote sensing and wireless communication, ensures a dynamic understanding of crack propagation. To validate the proposed approach, an experimental campaign was conducted using reinforced concrete beams. Three point bending tests were conducted on two small-scale reinforced concrete beams. Different configurations of SEC arrays were used on the two specimens to assess the capacity and limitation of the proposed approach. Results show that the sensing skin was capable of detecting and localizing cracks that formed in both specimens.
An Effective and Efficient Intrusion Detection System of Network Attacks Using Stacked CNN and Voting Technique
Authors: Swati Mirlekar Et al.
Published: December 2023

IDS are crucial to network security because they can identify malicious activity and halt it in its tracks. Network intrusion data is often masked by a sea of benign data, making it difficult to train a model or perform a detection with a high FPR. This is because networks are inherently dynamic and change over time. In this research, we offer a ML & DL model-based method to ID, and we demonstrate how to deal with the issue of data imbalance by using a hybrid sampling technique. Conventional firewalls and data encryption technologies are unable to provide the level of security required by current networks. As a result, IDSs have been endorsed for use against network threats. Recent mainstream ID approaches have benefited from ML, but they have low detection rates & need a lot of feature engineering to be truly useful. Using layered CNN and Voting classifier (XGBoost and LGBM), this study introduces ML-DL-NIDS to address the issue of subpar detection precision. Using a publicly available NSL-KDD & UNSW-15 benchmark datasets for network intrusion detection, we find that this model outperforms competing methods according to accuracy and F1-score obtained from experimental evaluations.
A Novel Blockchain-Reinforcement Learning Framework for Securing Wireless Sensor Networks with Energy Efficiency
Authors: Satpal Singh Et al.
Published: December 2023

This paper introduces a novel approach to Reinforcement Learning (RL), focusing on the development and implementation of a Q-learning based algorithm. Reinforcement Learning, a critical branch of machine learning, enables agents to make decisions by interacting with their environment and learning from the consequences of their actions. Our study emphasizes the Q-learning model, a popular, model-free, off-policy algorithm that offers a robust framework for agents to learn optimal strategies in diverse settings. By iteratively updating the action-value function (Q-function) based on observed rewards and future reward estimations, our algorithm aims to achieve efficient learning and decision-making. This work contributes to the field by providing a detailed algorithmic structure, complete with mathematical formulations, that facilitates a deeper understanding of the Q-learning process and its practical applications in various domains.
A Spatial Analysis Model for Proactive Assessment on Radioactive Isotopes in Environment
Authors: K. Revathi Et al.
Published: December 2023

— In general, no one can escape from radiation. Radiation exists in various forms as minerals in earth and water, cosmic rays in space, radioactive energy in human body, man-made effects as in the form of industrial waste, result of nuclear weapon test, nuclear reactor and medical procedures. The people those who are living closer to the environment where radioactive emission is found to be high, would have a high risk of getting significant health aliments like skin burns and radiation sickness. Moreover people who receive constant exposure to lower level of radiation will susceptible to have cancer and cardio vascular diseases in their life time. The primary motive of the proposed work is to evaluate the concentration of the radioactivity substances, tracking the impact through present and futuristic patterns using deep learning techniques.
High Isolation Wideband MIMO Antenna without Decoupling Technique for IoT Applications
Authors: Ashwini Kumar Et al.
Published: December 2023

This paper presents a method a high isolation wideband MIMO antenna without using any decoupling technique. This is achieved by transforming a strip line to a 1 ×2 MIMO antenna using Hilbert transform and Defected Ground structure is used in the antenna to resonate the antenna in for IOT and  5G sub-6 GHz bands. The proposed antenna has a size of 50 × 49.8 × 1.6 mm 3 and operates in the frequency band of 4.6 GHz to 5.94 GHz. The antenna simulated in ANSYS HFSS showed that its parameters Envelope correlation coefficient (ECC), Total Active Reflection Coefficient (TARC), Diversity gain (DG) and Channel Capacity Loss (CCL) are less than 0.04, -25 dB, 9.98 to 10 dB and less than 0.4 bits/s/Hz respectively. The radiation pattern of the antenna in both E-plane and H-plane has been simulated which the uniform distribution of power in the space.
Enhancing DDoS Attack Detection in SDNs with GAN-Based Imbalanced Data Augmentation
Authors: Konda Srikar Goud Et al.
Published: December 2023

