bluuebunny/crossref_metadata_2025 huggingface dataset:
Rough Set Techniques in Wireless Sensor Networks using Membership Function and Rough Labelling Graphs for Energy Aware Routing
Authors: R. Nithya Et al.
Published: January 2024

Rough set theory and rough graphs are employed for data analysis. Rough graphs utilize approximations, and this paper presents a method for implementing rough graphs using rough membership functions and graph labeling for data structures and reduction. This paper proposes a rough graph labeling method, termed rough -labeling similarity graph, that utilizes a similarity measure for vertex and edge labeling. This method aims to minimize boundary regions in rough graphs and is applicable to wireless sensor networks (WSNs). In WSN, the proposed algorithms such as PSO-LSTM, COA-LSTM, LOA-LSTM integrates with rough set theory based rough -labeling for boundary region identification. The proposed method incorporates the membership functions encapsulates the rough labeling graphs for cluster boundary region identification for WSN. The cluster boundary for WSN is based on rough set membership and rough labelling is termed as rough set membership boundary region (RMB). RMB in WSN and implementation of proposed routing algorithm such as LOA-LSTM provides high throughput and energy saving when compared to existing cluster boundary structure methods such as Voronoi, Spectrum and Chain.
Precision Mining of Gene-Disease Associations via Frequent Itemset Analysis and Bioinformatics Integration
Authors: K.Mary Sudha Rani Et al.
Published: January 2024

Biomedical text mining involves the extraction of relevant information from biomedical datasets. It plays a crucial role in genetic research, especially in the development of new drugs where understanding the relationships between genes and diseases is vital. This study introduces a method for generating sets of candidate genes associated with diseases, employing frequent itemset mining for analysis. Genes are ranked based on parameters such as maximum frequent itemset size and gene symbol frequency. This approach aims for precision and efficiency compared to traditional laboratory-based methods, providing highly accurate associations and uncovering novel relationships. Unlike time-consuming laboratory methods, our proposed approach leverages data from the NCBI (National Centre for Biotechnology Information database )via Entrez and utilizes bioinformatics tools like blast for indirect gene associations. Genes exhibiting single nucleotide polymorphisms are identified as indirect genes. The outcomes of this research are anticipated to contribute significantly to biomedical research by offering precise and valuable associations, thereby advancing our understanding of gene-disease relationships..
Optimization of Human, Technological, Administrative Resources for Decision-Making in Information Security Governance
Authors: Moisés Toapanta Et al.
Published: January 2024

Problems for decision-making in information security governance are persistent because there are no adequate models. The objective is to generate a model that optimizes human, technological and administrative resources for decision-making in the governance of Information Security. The deductive method and exploratory research were used. A resource optimization model with 95% reliability generated by Cronbach's Alpha resulted; the mathematical justification of the business, information and application architecture with 96.13%, 95.89% and 95.20% confidence respectively generated by the Pearson correlation; an enterprise architecture context diagram; and an algorithm expressed with flowchart techniques with 98.21% degree of association in its elements generated by the multiple correlation coefficient. It was concluded that with the proposed model, a company's resources can be optimized for information security governance; so that managers have adequate support for making decisions with a mathematical basis with simulations in quantities of components within the architectures; so that the function in business management becomes simpler by applying a general model that can be adapted to various types of companies to achieve success for decision making in security governance of information security.
A Review on Prediction of Heart Disease based on Machine Learning and Datamining Techniques
Authors: Durga Bhavani Adla Et al.
Published: January 2024

Heart is the important organ in human body which supplies blood to all organs of the body. The abnormal situation of heart is considered as heart disease. According to WHO data cardiovascular, respiratory and neonatal conditions are the top three causes of Deaths in the World. In the year 2019 Heart diseases occupies 16%(9 million) of overall deaths happened in World. From two decades there is 4 times increase in the deaths with heart diseases this is because of change in life style, lack of physical activity, food habits, obesity ,stress, cholesterol ,high blood pressure and diabetes.so there is a need  to work on prediction of heart diseases to save many lives because prediction is the only way to prevent the disease. In this paper we will discuss about existing algorithms and existing work done in different machine learning and datamining techniques, which are concentrated more on the classification and prediction. main objective is to evaluate the performance of these algorithms and identify the most accurate and efficient approach for diagnosing heart diseases.

