bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods and technologies for evaluating the quality of higher education in the context of international standards: A comparison of the Ukrainian and Polish experience of creating ratings
Authors: Matkivskyi Mykola , Taras Tetiana
Published: April 2024

The need to study the methods and technologies used to assess the level of educational services is primarily due to constant transformations in educational processes, improvement of the system of criteria used to rank higher education institutions. The purpose of this study was to analyse the tools used to assess the performance of higher education institutions in the context of international standardization of educational processes. The main methods used in the study were statistical analysis, comparative and contrastive and analytical and synthetic methods. The comparative and contrastive analysis revealed that research activities were among the main indicators that influenced the creation of the rankings: Times Higher Education University Impact Rankings (60%), TOP-200 Ukraine (42%), Perspektywy (42%), and prestige and reputation: QS World University Rankings (40%), Times Higher Education University Impact Rankings (30%), TOP-200 Ukraine (12%). The methods used to create the rankings included statistical, pedagogical analysis, analytical and synthetic methods, and surveys. To create the Ukrainian ranking, 6 international (Webometrics, Scopus, Times Higher Education World University Rankings, Times Higher Education University Impact Rankings, UniRank, QS World University Rankings) and 4 national (participation in research project competitions, number of patents received, awards for young scientists, the ratio between the number of applications and the competition score) indicators were used. The Polish ranking was based on 29 indicators divided into groups: prestige, scientific effectiveness, scientific strength, scientific and educational potential, internationalization, graduates on the labour market, innovation, study conditions, and economic cooperation. In Poland, there was a balance between international and national criteria, while in Ukraine, about 70% of international criteria and 30% of national criteria were used. This work can be used in the future to improve national and international systems for assessing the quality of higher education, and to develop a unified mechanism for creating ratings
The entrepreneurial revolution in science research and education
Authors: Halma Matthew
Published: April 2024

There are significant challenges in both student and researcher engagement in both education and research, and one contributing factor is the lack of autonomy that each person experiences. This work charts a new path, whereby students and researchers are free to pursue their own interests, and the role of the teacher or university is to support and guide them. This article is a narrative review examined the challenges faced by the educational industry and how they can be remedied through student led, entrepreneurial education, which can take place online. This article examines the impacts of learner autonomy and present alternative apprenticeship models which can be performed inexpensively and produce value independent of the education itself. Apprenticeship, self-directed and project-based pedagogy provides pupils with the skills that they need to succeed in the modern world, differing importantly from standard education, where the relevance to employers is decreasing. While currently, scientific institutions face a crisis of innovation and a related crisis of public trust, entrepreneurial education provides a means of addressing these related problems. These changes have implications at the institutional level, as well as for the relationship between science and society. During budgetary crises, these changes present ways to provide an improved educational experience at lower cost. Overall, entrepreneurial education may be an effective and cost-effective way to perform research and to train pupils for the skills needed in the 21st century. The work provides a roadmap to how existing educational institutions can adapt to the sea change underway with online education, as well as advise new entrants on best practices
Nexus Between Financial Liberalisation and Economic Growth in Nigeria (1987-2022)
Authors: A. B. El-Yaqub , M. Ibrahim , M. Sule
Published: April 2024

This study examines the influence of banking sector liberalization on economic growth (GDP) in Nigeria by employing the Ordinary Least Square (OLS) approach. Findings from the study show a positive association between the measurement of financial deepening (FD); which implies that greater financial liberalization (FLB) aligns with GDP. A more robust domestic currency results in diminished GDP. The result further indicates a positive correlation between FDM and GDP signifying that a unit increase in FDM results in a 0.012070 unit increase in GDP, a unit increase in exchange rate (EXR), leads to a decrease of 4,705.546 unit in GDP and a unit increase in inflation (INF) leads to 5,428.744 unit increase in GDP. Therefore, the study recommends that it is essential to maintain a balanced approach to EXR management and policies should aim for stability to support GDP while considering the impact on international trade competitiveness.
Determinants of Income Inequality Sources among Women in Rural Oyo State, Nigeria
Authors: O. Amao , K. Adenegan
Published: April 2024

