bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Mining Technique for Breast Cancer Prediction using Fuzzy Theory
Authors: Parul Bhatnagar Et al.
Published: December 2023

In order to find a reliable approach of breast cancer prediction, Data mining methods are used in the studies provided in this article. This study compares multiple patient clinical data in order to find a reliable model that can predict the occurrence of breast cancer. In this article, the support vector machine (SVM), artificial neural network (ANN), naive bayes classifier, and AdaBoost tree are used as four data mining methods. Furthermore, since it has such a significant impact on the efficacy and efficiency of the learning process, feature space is extensively examined in this work. Combining PCA with other data mining algorithms that use a PCA-like technique to compress the feature space is recommended. This hybrid is intended to assess the effect of feature space reduction. Wisconsin Breast Cancer Database (1991) and Wisconsin Diagnostic Breast Cancer (1995) are two frequently used test data sets that are used to assess the effectiveness of these algorithms. To calculate each model's test error, the method of 10-fold cross-validation is used. The findings of this research show a thorough trade-off between these tactics and also provide a thorough assessment of the models. In practical applications, it is anticipated that feature identification results would help to avoid breast cancer for both doctors and patients.
IOT Empowered Helmets: Pioneering Safety in the Mining Sector
Authors: Puja Cholke Et al.
Published: December 2023

Mining sector is the most prominent to accidents. The accident rate in mining is about 87% .Mining activity involves a lot of tedious and time-consuming tasks as well. Out of the various risks associated with mining there exists many different types of hazardous gases like carbon monoxide, hydrogen sulphide,methane and excess of carbon dioxide. Mining also includes the risk of tremors and landslides as well. Along with that there is the danger of falling of large and bulky objects as well. On top of that removal of helmet is even more dangerous. Moreover ,monitoring each and every miner is also an important factor when it comes to mining. As a result there are lot of risks associated with the same. At times these risks can be life-threatening and result into fatal error .Lifesaving helmet for miners is an IOT based complete protection of miners. As the traditional model of smart helmet doesn’t take all the above mentioned safety threats into consideration our aim through this proposed system is to develop a complete safety equipment in the form of helmet to safeguard miners from the various dangers present in the mining industry.
FLBP: A Federated Learning-enabled and Blockchain-supported Privacy-Preserving of Electronic Patient Records for the Internet of Medical Things
Authors: Satyanarayana Reddy Goluguri Et al.
Published: December 2023

The evolution of the computing paradigms and the Internet of Medical Things (IoMT) have transfigured the healthcare sector with an alarming rise of privacy issues in healthcare records. The rapid growth of medical data leads to privacy and security concerns to protect the confidentiality and integrity of the data in the feature-loaded infrastructure and applications. Moreover, the sharing of medical records of a patient among hospitals rises security and interoperability issues. This article, therefore, proposes a Federated Learning-and-Blockchain-enabled framework to protect electronic medical records from unauthorized access using a deep learning technique called Artificial Neural Network (ANN) for a collaborative IoMT-Fog-Cloud environment. ANN is used to identify insiders and intruders. An Elliptical Curve Digital Signature (ECDS) algorithm is adopted to devise a secured Blockchain-based validation method. To process the anti-malicious propagation method, a Blockchain-based Health Record Sharing (BHRS) is implemented. In addition, an FL approach is integrated into Blockchain for scalable applications to form a global model without the need of sharing and storing the raw data in the Cloud. The proposed model is evident from the simulations that it improves the operational cost and communication (latency) overhead with a percentage of 85.2% and 62.76%, respectively. The results showcase the utility and efficacy of the proposed model.
Image based Chili Crop Disease Prediction Using Deep Transfer Learning
Authors: Pallepati Vasavi Et al.
Published: December 2023

Crop diseases have a terrible impact on food protection and can result in considerable reductions in both the supply and quality of agricultural products. Human professional have traditionally been relying on to diagnose crop diseases caused by insects, pests, virus, bacteria, fungal, inadequate nutrition, or adverse environmental conditions. This, however, is costly, time demanding, and in some situations unworkable. Thus, in the area of agricultural information, the automatic identification of crop diseases is significantly required. Many strategies have been presented to solve this challenge, with deep learning becoming as the preferred approach due to its outstanding performance.This research describes a method for detecting chili leaf diseases using a deep convolutional neural network. We compared performances of four architectures: MobileNet, Inception-ResnetV2, EfficientNetB0, and DenseNet. The proposed approach evaluated the findings using measures such as accuracy, loss and time. Our model compares favorably to EfficientNetB0 with an accuracy of 0.995, a loss of 0.023, and time is 5 minutes 45 seconds. EfficientNetB0, a compact deep learning architecture has fine tuned to classify two forms of chili leaf diseases. The method was tested on 2475 photos from the Plant Village dataset.
Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images
Authors: Vidyasri S. Et al.
Published: December 2023