Some of the machine learning and data mining techniques are Artificial Neural Network(ANN),Decision Tree, Naive Bayes, SVM(Support Vector Machine),k-Nearest Neighbours (KNN),J48,SMO,Random forest and classification Tree.
Lung Cancer Classification Using Modified Squeezenet
Authors: Nishiya Vijayan Et al.
Published: January 2024

Lung cancer is the primary cause of mortality in individuals diagnosed with cancer. Detecting and diagnosing lung cancer early significantly reduces the mortality rate. The early diagnosis of lung cancer is greatly facilitated by medical imaging. The recommendation is to undergo a CT scan, as it has a higher probability of detecting lung cancer during its initial phases. The detection of lung cancer greatly depends on the utilization of advanced deep learning technology, specifically convolutional neural networks, which assist in accurately classifying the CT image. This paper proposed a Modified light weight SqueezeNet architecture that mixes bottleneck residual network and fully connected layer along with global average pooling in the original network. This modification enhances the classification performance with a slight rise in computational complexity.  CT images of 330 patients are used as a data set for testing the proposed technique, which is executed in MATLAB 2022a platform. The proposed method can identify lung cancer and categorize it as either malignant or normal with test Accuracy of 95.76%, Recall-92.94%, Precision of 98.75%, Specificity-98.75%, and AUC-0.9977. The Modified SqueezeNet gives better classification performance against the base SqueezeNet model. The proposed method outperforms traditional deep learning networks like AlexNet, ShuffleNet, ResNet-50, and GoogleNet.
Agricultural Crop Recommendation, Crop Disease Detection and Price Prediction Using Machine Learning
Authors: Tumma Susmitha Et al.
Published: January 2024

India's foundation is its agriculture. With over 60% of the workforce employed and producing over 18% of the nation's GDP, it is a vital sector of the Indian economy. Although there are many ways in which we can use technology to increase product production, a farmer can only profit if he is able to sell his crops. Three laws have been passed by the Indian government to encourage the export of agricultural products across the nation. But today, we witness farmers all over the nation fighting against these regulations to protect their rights. Farmers worry that big merchants will exploit them as puppets and undercut the price at which they sell their goods. After doing a thorough analysis of the situation, we developed the concept of creating an agricultural produce application that facilitates direct communication between farmers and retailers, allows for product reviews and crop yielding rate prediction, and predicts the price of agricultural produce based on quantity produced and previous years' sales rates. Unpredictable rains, unexpected temperature decreases, and heat waves have all been brought on by the shifting climate, and the ecosystem has suffered significant harm. Thankfully, machine learning has produced useful methods for tackling international problems, such as agriculture. These climate change-related agricultural issues can be resolved by using various machine learning methods. The purpose of this piece is to Create a method to identify crop diseases and suggest crops. For both objectives, publicly accessible datasets were utilized. Regarding the crop recommendation system, feature extraction was done, and a variety of machine learning methods were used to train the dataset, including Support Vector Machine (SVM), Random Forest, Decision Tree, Logistic Regression, and Multilayer Perceptron. 99.30% accuracy was attained via the random forest algorithm.CNN architectures such as ResNet50, and EfficientNetV2 were trained and compared for the plant disease identification system. EfficientNetV2 outperformed the rest, with a high accuracy of 96.08%.
Advanced Dielectric Anomaly Detection for Non-Destructive Fruit Inspection Using Electromagnetic Microwave Technique
Authors: Md Najumunnisa Et al.
Published: January 2024