This paper applies the regression-based inequality decomposition approach to explore determinants of income inequality sources among women in rural Oyo State, Nigeria. A four-stage sampling procedure was used in the collection of primary data with purposive selection of two agricultural development programme zones in Oyo State of Nigeria. 150 respondents were eventually used for the study. Structured questionnaires and interview schedules were used to collect data. Data were analyzed using descriptive statistics and Shapley decomposition approach. Age and household size of women were 35.5±1.06 years and 8.0±0.59 persons, respectively. Most respondents (88.0%) were married and 43.3% were farmers with income of N24,196.76±11,897.90 per month. Sources that largely explain inequality were primary occupation (0.5551), farm size (0.2523) and household assets (0.0766) and the relative contributions of these factors sum up to 80%. Total inequality computed by the Gini index was 0.2206 and it implies that the contribution of the predicted residual term to income inequality in this case was 22.06%.The marginal contribution of the estimated income sources of the weighted mean with no negative values from level 1 to 12 were primary occupation, educational level, workers per household, number of assets and location with weighted marginal contribution of 0.0125, 0.0120, 0.0105, 0.0071 and 0.0053 respectively. Narrowing the gap between those at the top and the bottom of income distribution will reduce inequality in the households and the nation at large and that is the more reason this study needs attention because within group components overwhelmingly accounted for inequality compared to the between group components. The role of spatial inequality and policies that encourage entrepreneurship training and non-formal education for women in rural areas would be inequality reducing, and would tend to be more effective if additional policy instruments are used to target other sources of measured income inequality.
ScienceOpen
Authors: deLaubell Lauren
Published: April 2024

ScienceOpen is a free aggregator database for scientific literature, including open access and preprint publications. The inclusion of altmetrics and linked citations will be of interest to scholars tracking their own author impact and/or conducting systematic reviews. Additional levels
of access, including peer reviews and commenting, are available to users who provide their ORCID ID and link publications.
Simultaneous UV-Spectrophotometric Estimation of Aceclofenac and Cyclobenzaprine HCl by three different methods
Authors: Deshpande Mahesh Mukund , Chavan Machindra Jayram
Published: April 2024

Background: An Aceclofenac and Cyclobenzaprine HCl is NSAID and antidipressive agent, a rare combination. Objective: The aim was to develop and validate new UV-spectrophotometric methods. Method: In first method (M-I) simultaneous estimation with forced degradation was performed. In second method (M-II) simultaneous estimation by derivatization technique was performed. In third method (M III) the simple simultaneous estimation was performed. The abbreviations ACE and CYC are used for Aceclofenac and Cyclobenzaprine HCl in the current manuscript. Results: In method I, the linearity range was obtained as 5 - 25µg/ml with correlation coefficient 1 respectively. The results were 99.45% 99.50% 99.97% 99.78% 99.36% 99.67% respectively for both the drugs. The forced degradation for both drugs by applying the method was carried out. In method II, the derivatization with 2% ninhydrin was carried out. The correlation coefficient was obtained as 1 for both the drugs respectively. The recovery study values were obtained as 95.60%, 95.95%, and 95.53% 95.85% 98.82% 98.33% respectively for both the drugs. In the method III, the correlation coefficient of both the drugs was 1. The recovery study values were obtained as 99.56-100.32% 99.05 – 100.2% for both drugs respectively. Conclusion: All values were obtained in the acceptable range of as per ICH guidelines. The forced degradation was useful to obtain stability indicating data for this combination. The derivatization technique is first time used for the analysis of this combination. The one-way analysis of variance (ANNOVA) shows no significant difference among these three methods. As no methods are available for this new and recent combination, these methods can be routinely used for the analysis of these drugs.
Association between number of Barrett’s esophagus risk factors and disease prevalence
Authors:   Division of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, Florida, USA., Qumseya Bashar
Published: April 2024

Introduction: Current clinical practice guidelines recommend screening endoscopy for patients with multiple Barrett’s Esophagus (BE) risk factors. However, there is only indirect evidence that BE risk factors are additive.
Influence of protein intake on clinical and nutritional parameters in pancreatic cancer
Authors:   Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, 69120 Heidelberg, Germany., Rotzer Ingeborg
Published: April 2024

Introduction Nutritional status is an important determinant of survival in patients with advanced pancreatic cancer. However, the effects of protein intake on nutritional status are largely unknown.
Talent Development in Software Technology Specialisation at Vocational Colleges: A Case Study of Luoyang Vocational College of Science and Technology
Authors: Siti Norbaya Niu Feifei,
Published: April 2024