Lung diseases or otherwise called respiratory diseases are airborne diseases that affect the lungs and the other tissues of the lungs. Tuberculosis, Coronavirus Disease 2019 (COVID-19), and Pneumonia are a few instances of lung diseases. If the lung disease is diagnosed and treated in the initial stage, the chances of recovery rate and long-term survival rates can be increased. Usually, lung disease is identified by Chest X-Ray (CXR) image examination, skin test, sputum sample test, Computed Tomography (CT) scan examination, and blood test. Because of its non-invasive and convenient evaluation for overall outcomes of the chest situation, Lung disease can be detected by specialized radiologists on CXR images. In recent times, Deep Learning (DL) applies to medical images for disease detection and has proved an effective technique for detecting disease. The recent advancement of DL supports the detection and classification of lung diseases in medicinal imaging. This article presents an Automated Lung Disease Detection Using Quantum Glowworm Swarm Optimization with Quasi Recurrent Neural Network (QGSO-QRNN) model on CXR imaging. The presented QGSO-QRNN technique focuses on the identification of lung diseases using DL concepts. To accomplish this, the presented QGSO-QRNN technique initially performs image pre-processing by the use of the Gaussian Filtering (GF) technique. Besides, the Faster SqueezeNet approach is exploited for feature vector generation. Finally, the QRNN model is applied for precise classification of lung diseases with the QGSO technique as a hyperparameter optimizer. The investigational assessment of the QGSO-QRNN technique is examined by employing standard medical datasets and the outputs display the promising performance of the QGSO-QRNN technique over other existing techniques by means of diverse measures.
A Smart Management System For Garbage Classification Using Deep Learning
Authors: Nagaraj Et al.
Published: December 2023

Thanks to the development of artificial intelligence (AI), the outdated trash system now offers better time monitoring and enables for better waste management. The purpose of this paper is to develop a smart sterile management system using a Tensor Flow-based deep learning model. In real time, it recognizes and categorizes items. Metal, plastic, and paper waste are separated from other sorts of trash in the bin's several divisions. Object detection and garbage classification are carried out using the Tensor Flow framework and a trained object recognition model. In order to create a frozen inference graph that can be used to recognize things using a camera, this trash detection model is trained on garbage photographs.
Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing
Authors: Manish Chhabra Et al.
Published: December 2023

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.
Detection of HIV by using Rough Set and Homotopy Analysis Method
Authors: Subrata Kumar Nayak Et al.
Published: December 2023

The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV & AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV & AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST & HAM over other soft computing techniques and Mathematical Modelling as both RST and Homotopy Analysis (HAM) because RST can handle vague and imprecise data efficiently, and HAM is a suitable technique for finding analytical solutions. We have used the chi-squared test to validate our claim.
An Overview of Deep Learning Networks for Remote Sensing Applications
Authors: Gottapu Santosh Kumar Et al.
Published: December 2023

To study and understand the world around us, remote sensing specialists rely on aerial and satellite photographs. Today, deep learning models necessitating extensive data or specialised data are employed in many remote sensing applications. Sometimes, the spatial and spectral resolution of Observation satellites of the planet earth will fall short of requirements due to technological constraints in optics and sensors, as well as the expensive expense of upgrading sensors and equipment. Insufficient information might reduce a model's efficiency. The efficiency of deep learning frameworks that rely on data can be improved by the use of a adversarial networks, which is a type of technique that can generate synthetic data. This is one of the best innovative developments in Deep Learning  in past decade. GANs have seen rapid adoption and widespread success in the Remote Sensing sector. GANs can also perform picture-to-image translation, such as clearing cloud cover from a satellite image.This paper aims to investigate the applications of different Adversarial Networks in the remote sensing area and the databases used for training of GANs and metrics of evaluation.
IOT Based Continuous Glucose Monitoring for Diabetes Mellitus using Deep Siamese Domain Adaptation Convolutional Neural Network
Authors: Sathish N Et al.
Published: December 2023