Securing computer networks has become crucial due to the ongoing emergence of diverse network attacks. The popularity of Software Defined Networks (SDN) has risen because of its ability to enhance network agility, efficiency, and adaptability to recent networking challenges. However, it is essential to note that SDNs, which depend on centralized controllers, can be severely affected by Distributed Denial of Service (DDoS) attacks. The threat of DDoS attacks has grown exponentially, resulting in the evolution of robust Machine Learning-based DDoS attack detection systems within SDN. DDoS attack detection systems may deliver poor performance when trained on imbalanced datasets. Traditional techniques for handling imbalanced datasets need to be revised. Recent advances in generative adversarial networks (GANs) have revealed significant potential in generating synthetic data while preserving the probability distribution of the original data. This innovative procedure offers a promising solution to mitigate the challenges of imbalanced data in DDoS attack detection. To address challenges originating from imbalanced training datasets, we employed Generative Adversarial models to generate adversarial attacks from one viewpoint and evaluate their quality from another perspective. We chose Generative Adversarial Networks (GANs), Bidirectional GANs (Bi-GANs), and Wasserstein GANs (WGANs) based on extensive usage and reliability criteria in various domains. We conducted a comprehensive assessment to evaluate their effectiveness and resilience in generating high-quality attacks. It helps to develop, train, and fine-tune machine and deep learning models to estimate their impacts. We utilized NSL-KDD and CICIDS-2017 datasets to ensure generalization, implementing both ML and DL approaches. The outcomes demonstrate that the WGAN model outperformed GAN, Bi-GAN, and the models trained on the original imbalanced dataset and traditional sampling techniques in binary and multiclass classifications for both datasets.
Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques
Authors: Kulvinder Singh Et al.
Published: December 2023

Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval.
Enhancing Perinatal Outcomes in Gestational Diabetes Mellitus: A Midwifery Facilitator Based Approach
Authors: Debajani Nayak  , Pravati Tripathi  , Prasanta Kumar Nayak  , Madhusmita Nayak  , Nihar Ranjan Das  , Monalisha Mall  , Deepanjali Patel  , Rojalin Dash
Published: December 2023

Introduction: Gestational diabetes mellitus, or GDM, is a metabolic disorder that affects both expectant mothers and their developing fetuses. It increases the chance of several pregnancy and delivery complications. Babies born to women with gestational diabetes mellitus (GDM) are at risk for perinatal death, fetal macrosomia, low blood sugar, birth trauma, and congenital malformations. This paper explores the possibility of enhancing perinatal outcomes in GDM by applying a midwifery facilitator-based approach. Objectives: This study's primary objective is to assess the impact of a midwife facilitator-based approach on the management of gestational diabetes mellitus (GDM) and its associated complications in expectant mothers and their babies. Our objective is to create a midwifery facilitator approach model that provides recommendations for clinical practice. Methods: A prospective cohort study involving 111 mothers compared standard care with care led by midwives. The intervention group was chosen based on their preferences, and they received full support, which included education, lifestyle modifications, and assistance for mothers. A certified diabetes educator led the classes, which covered a variety of subjects like multiple pregnancies, chronic illnesses, and absences. Group 1 comprised 62 mothers who attended classes and follow-ups, while 49 mothers in Group 2 did not attend any of them. Consistency and low turnover were assured. Results: The Midwifery Facilitator-Based Approach, which considers psychological, emotional, and physical factors, fully manages GDM. Strict diabetes management, nutritional counseling, promoting a healthy diet, and aiding in stress reduction are all made possible by these midwives. They empower mothers with GDM to take care of themselves and allow informed decision-making, potentially improving perinatal outcomes and the general well-being of GDM-affected families. Conclusion: A promising strategy for improving perinatal outcomes in GDM is the facilitator-based approach used by midwives. It offers a treatment plan that is holistic in nature, taking into account the relationships between various aspects of health. By integrating midwifery into GDM management, we can enhance mothers' empowerment and comprehension, which will ultimately benefit moms and their infants.
The Influence of Destination Image on Tourists' Behavioural Intentions: Explore How Tourists' Perceptions of a Destination Affect Their Intentions to Visit, Revisit, or Recommend It to Others
Authors: Yuan Fei , Vui Chok Nyen
Published: December 2023