The software industry's competitiveness hinges on the prowess of its software technology talents. In light of this, the present study focuses on Luoyang Science and Technology Vocational College, renowned for its provincial brand specialty in software technology. Employing a qualitative research approach, particularly through semi-structured interviews, this research involves graduates from the software technology program and professionals from information technology enterprises both within and outside the province. The findings reveal several critical issues, including a disconnect between the curriculum and industry demands, insufficient emphasis on skill development and practical expertise, and a lack of vocational literacy in software technology education. To address these challenges, the study proposes tailored strategies. These strategies aim to bridge the gap between academic content and industry requirements, enhance the cultivation of practical skills and hands-on experience, and bolster vocational literacy in software technology programs. By implementing these recommendations, Luoyang Science and Technology Vocational College, as well as other institutions, can effectively nurture competent software technology talents, equipping them to thrive in the fiercely competitive software industry.
The Digital Revolution and Satisfaction Knowledge of Fintech-Based Advancements in Machine Learning
Authors: Vedala Naga Sailaja
Published: April 2024

On the international stage, financial technology, or Fintech, is a relatively new industry. The automation of financial services using IT is becoming more and more important as the several transactions occurring online rise. Fintech helps businesses to provide round-the-clock services to clients wherever in the world. However, since Fintech transactions involve information, maintaining security becomes crucial. Such systems' weaknesses expose them to fraudulent activities, which seriously harm both clients and suppliers. For this reason, anomalies in Fintech applications are detected using methods from the field of machine learning (ML). The results showed that customer satisfaction is highly influenced by the tangibles, fintech services' dependability, and empathy. However, there was no discernible difference in consumer satisfaction between assurance and responsiveness. Additionally, the results demonstrated that customer satisfaction with fintech services has a significant and positive impact on the intention to re-use fintech services, underscoring the importance of sustaining high customer satisfaction for retention. Interestingly, there was little direct effect of digital transformation on the sustainable performance of banks, even though the intention to reuse fintech was positively correlated with that performance. Furthermore, adopting fintech services with digital awareness has a major and favorable impact on the long-term success of banks. Furthermore, we offer an examination of the impact of particular characteristics on their functionality. We conclude by talking about how the results will affect future Fintech application security.
Decision Support System for Optimizing Tactics and Strategies of Sports Competition Using Reinforcement Learning Algorithm
Authors: Xu Zhiyong
Published: April 2024

This paper presented an augmented reality secure edge machine learning dynamic optimization model for detecting volleyball movement recognition using a decision support system. The proposed Edge Augmented Decision Support System Blockchain (EdgeAu-DSSBC) model aims to address the limitations of centralized machine learning models, such as high latency, network congestion, and data privacy concerns, by utilizing edge computing and dynamic optimization techniques. The proposed EdgeAu-DSSBC system consists of two main components: an augmented reality interface that allows users to interact with the system and an edge machine learning algorithm that performs the recognition task. The system uses dynamic optimization techniques to optimize the parameters of the machine learning algorithm in real time based on the feedback received from the users. The decision support system provides additional guidance to the users and helps them to make informed decisions based on the recognition results. The EdgeAu-DSSBC model was evaluated using a dataset of volleyball movement videos and compared to existing centralized and edge-based machine-learning models. The experimental results demonstrate that the EdgeAu-DSSBC exhibits improved performance with an accuracy of 99%.
Exploration of Emotion Analysis and Personalized Emotion Adjustment Algorithm in English Learning Process
Authors: Liang Kaikai
Published: April 2024

An important component of human existence and behaviour is emotion. The automatic identification of emotions has gained significant importance in the domains of affective computing and human-machine interaction in recent times. Getting pictures of people's facial expressions is one of the simplest and least expensive methods for identifying emotions among the several physiological and kinematic data that may be utilised. Because of variations in anatomy, culture, and environment, developing a generalised, cross-subject model for emotion identification from facial expression remains difficult.This study presents an intelligent classroom that uses e-learning to help persons with autism spectrum condition strengthen their emotional abilities. Monitoring autism child engagement and participation in class while looking for signs of attention is necessary for effective classroom education. Teaching quality is increasingly being determined by the teachers' capacity to assess and evaluate the behaviour of their pupils in the classroom. Modern classroom systems are enhanced with the most recent technologies in educational institutions to make them more interactive, student-centered, and personalised. Even with these tools, it can be challenging for teachers to gauge their pupils' levels of interest and focus. This study makes use of contemporary technology to establish a real-time, intelligent vision-based classroom that can track students' moods, attendance, and levels of focus even when they are wearing face masks. In order to evaluate students' attention or lack thereof in a classroom, we trained AlexNet models that recognise student behaviour, including recognising facial expressions. Another designed to manage the irregular relationship between pupils and activities is the Interaction Students Education Neural Network ( Int_Edu_NN). The Q-matrix is used to firstly determine the pupil variable and the assignment indicator. This network is analyzed using two datasets in terms of various parameters and hence the proposed Int_Edu_NN achieves 99.% and 98% of accuracy.
An English Translation Error Correction System Based on Multi-Feature Fusion
Authors: Liu Xi
Published: April 2024