The phrase "Internet of Things" (IoT) refers to the forthcoming generation of the Internet, which facilitates interaction among networked devices. IoT functions as an assistant in medicine and is critical to a variety of uses that monitor healthcare facilities. The pattern of observed parameters can be used to predict the type of the disease. Health experts and technologists have developed an excellent system that employs commonly utilized technologies like wearable technology, wireless channels, and other remote devices to deliver cost-effective medical surveillance for people suffering from a range of diseases. Network-connected sensors worn on the body or put in living areas collect large amounts of data to assess the patient's physical and mental wellbeing. In this Manuscript, IoT -based Continuous Glucose Monitoring for Diabetes Mellitus using Deep Siamese Domain Adaptation Convolutional Neural Networ k (CGM-DM- DSDACNN) is proposed. The goal of the work that has been described to investigate whether Continuous Glucose Monitoring System (CGMS) on the basis of IoT is both intrusive also secure. The job at hand is for making an architecture based on IoT that extends from the sensor model to the back-end and displays blood glucose level, body temperature, and contextual data to final users like patients and doctors in graphical and text formats. A higher level of energy economy is also attained by tailoring the Long range Sigfox communication protocol to the glucose monitoring device. Additionally, analyse the energy usage of a sensor device and create energy collecting components for it. Present a Deep Siamese Domain Adaptation Convolutional Neural Network (DSDACNN) as a last resort for alerting patients and medical professionals in the event of anomalous circumstances, like a too -low or too-high glucose level.
An Optimized Deep Learning Framework For Pothole Detection
Authors: Sridhar Patthi Et al.
Published: December 2023

Pothole detection plays a crucial role in preventing road accidents and is effective in establishing road maintenance and safety. Although various pothole detection models are designed to accurately identify the pothole based on road images, they face issues in accuracy and hyperparameter tuning. The presented research work concentrates on developing a novel optimized deep learning model for the accurate prediction of potholes on the road infrastructure using the recurrent neural network (RNN) and grey wolf optimization (GWO). Initially, the road images are collected and pre-processed. The pre-processing includes the removal of noises, image resizing, etc., to improve the image quality. Further, texture-based feature extraction was employed to extract the most relevant features from the pre-processed image. Then, the RNN architecture was trained using the extracted features to learn the interconnections between the image features and pothole detection. In addition, the GWO fitness solution was integrated into the classification module to tune and optimize the RNN hyperparameters, which increases the detection performances such as accuracy, and reduces the loss function. Finally, the presented model was evaluated with the publically available road image detection database and the outcomes are determined. The performance assessment demonstrates that the designed model attained greater accuracy of 98.76%, and a loss function of 0.06. Furthermore, a comparative assessment was performed with existing methods to evaluate the effectiveness of the proposed model.
Big Data Network Optimization for Mobile Cellular Networks in 5G
Authors: M. Dhasaratham Et al.
Published: December 2023

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing.
Enhanced Cauchy Matrix Reed-Solomon Codes and Role-Based Cryptographic Data Access for Data Recovery and Security in Cloud Environment
Authors: Vijay R. Sonawane Et al.
Published: December 2023

In computer systems ensuring proper authorization is a significant challenge, particularly with the rise of open systems and dispersed platforms like the cloud. Role-Based Access Control (RBAC) has been widely adopted in cloud server applications due to its popularity and versatility. When granting authorization access to data stored in the cloud for collecting evidence against offenders, computer forensic investigations play a crucial role. As cloud service providers may not always be reliable, data confidentiality should be ensured within the system. Additionally, a proper revocation procedure is essential for managing users whose credentials have expired.  With the increasing scale and distribution of storage systems, component failures have become more common, making fault tolerance a critical concern. In response to this, a secure data-sharing system has been developed, enabling secure key distribution and data sharing for dynamic groups using role-based access control and AES encryption technology. Data recovery involves storing duplicate data to withstand a certain level of data loss. To secure data across distributed systems, the erasure code method is employed. Erasure coding techniques, such as Reed-Solomon codes, have the potential to significantly reduce data storage costs while maintaining resilience against disk failures. In light of this, there is a growing interest from academia and the corporate world in developing innovative coding techniques for cloud storage systems. The research goal is to create a new coding scheme that enhances the efficiency of Reed-Solomon coding using the sophisticated Cauchy matrix to achieve fault tolerance
Performance Evaluation of Underwater Routing Protocols DHRP, LASR and DFR for Underwater Wireless Sensor Network using MATLAB
Authors: Suresh Wati Et al.
Published: December 2023