This paper presents a comprehensive investigation into the application of an electromagnetic microwave technique combined with the dielectric anomaly approach algorithm for the non-destructive inspection of fruits. The proposed antenna configuration comprises eight elements arranged in a circular array, enabling the collection of signals scattered by objects, specifically fruits, placed in their path. The collected data undergoes a series of processing steps, including Fast Fourier Transform, covariance matrix estimation, eigenvalue and eigenvector computation, and spatial spectrum construction. The dielectric anomaly algorithm is then applied to detect defects in the fruits. The study covers five different types of fruits, both healthy and defective, and gathers essential dielectric properties. Furthermore, a novel hybrid IQR method is introduced for outlier detection in the dielectric data. The results demonstrate the effectiveness of the proposed methodology in providing valuable insights into the internal structures of fruits and detecting anomalies, contributing to the enhancement of quality assessment in fruit inspection processes.
A Learning based Secure Routing Approach using Deep Reinforcement Learning in IoT Integrated Wireless Sensor Network
Authors: Battina Srinuvasu Kumar Et al.
Published: January 2024

The usage of Wireless Sensor Network (WSN) is ubiquitous in nature. With the emergence of Internet of Things (IoT) technology and its unprecedented use cases, the role of sensor networks as part of IoT application became crucial. WSN became backbone of IoT to realize integration between physical and digital worlds and connectivity to Internet. However, IoT devices are resource constrained with limited computational capabilities. The entire network is distributed in nature and has increased complexity. Routing in such WSN integrated IoT network plays an important role in achieving meaningful communication among objects. In this context, it is indispensable to have more energy efficient routing method. Since the IoT integrated sensor network is highly complicated, it is very dynamic in nature. Thus routing decisions are also dynamic leading to much importance to routing in such use cases. With the emergence of Artificial Intelligence (AI), it became possible to solve complex real world problems through learning based approach which acquires desired intelligence prior to making decisions. In this paper we proposed a deep reinforcement learning based routing mechanism for energy efficient routing in WSN-IoT integrated application. We proposed novel algorithms for network setup, formation of clusters and routing. Our method adapts to network changes due to energy levels, mobility and makes learning based routing decisions. We enhanced the method further with security to ensure its Qualityof Service (QoS) in presence of attacks. Our simulation study using MATLAB has revealed that the proposed secure routing approach outperforms existing protocols.
Kubernetes-based Deep Learning with Blockchain for Cancer Image Prediction
Authors: Arun Algude Et al.
Published: January 2024

The ability of modern artificial intelligence techniques to analyze complex data, including medical images, with greater precision and depth has significantly increased the amount of real-world data that can be collected for scientific research. This has led to the development of a global library and elevated computing choices to meet the demand for training model calculation. The development of medical image biomarkers involves additional processes that don't necessitate spending so much money on expensive computer resources. To collect and manage cancer data, this study presents a Blockchain-enabled deep learning framework for the Kubernetes cluster which replicates the training set of parameters of DL techniques across remote cluster nodes. Healthcare providers' data will be synchronized thanks to the distributed storage system known as the blockchain. Data are gathered for this suggested framework utilizing the Kaggle Dataset from several hospitals. Data preprocessing shows pixel correction and data augmentation. Following data preprocessing, U2-Net segmentation is used to segment up the images. This article uses blockchain to decentralize data, security, and privacy. A system called Kubernetes cluster is used to manage diverse containerized applications, Workload Distribution, Scalability, and Storage Management. The Kubernetes cluster is revealed in the study when investigating the cancer prediction capabilities of the hybrid learning algorithms VGG19 and BiLSTM. This proposed paper forecasts the article and explains the various applications for breast, kidney, and oral cancer predicated on the Blockchain-Enabled deep Learning model on Kubernetes and providing authentication for the use of the majority of the platform's features using a provided case.
Investigations of Naive Bayes, Random Forest and Decision Tree Algorithm for building aeroponics system
Authors: Arunraja A Et al.
Published: January 2024