Multi-feature fusion is an advanced computational technique that combines multiple sources of information or characteristics to improve the performance of a system or model. In various fields such as computer vision, natural language processing, and machine learning, this approach integrates diverse features like visual cues, textual data, and contextual information to enhance accuracy, robustness, and efficiency. Multi-feature fusion-based English translation represents a cutting-edge approach in natural language processing, where diverse linguistic features are amalgamated to refine the accuracy and quality of translation outcomes. By integrating various elements such as syntax, semantics, context, and linguistic patterns, this method significantly enhances the translation process, ensuring more nuanced and contextually precise results. This paper introduces an innovative English Translation Error Correction System (ETECS) founded on Multi-Feature Fusion, supplemented by a bi-gram Statistical Classification Neural Network (n-gramCNN). In response to the growing demand for accurate and efficient translation error detection and correction, this research endeavors to enhance the quality of English translations through a comprehensive and advanced methodology. Using a dataset of 1,000 English text samples with corresponding translations, ETECS demonstrates an impressive error detection accuracy of 95%. Upon identifying errors, the system achieves a correction accuracy of 92%, effectively rectifying grammatical, syntactical, and semantic inconsistencies. Moreover, ETECS outperforms baseline error correction systems by a significant margin, showcasing its superiority in handling complex translation errors. The bi-gram Statistical Classification Neural Network (n-gramCNN) contributes to this success by achieving an F1 score of 0.94, highlighting its effectiveness in classifying and correcting translation errors.
Multi-Objective Construction of English Web-Based Independent Learning Based on Mobile Intelligent Information System
Authors: Jue Fu Ying Zhang,
Published: April 2024

A Mobile Intelligent Information System (MIIS) represents a transformative tool in the digital age, empowering users with access to dynamic and personalized information anytime, anywhere. Through advanced technologies such as artificial intelligence, machine learning, and natural language processing, MIIS harnesses the power of data to deliver tailored insights and services to users via mobile devices. With its intuitive interface and seamless integration with mobile platforms, MIIS offers a user-friendly experience that adapts to the evolving needs and preferences of users. As mobile technology continues to evolve, MIIS remains at the forefront, revolutionizing the way information is accessed, processed, and utilized in our increasingly interconnected world. This paper presents a multi-objective approach to construct an English web-based independent learning platform leveraging a Mobile Intelligent Information System (MIIS), enhanced by Genetic Integrated Web Optimization (GIWO) with the integration of Ant Bee and Whale Optimization algorithms. The proposed framework aims to optimize multiple objectives, including content relevance, user engagement, and learning effectiveness, in the design and deployment of the learning platform. Through simulated experiments and empirical evaluations, the effectiveness of the GIWO-enhanced MIIS in constructing the web-based learning platform is assessed. Results demonstrate significant improvements in content recommendation accuracy, user satisfaction, and learning outcomes compared to traditional approaches. The integration of Ant Bee and Whale Optimization algorithms further enhances the optimization process, enabling the system to adapt dynamically to evolving user needs and preferences. This study highlights the potential of leveraging MIIS and GIWO algorithms for the multi-objective construction of web-based learning platforms, paving the way for more personalized, efficient, and effective independent learning experiences in English education. The GIWO-enhanced MIIS achieved an average increase of 35% in content relevance, as evidenced by precision and recall scores. Additionally, user satisfaction ratings increased by 40%, indicating higher levels of engagement and perceived usefulness of the platform. Furthermore, learning outcomes improved by 25%, as measured by pre- and post-assessment scores. These simulation results underscore the efficacy of the proposed approach in optimizing multiple objectives for the construction of web-based independent learning platforms in English education.
Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images
Authors: Suman Kumar Swarnkar
Published: April 2024

This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN-based approaches in leveraging raw MRI images for accurate and early detection of AD.
Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors
Authors: Suman Kumar Swarnkar
Published: April 2024