Communication issues in Underwater Wireless Sensor Networks (UWSNs) are the main problem. In this research paper and we proposed “Dolphin Heterogeneous Routing Protocol” (DHRP) and it determine the most efficient path to destination, it balance the energy and it increase the lifetime of nodes. Due to the lack of growth in underwater wireless communications, Communication cables are still used for underwater communication. The use of wires to ensure the communication of sensor nodes at the ocean's depths is extremely costly. In underwater wireless sensor networks, determining the optimum route to convey sensed data to the destination in the shortest amount of time has become a major difficulty (UWSN). Because of the challenging communication medium, UWSN routing protocols are incompatible with those used in traditional sensor networks. Existing routing protocols have the problem of requiring more energy to send data packets, as well as experiencing higher delays due to the selection of ineffective routes. This research introduces the Dolphin Heterogeneous Routing Protocol (DHRP) to tackle the routing issues faced by UWSN. The swarming behavior of dolphins in search of food is the inspiration for DHRP. In order to find the best route in UWSN, DHRP goes through six essential processes are initialization, searching, calling, reception, predation and termination.
Analytical Report on Metaheuristic and Non-Metaheuristic Algorithms for Clustering in Wireless Networks
Authors: Nishant Tripathi Et al.
Published: December 2023

This analytical report delves into the comprehensive evaluation of both metaheuristic and non-metaheuristic algorithms utilized for clustering in wireless networks. Clustering techniques play a pivotal role in enhancing the efficiency and performance of wireless networks by organizing nodes into meaningful groups. Metaheuristic algorithms, inspired by natural processes, offer innovative solutions to complex optimization problems, while non-metaheuristic algorithms rely on traditional mathematical principles. This report systematically compares and contrasts the efficacy of various algorithms, considering key metrics such as convergence speed, scalability, robustness, and adaptability to dynamic network conditions. By scrutinizing both categories of algorithms, this report aims to provide a holistic understanding of their respective advantages, limitations, and applicability in wireless network clustering scenarios. The insights derived from this analysis can guide network engineers, researchers, and practitioners in selecting the most suitable algorithms based on specific network requirements, ultimately contributing to the advancement of wireless network clustering techniques.
Modified Threshold-based Spectrum Sensing Approach for VANETs
Authors: Narendrakumar Chauhan Et al.
Published: December 2023

The Primary User (PU) signal detection in Cognitive Radio (CR) is crucial and is achieved through spectrum sensing techniques. The Energy Detection method is a commonly used technique, and selecting a proper threshold is essential to enhance the efficiency of the CR system. This research paper demonstrates the maximum achievable throughput and validates a Modified Threshold (MT) approach. The authors consider a scenario with multiple antennas at the receiver, where these antennas are correlated and subjected to mobility effects, and they employ the Energy Detection (ED) for spectrum sensing. The study analyzes the system's performance over a Nakagami-m fading channel, considering available correlations among the antenna elements. To compute important statistical values, the Moment Generating Function (MGF) method is employed. The research employs specialized mathematical functions, such as the Lauricella and confluent hypergeometric functions, to derive closed-form expressions for the Probability of Detection when employing the diversity technique. The results indicate a significant enhancement in the performance of the proposed algorithm when utilizing the modified threshold parameter across a wide range of Signal to Noise Ratio (SNR) values. Additionally, increasing the number of branches in the antenna system further improves detection performance. Interestingly, under high fading parameter conditions (m=4), the detection probability is found to be superior with exponential correlation among the L antenna elements compared to other available correlated branches.
PAPR and BER Analysis in FBMC/OQAM System with Pulse Shaping Filters and Various PAPR Minimization Methods
Authors: Allanki Sanyasi Rao Et al.
Published: December 2023