Tourism is a dynamic and highly competitive industry, with destinations worldwide vying for the attention of travelers. Central to tourists' decision-making processes are their perceptions of a destination, often encapsulated in the concept of "destination image." This study seeks to unravel the intricate relationship between destination image and tourists' behavioral intentions, specifically examining how tourists' perceptions of a destination influence their intentions to visit, revisit, and recommend it to others. To achieve this, a comprehensive review of the literature on destination image and its impact on tourists' behavior was conducted. Empirical research findings from various destinations and cultural contexts were analyzed to provide insights into the multi-faceted nature of destination image and its effects on tourists' behavioral intentions. The study explores the dimensions that contribute to the formation of destination image, including cultural, natural, social, and marketing factors. The research reveals that tourists' perceptions of a destination significantly affect their intentions. A positive destination image often leads to a higher likelihood of initial visitation, repeat visitation, and word-of-mouth recommendations to others. Conversely, negative perceptions may deter potential tourists and discourage repeat visits or advocacy. Furthermore, the study discusses the role of destination marketing and management in shaping and enhancing destination image. Strategies such as effective communication, experiential marketing, and sustainable development are identified as critical elements in managing and improving destination image. The findings of this study provide valuable insights for destination marketers, policymakers, and industry stakeholders. Understanding the dynamics of destination image and its impact on behavioral intentions can inform the development of more targeted and effective marketing campaigns, as well as sustainable destination management practices. Ultimately, a positive destination image can not only attract more tourists but also foster a loyal and enthusiastic visitor base, contributing to the long-term success and sustainability of tourism destinations
Electronic Tranding Platform and Data Flow
Authors: Nodira Latipova , Larisa Marisheva
Published: December 2023

This article discusses the features and trends of process of development and creation of a multi-functional electronic trading platform is aimed at efficient and profitable interaction of suppliers and buyers. Interaction takes place through electronic communication systems, so it is always operative and relevant. Electronic trading platform is a complex of information and modern technical solutions. There are several types of electronic trading platforms - for commercial customers, for placing government orders. The sites on which electronic transactions are performed by commercial customers are divided into specialized and multi-profile. Users, participants of the sites, held auctions and trades themselves can decide on which of the sites it is more convenient and profitable to work with. In addition, on a multiprofile resource the customer can act as a supplier, the seller - this is dependent on the scope of his activities, on the possibilities.
Bioinformatics Based: in Silico Docking Analysis of Polyherbal Formulation for The Management of Parkinson’s Disease (Nadukku Vatham)
Authors: Dr. Dharshini Priya G  , Dr. Suguna M  , Dr. Dhineshraman G  , Dr. Priyadharshini S  , Dr. Jeeva S  , Dr. Kavitha S  , Dr. Aravindan senbagaraman R  , Dr. Suburaj S  , Dr. Amala Hazel A.M  , Dr. Meenakshi Sundaram M  , Dr. Meenakumari R
Published: December 2023

Background: The discipline of Siddha medicine, particularly herbal formulations, can benefit greatly from the use of molecular docking because it enables the molecular interactions of the formulation’s lead molecules with receptors to be understood, as well as the inference of the formulation's basic biochemical targets.  Aim: The goal of this study is to carry out an In-silico computational analysis of the phytochemicals found in Kuruver Kudineer (KK), a traditional Siddha remedy that is used for managing behavioral deficit in Parkinsons disease.  Methodology: The ligand structures were developed and optimized using Auto Dock Tools (Morris, Goodsell et al., 1998). Using Auto dock Vina, the compounds were all docked. The function of the target protein Monoamine Oxidase -A (PDB 2Z5X), which is involved in the breakdown of the neurotransmitters by MAO-A, will be inhibited by the creation of a hydrogen bond between phytocomponents and the target's core amino acids (Tyr 69, Ile 335, Tyr 407, and Tyr 444). In order to control the dopamine level, phytocomponents that inhibit the target enzyme MAO-A may be used as potential targets. Results: The compounds present in Kuruver Kudineer (KK) like Gingerenone-A, Betulinic acid, Zingiberene, Rutin, Geniposide and β-sitosterol showed maximum interactions with MAO –A when compared to that of Clorgyline. According to the outcomes of the computational investigation, the bio-active substances present in the Siddha formulation Kuruver Kudineer (KK) have significant affinity to the target MAO-A (PDB 2Z5X). Conclusions: From the results of the present study, it was concluded that the MAO – A reveal significant effect to managing the behavioral deficit and thereby considered an excellent drug choice for the clinical management of Parkinson’s disease (Nadukku vatham)
Comparison of the variability and nitrogen-fixing activity of rhizobia strains isolated from Trifolium Hybridum L. and Galegaorientalis Lam. nodules at different stages of plant vegetation
Authors: Baymiev Andrei Kh. , Koryakov Igor S. , Akimova Ekaterina S. , Vladimirova Anastasiya A. , Matniyazov Rustam T. , Baymiev Alexei Kh.
Published: December 2023