This research investigates the adequacy of Irregular Forest-based expectation models in evaluating cardiovascular disease (CVD) hazards by joining clinical and hereditary variables. Leveraging a dataset comprising comprehensive clinical profiles and hereditary data, we prepared and assessed Arbitrary Woodland models near other machine learning calculations. Ours comes about illustrates that the Random Forest show outperformed Logistic Relapse, Support Vector Machine, and Slope Boosting in precision (85%), affectability (82%), specificity (88%), and zone beneath the recipient working characteristic bend (AUC-ROC) (0.92). Furthermore, the consideration of hereditary highlights altogether progressed the prescient execution, with the show accomplishing a precision of 88%, affectability of 86%, specificity of 90%, and AUC-ROC of 0.94. This study highlights the significance of coordination hereditary data for personalized hazard evaluation in CVD. The comparison with related works underscores the progressions made in leveraging machine learning for cardiovascular hazard forecast and conclusion. Our discoveries recommend that Irregular Forest-based models offer a promising approach for upgrading quiet results through precise chance evaluation, early location, and focused on preventive intercessions. Moving forward, encourage inquiry about is justified to illustrate fundamental pathophysiological components and direct accuracy medication approaches in cardiovascular wellbeing.
Support Vector Machine Approaches for Tumor Classification and Survival Prediction in Cancer Patients: A Multi-Omics Data Analysis
Authors: V.Saravana Kumar
Published: April 2024

This study examines the viability of Support Vector Machine (SVM) calculations in tumour classification and survival forecast utilizing multi-omics information investigation in cancer patients. Leveraging a comprehensive dataset comprising genomic, transcriptomic, proteomic, and metabolomics profiles from assorted cancer sorts, we compared four SVM variations: Direct SVM, Polynomial SVM, Radial Basis Function (RBF) SVM, and Sigmoid SVM. Results illustrated that the RBF SVM calculation displayed predominant execution in tumour classification, accomplishing an exactness of 92%, with accuracy, review, and F1 score values of 91%, 94%, and 92% respectively. For survival forecast, the RBF SVM too beat other variations with a concordance file (C-Index) of 78%. These discoveries highlight the potential of SVM approaches in leveraging multi-omics information to move forward with cancer determination and forecast. Our consideration contributes to the developing body of research in machine learning-based cancer investigation and underscores the significance of coordination of different atomic datasets for personalized oncology.
Integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry
Authors: Suman Kumar Swarnkar
Published: April 2024

This inquire about examines the integration of Artificial Intelligence (AI) and information analytics to optimize supply chain forms within the pharmaceutical industry. Through tests and writing audits, the ponder investigates the adequacy of AI calculations counting Linear Regression, Random Forest Regression, K-Means Clustering, and Deep Learning Neural Systems over request estimating, stock optimization, generation planning, and coordination optimization. Results appear that Random Forest Relapse beats Direct Relapse in request determining with RMSE of 80.20, MAE of 60.75, R² of 0.90, and MAPE of 6.50%. K-Means Clustering recognizes five clusters for stock optimization. Profound Learning Neural Systems accomplish RMSE of 75.10, MAE of 55.30, R² of 0.92, and MAPE of 5.80% for generation planning. In coordination’s optimization, Genetic Algorithm accomplishes a add up to fetched of $150,000 and conveyance time of 5 days compared to Mimicked Strengthening with $160,000 and 6 days. The research contributes to understanding the part of AI and information analytics in improving supply chain effectiveness, decreasing costs, and guaranteeing maintainability within the pharmaceutical segment.
Real-time Hazard Detection in Autonomous Vehicles: Deep Learning Approaches for Enhancing Road Safety
Authors: T.K.Shaik Shavali
Published: April 2024

This examination explores ongoing risk location in independent vehicles through the use of profound learning philosophies, expecting to upgrade street security. Utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the review centers around distinguishing and grouping dangers like people on foot, vehicles, obstructions, and street abnormalities in assorted driving situations. By examining information from locally available sensors including cameras, LiDAR, and radar, the profound learning models learn perplexing examples and portrayals, empowering them to settle on informed choices in powerful conditions. The trial results exhibit the viability of the proposed approaches, with CNN-based and RNN-based models accomplishing high exactness, accuracy, review, and F1-score upsides of 0.95, 0.93, 0.96, and 0.94 individually. These outcomes outperform those of conventional AI calculations and pattern techniques, displaying the predominance of profound learning in danger identification undertakings. Also, examinations with related work highlight the headways accomplished, featuring the capability of profoundly figuring out how to upgrade the well-being abilities of independent vehicles. In general, this examination adds to the continuous endeavors in creating solid and effective danger recognition frameworks, making ready for more secure and more dependable independent driving innovation.
Design of Fingerprint-Based Gender Clustering Using Fuzzy C-Means Algorithm
Authors: Chandrakant P. Divate
Published: April 2024