Filter Bank Multicarrier with Offset Quadrature Amplitude Modulation(FBMC/OQAM) system design based on frequency sampling prototype filter takes into account the low frequency utilization of Orthogonal Frequency Division Multiplexing(OFDM)  caused by adding Cyclic Prefix(CP). The CP decreases spectral efficiency and increases Peak to Average Power Ratio(PAPR). FBMC is an OFDM enhancement. In this paper to reduce the PAPR, we explained companding methods. We have proposed an FBMC that makes use of prototype pulse shaping filters which can be adjusted to meet system requirements in order to defeat these limitations. Due to its significant effect on the performance of FBMC-OQAM, choosing the right filter is crucial. Different prototype filters are used to investigate the performance of the FBMC-OQAM in this paper. Using the validated system, it was found that frequency utilization is more and good out-band suppression as well as an excellent application value in 5G technology. By using ?-law companding method, FBMC/OQAM provides better performance. It produces low PAPR, low out of band(OoB), high BER performance, less computational complexity and high spectral efficiency as compared to other methods.
An Enhanced Automated Identification of Brain Tumor Cells Using Image Segmentation
Authors: Shaik Masood Ahamed Et al.
Published: December 2023

Brain tumors are a serious threat to human health, and getting a proper diagnosis quickly is essential for successful treatment. This study introduces a novel automated method that uses improved image segmentation algorithms to detect brain tumor cells in diagnostic images. The work uses cutting-edge deep learning models and novel preprocessing techniques to boost the precision and speed with which tumor cells may be identified. The procedure starts with gathering a large dataset of brain tumor images, then moves on to intensive preprocessing to improve the quality of those images. To accurately define tumor locations, a new picture segmentation approach is developed that utilizes convolutional neural networks (CNNs) and morphological operations. The segmented sections are then used to develop a deep learning classifier to recognize tumor cells. The proposed method has been shown to be effective in experiments, with a noticeable increase in the accuracy of identifying tumor cells over previous methods. On a large test dataset, the system demonstrates clinical viability with an average classification using Average polling technique of 92% and classification using Max polling technique of 98.87%. In conclusion, this study advances medical image analysis by providing a more effective automated method of identifying brain tumor cells.
Analysis of Recent Advances in Artificial Intelligence for Human Resource Management
Authors: Al-Onizat Hamdan Hasan
Published: December 2023

Perhaps people once thought AI was something out of a science fiction novel. The majority of today's working population, however, is well aware that smart technology is actively reshaping the business world. It's true despite the fact that AI has traditionally been associated with science fiction. Human resources are one of the most notable uses of AI today, but it is not an exception to the rule that AI can be used to practically any sector or profession. According to the results of a recent survey conducted by Oracle and Future Workplace, human resource professionals are optimistic that AI will open up opportunities for learning new skills and gaining more personal time. Human resource experts will be able to take on greater responsibility and play a more strategic role inside their companies as a result.
Detection of Pothole by Applying Convolutional Neural Network and Random Forest Techniques
Authors: Mukesh Kumar Tripathi Et al.
Published: December 2023

Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formation of potholes and cracks on the roads, which can cause damage to vehicles or pose a physical danger to occupants, particularly in underdeveloped countries. Identifying potholes in real-time can help drivers avoid them and prevent accidents. Furthermore, recording their locations and sharing them can assist other drivers and road maintenance organizations take prompt corrective measures. In our attempt to address the issue of pothole detection, we aim to combine the latest technological advancements. We aim to develop practical, reliable, adaptable, and modular solutions. To achieve this, we will compare the performance of Random Forest, a machine learning model, with CNN, a deep learning model, in detecting potholes. We will train these models using multiple datasets and analyse their performance to determine their effectiveness in pothole detection.
Detection and Avoidance of Objects Robot with Internet Connectivity and Signal Strength-Based Location Tracking
Authors: B. Bala Abirami Et al.
Published: December 2023

This paper presents an object detection and avoidance robot with location tracking and internet connectivity capabilities. The robot is equipped with a hardware module that detects the highest frequency rate of radio wave signals and sends the detected signal, then from color sensors to the object avoidance robot to take a decision in search of the best signal strength. The robot moves forward in search of a better signal strength, then stays back if the signal is stronger or retreats if it is weak. When the signal strength is strong at a location, the robot informs the user of its current location and provides internet connectivity through a single-band dongle with a SIM card reader. Additionally, if the user wants the robot to return to the user's location, it backtracks the path with the help of an object detection and avoidance algorithm, whereas the user can track its activity. The proposed system has the potential to improve location tracking by object detection and avoidance in various applications, such as surveillance, search and rescue missions, military operations, and internet tracking connectivity for handicapped people in open spaces or remote areas. The proposed system here is used to track network signals and is predefined with an object detection and avoidance algorithm.
Enhancing the Security and Privacy of eHealth Records through Blockchain-based Management: A Comprehensive Framework
Authors: Abida Khanam Et al.
Published: December 2023