Urban farming has becoming popular and most adopted recently as society has become more conscious about the food quality they consume and conservation of resources. The Internet of Things (IoT) and machine learning algorithms are employed to build and implement an automated aeroponics system. This system contains Arduino as a main processing unit and uses Node MCU for cloud storage.  It gets inputs from sensors like ultrasonic sensor, light sensor, humidity sensor, temperature sensor and exhibit the output in a LCD unit.  In response to the inputs, water pump, cooler fan and light acts automatically. The data are stored in cloud as a record for future use.  Using machine learning algorithms Naive Bayes, Random Forest and Decision Tree the datasets from cloud are trained and tested to know the crop suitable for the given condition. According to the results, the system has the potential to save more labour and reducing water consumption than conventional methods.
Exploring the Evolving Dynamics of Data Privacy, Ethical Considerations, and Data Protection in the Digital Era
Authors: Deepak Hajoary Et al.
Published: January 2024

This paper presents a comprehensive exploration of the evolving dynamics of data privacy, ethical considerations, and data protection in the rapidly advancing digital era. Recognizing the profound impact of technologies such as big data, IoT, and AI, this study delves into the intricate interplay between technological innovation and the pressing need for robust data-governance frameworks. It addresses the challenges and opportunities presented by the massive surge in data generation, focusing on the implications for personal privacy, the ethical use of data, and the development of legal and regulatory measures. Through a bibliometric analysis spanning from 2015 to 2023, this research systematically examines the literature in this domain, identifying key trends, influential authors, and pivotal publications. This study also investigates collaboration networks among leading data privacy scholars, revealing significant insights into the intellectual landscape of this field. The findings highlight a marked increase in academic interest in data privacy and ethics, particularly post-2020, underscoring the growing societal and scholarly attention to these issues.

This study emphasizes the criticality of balancing the benefits of data-driven innovation with ethical and privacy concerns. This underscores the role of education and awareness in fostering a responsible, data-literate society capable of navigating the complexities of a digitalized economy. In conclusion, the study calls for ongoing reforms and a proactive approach to data governance, advocating a future in which technological advancements and ethical data use coexist harmoniously.
In-Vehicle Data Communication with CAN &Security Monitoring: A Review
Authors: Karuppusamy S Et al.
Published: January 2024

Automobiles are now being created with more electronic components for efficient functioning such as Anti Lock Braking system, Adaptive Cruise Control, Traction control system, Airbag, Power Steering etc. managed by networked controllers that include hundreds of ECUs (electronic control units) that can coordinate, control, and monitor loads of internal vehicle components. Each component, such as ABS, TCS (Traction control system), tire pressure monitoring system and telematics system, may communicate with nearby components over the CAN (Controller Area Network) bus, establishing an in-vehicle communication network. These modern automobile system networks intended for safety with minimal consideration for security have drawn the attention of researchers for providing security in CAN. The Paper reviews the behavior and vulnerabilities of CAN within an in-vehicle network including various attacks possible in CAN along with the proposed solutions in the literature with extensive survey on a security promising approach named as IDS (Intrusion detection system).
Enhanced Disease Detection for Potato Crop Using CNN with Transfer Learning
Authors: Harsh Mishra Et al.
Published: January 2024

As the fourth most popular basic food in the world,potatoes are widely available. In addition, the worldwidemarket is causing the demand to rise daily. Diseases likeearly and late blight have a significant impact on the quantity and quality of potatoes. Determining which potato leaves are afflicted with a certain illness becomes more challenging when interpreting these diseasesmanually. Thankfully, it is possible to identify potato leafdiseases by examining the leaf conditions. This proposedstudy presents a technique that employs deep learning toidentify the two types of diseases and generates an accurate classifier using heavy designs for convolutionalneural networks, such as GoogleNet, Resnet15, VGG16,and Xception. We achieved 97% accuracy in the first 40 CNN epochs, demonstrating the practicality of the deep neural network approach.
An Efficient Anomaly Detection Through Optimized Navigation Using Dlvq-Cdma And H-Dso In Healthcare Iot Environment
Authors: Sampath Kumar Y R Et al.
Published: January 2024