Biometric systems, with fingerprint recognition as a cornerstone, play a crucial role in identity verification and access control. This paper introduces an innovative approach to gender clustering based on fingerprint data, leveraging the Fuzzy C-Means (FCM) algorithm. The proposed design aims to enhance the precision of gender classification by incorporating the inherent uncertainty in fingerprint features. Through the integration of FCM, which allows soft assignment of fingerprints to gender clusters, the system achieves a nuanced and adaptable classification. The study meticulously explores the design considerations, including the selection of features, data preprocessing, and the configuration of FCM parameters. Additionally, the paper discusses the experimental results, demonstrating the effectiveness of the proposed method in accurately clustering fingerprint data according to gender. The research contributes to the evolving field of biometrics, offering a novel perspective on gender classification that embraces the complexity of fingerprint patterns. The outcomes provide valuable insights for the development of reliable and versatile biometric systems, with potential applications in security, forensics, and personalized identification
Dynamic Spectrum Sensing For 5G Cognitive Radio Networks Using Optimization Technique
Authors: Sadhana D. Poshattiwar Sandip B. Shrote ,
Published: April 2024

With increasing development of 5G technology, the rapid growth of various technologies and the growth of various wireless devices, demand for wireless spectrum becomes more urgent. Wireless communication technologies have been advancing rapidly, leading to the emergence of 5G communication systems .Spectrum sensing is the key model utilized to access the spectrum dynamically in CRN. Various researchers are done in spectrum sensing scenario and different methods are designed to perform the task of spectrum resource sharing. Most of the methods design a decision statistics for identifying the signal by analyzing the features of noise and signals. One promising approach to address the challenges of spectrum sensing in cognitive radio networks for 5G communication is the integration of deep learning with hybrid optimization techniques.  By combining the power of deep learning algorithms with optimization methods, it becomes possible to improve the efficiency and accuracy of spectrum sensing in dynamic and diverse communication scenarios. One of the key advantages of MIMO-based spectrum sensing is its ability to exploit the spatial diversity inherent in the environment. By using multiple antennas at both the transmitter and receiver, MIMO systems can distinguish between signals arriving from different directions, thereby improving the accuracy of spectrum sensing. Moreover, MIMO technology also enables the cognitive radio network to utilize the available spectrum more efficiently. With the ability to establish multiple parallel communication links, MIMO-based cognitive radio networks can achieve higher data rates and improved spectral efficiency, especially in dynamic and challenging radio environments. Research in this field is focused on further enhancing the performance of MIMO-based spectrum sensing in cognitive radio networks through advanced signal processing algorithms and machine learning technique  Spectrum sensing detect existence of primary users (PUs) and it becomes a main research topic of CRN in industry and academic domain. This research developed a new framework based on algorithm to progress the mechanism of spectrum sensing in the CRN by detecting the availability of free channel. The signal components are extracted from the received signal and thereby spectrum availability of detected through fusion center using proposed Feedback Artificial Optimization Algorithm-based Deep Q network. However, Simulation results show that the proposed  multiple-input-multiple-output(MIMO) spectrum sensing method achieves good performance Cognitive Radion Network attains maximum probability of detection and minimum probability of false alarm as 70%, and 38% for Rayleigh channel.
Robustness and Security in Deep Learning: Adversarial Attacks and Countermeasures
Authors: Navjot Kaur
Published: April 2024

Deep learning models have demonstrated remarkable performance across various domains, yet their susceptibility to adversarial attacks remains a significant concern. In this study, we investigate the effectiveness of three defense mechanisms—Baseline (No Defense), Adversarial Training, and Input Preprocessing—in enhancing the robustness of deep learning models against adversarial attacks. The baseline model serves as a reference point, highlighting the vulnerability of deep learning systems to adversarial perturbations. Adversarial Training, involving the augmentation of training data with adversarial examples, significantly improves model resilience, demonstrating higher accuracy under both Fast Gradient Sign Method (FGSM) and Iterative Gradient Sign Method (IGSM) attacks. Similarly, Input Preprocessing techniques mitigate the impact of adversarial perturbations on model predictions by modifying input data before inference. However, each defense mechanism presents trade-offs in terms of computational complexity and performance. Adversarial Training requires additional computational resources and longer training times, while Input Preprocessing techniques may introduce distortions affecting model generalization. Future research directions may focus on developing more sophisticated defense mechanisms, including ensemble methods, gradient masking, and certified defense strategies, to provide robust and reliable deep learning systems in real-world scenarios. This study contributes to a deeper understanding of defense mechanisms against adversarial attacks in deep learning, highlighting the importance of implementing robust strategies to enhance model resilience.
A Comprehensive Analysis on the Efficacy of Machine Learning-Based Algorithms for Breast Cancer Classification
Authors: K.P.Senthilkumar
Published: April 2024