BACKGROUND: The beginning of the life cycle of a leguminous plant in its natural habitat is usually associated with interaction with nodule bacteria in order to form a nitrogen-fixing symbiosis. In a short period of time, a plant needs to “choose” suitable microsymbionts for itself. Since a wide variety of rhizobial strains is formed in the rhizosphere of legumes, the choice made by the macrosymbiont will further influence its productivity.
AIM: The purpose of our work was to compare the principles of selection by different plants of their microsymbionts at different stages of plant development.
MATERIALS AND METHODS: Nodule bacteria Trifolium hybridum L. and Galegaorientalis Lam. were taken into the study. Their genetic diversity was studied by the RAPD method, a phylogenetic analysis of bacteria and their symbiotic nodC and nifH genes was carried out, and their nitrogen-fixing activity was assessed.
RESULTS: It was found that the rhizobia that form nodules on the roots of the studied leguminous plants at different stages of their vegetation have certain patterns. It was found that the highest polymorphism and specific nitrogen-fixing activity are characteristic of bacteria obtained from nodules formed at the initial stage of vegetation.
CONCLUSIONS: We assume that the plasticity of the rhizobia genome gives the host plant the ability to more flexibly adjust its nitrogen-fixing apparatus to changes in growing conditions.
Los trastornos neurocognitivos y retos de la epigenética
Authors: Marenco Salazar Juan David , Mejía Barrera José María , Pérez Martínez Javier Alfredo , Sánchez Salcedo Said Samir , Pugliese Jiménez Alfredo
Published: December 2023

Introducción: Los Trastornos neurocognitivos se definen como un conjunto de anomalías en la capacidad del aprendizaje y la memoria de un individuo. En múltiples estudios se han encontrado variados marcadores genéticos que están emergiendo a raíz de la evidencia de que distintos factores ambientales tienen la capacidad de influir en la expresión de dichos marcadores. Objetivo: Recopilar información actual acerca de las interacciones entre enfermedades neurológicas y la epigenética en el desarrollo de los trastornos neurocognitivos, siguiendo la metodología prisma para revisiones sistemáticas sobre los aspectos genéticos y epigenéticos que participan en la función sináptica y la memoria. Conclusiones: Los mecanismos epigenéticos tienen un papel crucial en el control de los procesos de modificación sináptica, en la formación y desarrollo de las funciones cognitivas. Alteraciones en estos mecanismos producen déficit cognitivo y de memoria en padecimientos neurodegenerativos. Los resultados obtenidos en diferentes modelos muestran un escenario promisorio con tratamientos potenciales para algunos padecimientos.
Endoscopic Management of Biliary Obstruction in Patients with Post-Surgical Anastomosis and Special Situations: A Concise Review
Authors: Kapil D. Jamwal  , Rajesh K. Padhan  , Atul Sharma
Published: December 2023

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The Role of Probiotics, Prebiotics and Synbiotics in Adult Gastrointestinal Health
Authors: Jin Yu Chieng  , Yan Pan
Published: December 2023

Gastrointestinal (GI) health is important because a healthy gut can maintain our general health and protect our body from infection or illness. Apart from vitamins and minerals, probiotics, prebiotics, and synbiotics have become increasingly famous as a supplement in our daily diet. Previously, a large number of studies had been performed to study the efficacy of probiotics in the prevention or treatment of illnesses. The purpose of our review is to discuss recent data on the benefits of probiotics in human’s GI health. Promising results on the effect of probiotics in the treatment of inflammatory bowel disease, particularly ulcerative colitis and pouchitis have been obtained from studies. Other reports also showed that a few probiotics can improve symptoms in irritable bowel syndrome. Saccharomyces boulardii was shown to prevent traveler’s diarrhea but further studies are needed for firm conclusions. Lactobaccilus rhamnosus and S. boulardiiare recommended in the treatment of acute infectious diarrhea. There are promising indications that probiotics could be useful in the prevention or treatment of ntibiotic-associated diarrhea, and a Lactobacillus-containing combination has been shown to prevent diarrhea caused by Clostridioides difficile. Addition of probiotics to current Helicobacter pylori eradication regime can further increase the eradication rate.