An Anomaly detection (AD) framework intends to discover irregular data and also unusable activities in a system. The abnormality in the healthcare information is picked up by the AD in the healthcare system and then, the outcome is updated for the authority to evaluate the data. Numerous researchers have developed an AD method that has the disadvantage of data loss issues and complexity in computation. An enhanced AD framework utilizing Deep Learning Vector Quantization-Correlation Distance Mayfly Algorithm (DLVQ-CDMA) and Hyper-sphere Dolphin Swarm Optimization (H-DSO) methodology is presented in this work to overcome these disadvantages. By aid of the Internet of Things (IoT)-connected systems, proffered model gathers information about the patient and as well forwards the information to patient's health care application. Information from health care application is then sent via the optimal path by utilizing the H-DSO method. The data is uploaded to the cloud server later and then, it is recovered and provided to the AD system. The data is then pre-processed in an AD system. After extricating the features, the feature reduction is performed by employing the Entropy-Generalized Discriminant Analysis(E-GDA) scheme. Subsequently, the DLVQ-CDMA algorithm is utilized with the required features. Information is formerly categorized as usual data or irregularity data. data, which is attacked is stored in the log file and the normal data will undergo further evaluation for the identification of the presence of disease or disorder. After evaluation, the outcome is communicated to the patient. The experiential analysis specifies that the proffered DLVQ-CDMA methodology executes better than the prevailing methodologies.
Performance Comparison Analysis of Classification Methodologies for Effective Detection of Intrusions
Authors: Rajesh Bingu Et al.
Published: January 2024

Intrusion detection systems (IDS) are critical in many applications, including cloud environments. The intrusion poses a security threat and extracts privacy data and information from the cloud. The user has an Internet function that allows him to store personal information in the cloud environment. The cloud can be affected by various issues such as data loss, data breaches, lower security and lack of privacy due to some intruders. A single intrusion incident can result in data within computer and network systems being quickly stolen or deleted. Additionally, intrusions can cause damage to system hardware, resulting in significant financial losses and exposing critical IT infrastructure to risk. To overcome these issues, the study employs the performance comparison analysis of Autoencoder Convolutional neural network (AE+CNN), Random K-means clustering assisted deep neural network (RF+K-means+DNN), Autoencoder K-means clustering assisted long short term memory (AE+K-means+LSTM), Alexnet+Bi-GRU, AE+Alexnet+Bi-GRU and Wild horse AlexNet assisted Bi-directional Gated Recurrent Unit (WABi-GRU) models to choose the best methodology for effective detection of intrusions. The data needed for the analysis is collected from CICIDS2018, UNSW-NB15 and NSL-KDD datasets. The collected data are pre-processed using data normalization and data cleaning. Finally, through this research, the best model suitable for effective intrusion detection can be identified and used for further processes. The proposed models, such as RF+K-means+DNN, AE+K-Means+LSTM, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can obtain an accuracy of 99.278%, 99.33%, 99.45%, 99.50%, 99.65% for the CICIDS dataset 2018 for binary classification. In multi-class classification, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU can attain accuracy of 99.819%, 99.852% and 99.890%. In NSL-KDD, the AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.34%, 99.546% and 99.7%. In UNSW-NB 15 dataset, AlexNet Bi-GRU, AE+Alexnet+Bi-GRU and WABi-GRU achieve accuracy of 99.313%, 99.399% and 99.53%. AlexNet Bi-GRU-based models can obtain better performances than other existing models.
A Trust and Node Capability Model for Reliable and Secure MANET Communication
Authors: Mohan Patsariya Et al.
Published: January 2024