This research focuses on using machine learning to make breast cancer classification better. In this research various machine learning algorithm such as Decision Tree, Linear Discriminant, Support Vector Machine, K-Nearest Neighbors, Probabilistic Neural Network, Logistic Regression, Recurrent Neural Network, and Ensemble Method are used. We tested them using two different ways of splitting the data—90/10 and 70/30—and we also picked important features to consider. The Ensemble Method came in second place with accuracies of 98.2% and 97.6%. The Deep Neural Network performed really well too, with accuracies of 96.2% in the 90/10 split and 89.1% in the 70/30 split. We also found that selecting the right features improved accuracy a lot. This shows how important it is to choose the best features to make the models better. These results show that machine learning can be used to classify breast cancer effectively. The numbers prove that the Deep Neural Network and Ensemble methods have high accuracy, and selecting the right features makes them even better. The research outcomes introduces machine learning techniques that can improve breast cancer diagnosis, potentially changing the way doctors make decisions and improving patient outcomes.
IOT- Based Smart Plant Protection and Pest Control by Using Raspberry Pi
Authors: Bhuvaneshwar D. Patil
Published: April 2024

In economies heavily dependent on agriculture, such as India, the farming sector plays a crucial role, yet it faces various challenges that hinder its profitability and, unfortunately, contribute to farmer suicides. Pest attacks stand out as a significant factor contributing to the agricultural woes, causing substantial harm to crops. This research proposes a solution leveraging Raspberry Pi technology, incorporating a mathematical model known as Beta regression analysis. The model utilizes farm humidity and temperature as inputs to predict environmental conditions conducive to pest formation and attacks. The resultant Beta regression factor serves as a risk indicator for environmental health. Based on this factor, the system forecasts the likelihood of pest occurrences. By offering advance predictions of pest activity, farmers can strategically apply the right amount of pesticides, effectively mitigating the impact of pests on their crops. This proactive approach allows farmers to manage potential damage before it occurs, fostering a more sustainable and profitable farming environment. The innovative system outlined in this paper aims to empower farmers with accurate pest control predictions, thus enhancing their ability to navigate and overcome challenges in the agricultural landscape.
ZSGEEHCP: Zonal Stable Gateway Based Energy Efficient Heterogeneous Clustering Protocol For WSN- Based IoT.
Authors: Ranjith Kumar, Vijayakumar Varadarajan Bishwajeet Kumar,
Published: April 2024

In future most of the devices on the earth connected to the internet for making world of intelligent networks. The internet of things (IoT) and Industrial IoT play a very significant role for implementing such kind of smart networks. So, in that case, the transmission of packets of data sent to the base station from sensor nodes with minimum energy consumption in a homogeneous or heterogeneous environment to hike the stability of the network appears as a big challenge for IoT-based networks. But, such kind of problem can be overcome by using a proper clustering algorithm. In this work, different kinds of classical clustering algorithms are studied precisely. Most of the traditional clustering algorithm protocols presume that all the nodes have the same amount of energy, they are unable to fully exploit the presence of node heterogeneity and the same problem will be faced with IoT-based networks. In this specific piece of work, we recommend a gateway based heterogeneous energy-based clustering algorithm which is crucial for many wireless sensor networks along with IoT-based applications. In this concept geographical area is divided into three zone, Advanced, Intermediate and normal zone. Here, advance nodes send data through gateway to the base station while intermediate node use clustering algorithm for transferring data and normal nodes communicate data to the base station directly. ZSGHCP is driven by the mounded election protocol probabilities of each node elected as a cluster head depending on the rest of the energy. By simulation, we show that ZSGHCP invariably extends the stability time as compared to current clustering procedures. Finally, we investigate the sensitivity of our ZSGHCP protocol to network heterogeneity characteristics that capture energy imbalance, throughput, and no of alive nodes. We construct that ZSGHCP payout extends the durability region for excessive values of additional energy brought by extra higher-powered nodes.
Rapid Strawberry Ripeness Detection And 3D Localization of Picking Point Based on Improved YOLO V8-Pose with RGB-Camera
Authors: Xia Chen
Published: April 2024