Progress in information technology is transforming the healthcare sector with the goal of enhancing medical services, diagnostics, and continuous monitoring through wearable devices, among other benefits, while also lowering expenses. This digital transformation enhances the convenience of computing, storing, and retrieving medical records, ultimately leading to improved treatment experiences for patients. Electronic health record systems have come under fire for centralized control, faults, and attack points with transferring data custodians. These systems are frequently utilized for the interchange of health information among healthcare stakeholders. The main objective is to overcome information asymmetry and data breaches commonly encountered in the Electronic Health Record (EHR) system. This study introduces a decentralized and trustless architecture aimed at securely storing patients' medical records and granting access to authorized individuals, including healthcare providers and patients themselves. The research primarily focuses on bolstering the security and privacy of healthcare data management systems using blockchain technology. To address the issue of blockchain scalability, an off-chain scaling approach is proposed, utilizing an underlying medium to store large volumes of data. This is achieved through the integration of Elliptic Curve Cryptography (ECC) and the Interplanetary File System (IPFS). The proposed system provides a secure and efficient method for storing and sharing sensitive healthcare data while ensuring confidentiality and data integrity.
Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers
Authors: Sushree Gayatri Priyadarsini Prusty Et al.
Published: December 2023

Despite huge advancements in medical fields, diseases like cancer continue to plague people since we are still prone to them. The main purpose is to design a critical analysis of cancers, including breast, pancreatic, colon, skin, lung, and other cancers from the year 1990 to 2019, to identify some of the rising factors of both lung and colon cancers that cause unnecessary deaths. Thus, early detection on the other hand greatly improves the chances of survival rates in humans. At that place, Transfer Learning (TL) techniques have made significant improvements these days.

Material and Methods: We herein implemented the exploratory data analysis (EDA) technique to analyze 29 types of cancer deaths worldwide since 1990th century. Between them, we have emphasized lung cancer (LC) and colon cancers (CC) for our study and taken 25,000 histopathology image data from the publicly available Kaggle repository. Further, we proposed a novel methodology using the EfficientNetB7 model defining each step to classify five types of lung and colon tissues (two benign and three malignant) from the histopathological images. Although, an algorithm has been designed that clearly describes the workflow of our proposed method. This experiment has been done through input_shape=(X, Y, 3) on the ‘ImageNet’ dataset, Adam optimizer, and accuracy metrics using Python 3.8.8 software on Jupyter 6.4.3 environment.

Results: We evaluated the model progress using 50 epochs during the training phase, which resulted in more than 98% accuracy and less than 2% loss in both the training/ validation phases. Besides that, we designed a classification report that describes the performance of our model, where we achieved more than 98.07% accuracy score, 98% precision score, 98% of recall score, and 98% of f1-score. These results signify that implementing an optimization technique on the EfficientNetB7 model improves the overall performance as compared to 93.19% for LC 1 and 93.91% for CC 2 for the RestNet50 model.

Conclusion: A novelty of this research specifies the methodology to predict LC and CC by applying the EfficientNetB7 model to the cancer dataset which might be beneficial for both doctors as well as healthcare firms.
Differentiation of Identical Twins by Facial Morphological Comparison: An Exploratory Study and Implications for Forensic Science
Authors: Enrique Lee Huamaní Et al.
Published: December 2023

— This study aimed to explore the ability of facial morphological comparison to differentiate monozygotic twins and identify which facial components were most useful for this purpose. The research was carried out on facial images of 09 pairs of twins (18 people), where 12 facial components were identified using the morphological comparison method. Each of these components were compared in each pair of twins, so we identified those components that were similar or different. Subsequently, the frequencies of similarities and differences for each facial component were calculated. Next, an analysis of variance was applied between the components identified as different and similar. The results suggested that such a method was useful for differentiating identical twins and that some facial components were more useful than others. In this sample, facial markings and the ear were the most discriminating components. These results would set the tone for future research in this area.
Efficient Inductive Transfer Learning based Framework for Zero-Day Attack Detection
Authors: Gunupusala Satyanarayana Et al.
Published: December 2023