Accurate identification of strawberries at different growth stages as well as determination of optimal picking points by strawberry picking robots is a key issue in the field of agricultural automation. In this paper, a fast detection method of strawberry ripeness and picking point based on improved YOLO V8-Pose (You Only Look Once) and RGB-D depth camera is proposed to address this problem. By comparing the YOLO v5-Pose, YOLO v7-Pose, and YOLO v8-Pose models, it is determined to use the YOLO v8-Pose model as the fundamental model for strawberry ripeness and picking point detection. For the sake of further improving the accuracy of the model detection, this paper makes targeted improvements: all the Concat modules at the Neck part are replaced with BiFPN richer feature fusion, which enhances the global feature extraction capability of the model; the MobileViTv3 framework is employed to restructure the backbone network, thereby augmenting the model's capacity for contextual feature extraction. Subsequently, the output-side CIoU loss function is supplanted with the SIoU loss function, leading to an acceleration in the model's convergence. The enhanced YOLO v8-Pose demonstrates a 97.85% mAP-kp value, reflecting a 5.49% improvement over the initial model configuration.. For the sake of accurately localizing the three-dimensional information of strawberry picking points, the strawberry picking points are further projected into the corresponding depth information to obtain their three-dimensional information. The experimental results show that the mean absolute error and the mean absolute percentage error of strawberry picking point localization in this paper are 0.63 cm and 1.16%, respectively. In this study, we introduce a method capable of concurrently detecting strawberry maturity and identifying the precise harvesting location while accurately localizing the picking point. This investigation holds considerable theoretical and pragmatic relevance in augmenting the intelligence of strawberry harvesting robots and actualizing automation and smart capabilities in agricultural production.
Dual-Band Millimeter-Wave MIMO Antennas For 5G Wireless Implementation
Authors: Nandita Pradhan Tushar,
Published: April 2024

For 5G wireless applications, this exploration proposes a dual-band 28/38 GHz 4-component MIMO array depending on duplex-mode planar monopole antennas. Four planar monopole antennas are part of the layout framework, situated at the districts of a Rogers RO4003 substrate measuring 20 by 20 mm2 and with a dielectric constant of 3.55. The form of the suggested placoid monopole antenna is crescent-shaped. We created a notch at the bottom and inscribed two rectangular holes on each side to get the required behaviour and execution. For enhancement of isolation, we also employed a limited ground plane. Antenna elements can achieve significant isolation (> − 23 dB) using spatial and scattering behaviour approaches. A illustration of the four-element MIMO array is created, manufactured, and measured to multifariousness the outline idea. The investigational outcomes demonstrate that the suggested antenna has strong isolation and high efficiency while covering the 27.25–29 GHz and 34.5–41 GHz bands. Additionally, studies are done on the channel capacity, realized gain, and radiation pattern. The obtained findings suggest that, for millimeter-wave 5G MIMO implementation, the proposed MIMO antenna might be an excellent application-oriented design.
Energy-Saving Data Acquisition Model of Wireless Sensor Network Based on Nonlinear Algorithm
Authors: Huang Ping
Published: April 2024

Wireless sensor network significant data acquisition has a high cost, long completion time, and low accuracy. This paper adopts a sensor network data acquisition method based on a nonlinear algorithm to solve the above problems. In this paper, a distributed data acquisition method based on nonlinear regression is established by combining the time series relationship of data. First, this paper uses nonlinear regression analysis technology to establish the sensing data model and retain the characteristics of the sensing data. This makes the node pass only the parametric data of the regression model. This paper uses this method to replace the transmission of actual monitoring sensory data information. Experiments show that this method can effectively reduce data redundancy and network traffic under any conditions. The method has been verified in practical WSN applications.
Deep Learning PCA Correlation Analysis of Teacher’s Educational Technology Quality and Teacher’s Professional Competence
Authors: Meilin Jin Xiaojuan Tang,
Published: April 2024

In contemporary education, the integration of educational technology has become crucial for enhancing teaching quality and fostering student learning outcomes. This study employs a novel approach by combining deep learning techniques with Principal Component Analysis (PCA) to explore the relationship between teachers' educational technology proficiency and their professional competence. The research leverages a dataset comprising various metrics related to teachers' usage of educational technology tools and their performance in professional development programs. Initially, the study preprocesses the data and applies deep learning models to extract high-level features from the complex and heterogeneous dataset. Subsequently, PCA is employed to reduce the dimensionality of the feature space while preserving the underlying structure and variability of the data. Through this process, latent factors representing different aspects of educational technology proficiency and professional competence are identified. The correlation analysis conducted on the reduced feature space reveals intricate relationships between teachers' competency in educational technology and their overall professional capabilities. The findings indicate that certain dimensions of educational technology quality, such as adaptability to new tools and innovative pedagogical approaches, positively correlate with measures of professional competence, including classroom management skills, student engagement, and instructional effectiveness.