An Intrusion Detection System (IDS) is a type of security domain that tracks and evaluates network connections or system operations to detect potential security breaches, unauthorized usage, and malicious activity within computer networks. Machine learning (ML) and deep learning (DL) algorithms provide better IDS based on the labelled dataset. However, due to a lack of labelled data, its effectiveness in detecting zero-day attacks is limited. Anomaly detection methods frequently produce high False Positive Rates (FPR). Transfer learning (TL) is a powerful technique in various domains, including intrusion detection systems (IDS). It also creates advanced classifiers using knowledge extracted from the related source domain(s) with little or no labelled data. This paper introduced zero-day attack detection (ZDAD) model by combining it with transfer learning that helps classify the attacks and non-attacks from the given dataset. Using the UNSW-NB15 dataset, the authors created a Transfer Learning-based prototype in this study. The goal was to unify the feature space for distinguishing unlabeled Generic samples representing zero-day attacks from regular instances using labelled DoS samples. The ZDAD performed admirably, achieving 99.24% accuracy and a low False Positive Rate (FPR) of 0.02%. This performance outperforms current state-of-the-art methods.
Practical Research on Project-Based Learning (PBL) in Film and Television Production in Xiamen Vocational Education
Authors: Youyou Lyu , Kit Ang Chun
Published: December 2023

The film and television industry plays a crucial role in the development of the global cultural sector. In recent years, vocational education in the field of film and television has experienced rapid growth in China. However, the current talent training model for this profession fails to meet the demands of the fast-paced industry development and lacks effective support for its advancement. Project-based learning is a student-centered teaching approach that employs authentic projects as the primary medium for learning. This study presents an empirical investigation conducted in a vocational college in Xiamen, where project-based learning was incorporated into the film and television production courses to assess its effectiveness. The findings of this research demonstrate that the implementation of project-based learning in the context of film and television production is viable. In comparison to traditional didactic instruction, project-based learning significantly enhances students' motivation to learn, practical skills, critical thinking abilities, and teamwork abilities. Consequently, it holds significant value in cultivating applied talents.
Model for Increasing the Economic Competitiveness of MSMEs in the Creative Industry Sector in Indonesia
Authors: Deri Firmansyah  , Eeng Ahman  , Disman  , Hari Mulyadi  , Asep Achmad Rifa’i  , Asep Suryana
Published: December 2023

This study predicts and analyzes the mediating role of innovation capability (IC) on the relationship between digital transformation (DT) and economic competitiveness (CP) of SMEs, by proposing model construction. The survey method was carried out by distributing research instruments to 98 respondents, the collected data was used to analyze the relationship between digital transformation, innovation ability and competitiveness, also used to test hypotheses. Evaluation of predictions through structural equation models with partial least squares-structural equation modeling (PLS-SEM). The results of model measurement and the results of the evaluation of model suitability show that model suitability is achieved and guarantees model suitability. The results of the structural model path analysis show that all hypotheses are accepted, and IC has a mediating effect on the effect of digital transformation on the economic competitiveness of MSMEs. There is an important component that must be considered to strengthen the contribution of DT and IC to the improvement of CP, namely a set of competencies and resources that must be prepared. A model of increasing the economic power of MSMEs in the creative industry sector based on innovation and digital transformation capabilities involving a set of competencies including digital competencies of human resources and economic resources as fundamental inputs that strengthen them, becomes this novelty to fill the existing gap. The contribution of this research broadens the horizons on the effectiveness of highly competitive businesses in this era, where digital transformation capabilities, digital skills and innovation capabilities must be prepared.
Nivel de conocimiento sobre toxoplasmosis en mujeres gestantes que asisten a consulta prenatal
Authors: Chávez Arévalo Mauricio Andrés , Roa Correa Stephani Tatiana , Terraza Cadavid Jorge Andrés , García Cuan Aracely
Published: December 2023

Objetivo: En este trabajo se investigó el nivel de conocimiento sobre toxoplasmosis y sus factores de riesgo en mujeres gestantes que asisten a control prenatal, debido a que el bajo nivel de conocimiento de esta infeccion puede relacionarse directamente con aumento en su incidencia por la pobre percepcion de la poblacion respecto a los riesgos y consecuencias a futuro.

Metodología: A una muestra aleatoria de 93 mujeres en estado de embarazo, quienes estaban llevando sus controles prenatales en la Clinica General San Diego de Barranquilla, en el segundo trimestre de 2021, se aplicó una encuesta con preguntas de tipo binario relacionando el entendimiento sobre Toxoplasmosis y factores de riesgo para contraer la infecccion. La encuesta fue dividida en dos partes: una parte antes y la otra despues de una charla informativa sobre Toxoplasmosis, factores de riesgo, consecuencias para la salud y métodos de prevención. Se tuvo en cuenta, antecedentes clinicos, controles prenatales y nivel socioeconomico de las participantes.

Resultados: Se encontró que el 64,5% de las embarazadas desconocía qué es Toxoplasmosis y 66,6% los factores de riesgo y métodos de prevención.

Conclusiones: El nivel de conocimiento sobre la infeccion por Toxoplasma gondii encontrado en las gestantes que asistieron  a control prenatal en la Clinica General San Diego, durante el periodo de estudio, es bajo.
Advances in Cell Therapy for Liver Regeneration
Authors: Bai Ling , Cheng Jin
Published: December 2023

With the development of translational medicine, cell therapy has been applied in clinical practice, especially in the regenerative treatment of severe liver diseases. This article introduces the origin, development, and prospects of cell therapy from the aspects of liver disease treatment, history, current status, and clinical application feasibility of cell therapy. The article also points out the existing problems in the translational medical research of cell therapy. To achieve a favorable development in cell therapy, more fundamental and clinical are warranted.
Cortical Visual Impairment as a Manifestation of Hepatic Encephalopathy in a Young Child: A Case Report
Authors: Eyad Altamimi  , Dua’ Samara  , Marwa Barukba  , Mohammed Sayel Alorjani
Published: December 2023

Cortical visual impairment (CVI) is an extremely rare manifestation of hepatic encephalopathy (HE), which refers to neuropsychiatric abnormalities as a result of impaired detoxification function of liver. A 16-month-old female Syrian refugee who developed jaundice 4 weeks before referral to our hospital was diagnosed with hepatitis A based on her clinical presentation at a local facility. Overtime, her jaundice worsened, her lower limbs became edematous, and the family noticed sleep disturbances and irritability. On admission to a local hospital, she was deeply jaundiced and irritable, but showed no stigmata of chronic liver dysfunction. During hospitalization, the patient lost the ability to track objects and eventually her sight. However, the results of fundoscopic and neurological examinations were normal, except for a fine tremor. Her laboratory workup showed elevated liver enzymes and ammonia, and a prolongation of prothrombin time. Brain imaging revealed an abnormal signal intensity which is suggestive of metabolic etiology. The patient was diagnosed with CVI secondary to HE and transferred to the pediatric intensive care unit. After a few days of HE management, ammonia levels dropped and vision recovered. In conclusion, CVI may indicate HE in children.
Pathologically and Bacteriologically Unconfirmed Esophageal Tuberculosis Diagnosed by Endoscopic Ultrasound Combined with the T-SPOT.TB test: A Case Report
Authors: Rui Huang  , Suyu Chen  , Juan Lin  , Hong Shi
Published: December 2023

Esophageal tuberculosis (TB) is relatively a rare disease, and its diagnosis is often unknown due to the lack of specific clinical symptoms and endoscopic manifestations, as well as the low success rate of pathological biopsy. In this case report, we describe a patient who presented with dysphagia as the chief complaint, manifested as a submucosal eminence under endoscopy. Endoscopic ultrasonography showed hypoechoic changes, disappearance of some layers and esophageal boundaries, and echo of fused lymph nodes around the esophagus. The patient had strongly positive results of the purified protein derivative skin test and T-SPOT.TB. Subsequently, the patient received treatment of anti-TB drugs, and the clinical symptoms improved. According to the patient’s medical history and endoscopic ultrasonography results, esophageal TB was the most probable diagnosis. Therefore, this case report offers new ideas that endoscopic ultrasonography and a review of clinical manifestations can be used to diagnose esophageal TB.
Importance of Nutritional Management in Gastric Cancer Treatment
Authors: Ichikawa Daisuke , Shoda Katsutoshi , Akaike Hidenori , Kawaguchi Yoshihiko
Published: December 2023

In addition to the aging of gastric cancer patients in recent years, those with advanced gastric cancer often receive suboptimal nutritional care preoperatively, given the physical impact of gastric cancer-related nutritional intolerance and tumor-bearing status. Available records indicate that poor preoperative nutritional status, especially sarcopenia, may culminate in poor short-term postoperative outcomes and diminished long-term cancer prognosis. Furthermore, it is widely acknowledged that postoperative weight loss and the loss of muscle mass have an adverse effect on the prognosis. To tackle these issues, clinical trials have been conducted on perioperative nutritional interventions which have demonstrated a certain degree of effectiveness. This paper presents recent reports emphasizing the significance of nutritional management in the treatment of gastric cancer.