The Mobile Ad-hoc Network (MANET) is a rapidly deployable network. That is valuable for industrial and domestic applications due to flexible, mobile, and wireless communication. But the network is constrained with resources and security. In this paper, we are presenting a node capability based trusted routing named TNC-AODV for MANET. It is a hybrid approach for maintaining route reliability and security. The model is composed of the property of node capability and Trust. The Node capability is defined by the quality of service parameters like remaining energy, available bandwidth, buffer length, and mobility pattern. The aim is to ensure the discovery of reliable routes. Additionally, the trust is implemented by using a local and global trust for securing the network. The TNC-AODV is implemented through modification of AODV routing. That routing technique has been tested on three security threats namely Black-hole, wormhole, and DOS flooding attack. The simulation has been carried out using the NS2 simulator. The experimental results demonstrate that TNC-AODV provides security against attacks. Additionally, improve the packet delivery ratio, and throughput. Finally, the possible and feasible future extension of the work has also been proposed.
A Novel Technique for Enhancing Data Security Using Hybrid Coding in Medical Images
Authors: Anant Shankar Ellapalli Et al.
Published: January 2024

The Internet's rapid development in data transmission has made it simpler to convey data quickly and accurately to the intended recipient. Data is one of the most essential assets to be protected during Internet transmission, thus it is necessary to handle the rising danger of security. Through hacking, unauthorized person can alter or misuse the crucial data. Therefore, security is a primary problem that requiring attention. Compression and encryption are frequently used to share and save images respectively. The effectiveness of digital image services depends on how the two activities are performed. Medical images play a significant role in the modern healthcare system because they are utilized to preserve patient data including illness diagnoses and other pertinent details. Thus, data must be maintained in a secure manner. To deliver data safely and unchanged to the target place, a variety of techniques, including cryptography and steganography are utilized to solve security difficulty, several research and exercises were done to produce a range of tactics and algorithms. In this paper, a technique for concealing the secret image is proposed.
Thermal Image-Based Hotspot Detection System in Solar Panels by Lilnet-5 and Relu6-F-Rcnn
Authors: P.Pradeep Kumar Et al.
Published: January 2024

The effects of high temperatures are the hotspots that occur in solar panels. In previous works, the particular hotspot-affected panel detection on a large scale and the damage-percentage analysis are not concentrated. Hence, this article proposes an efficient detection system for solar panel hotspot identification from thermal images. To convert Photovoltaic (PV) power from one voltage to another voltage level, solar PV panels are employed and connected with the DC-DC converter. The Student T Distributed-Osprey Optimization Algorithm (ST-OOA) is utilized for controlling the converter duty cycle. The output obtained from the converter is in a signal waveform that is converted into a snowflake image by a Symmetrized Dot Pattern (SDP) and given to the LeCun Initialized LeNet-5 (LILNet-5) classifier for fault-panels detection. Afterward, the fault panels’ thermal images are preprocessed along with faults and cracks segmentation by utilizing Rectified Linear Unit 6 Activated Faster Region-based Convolutional Neural Network (ReLu6-F-RCNN). Lastly, to identify the damage percentage of solar panels, power loss is calculated. Moreover, the performance analysis exhibited the framework’s robustness in effective solar panel hotspot detection.
Cyber Crime and Cyber Law’s In India: A Comprehensive Study with Special Reference to Information Technology
Authors: S.Thangamayan Et al.
Published: January 2024

The rapid evolution of information technology has brought about significant advancements in various sectors, but it has also given rise to new challenges, particularly in the realm of cyber security. This study aims to provide a comprehensive overview of cybercrime and cyber laws in India, with a special emphasis on the Information Technology Act, 2000. The research begins by defining cybercrime and elucidating its various forms, including hacking, identity theft, phishing, and other malicious activities conducted in the digital domain. A detailed examination of the cyber threat landscape in India is presented, highlighting the increasing frequency and sophistication of cyber-attacks and their impact on individuals, businesses, and the government. The study then delves into the legislative framework governing cyber activities in India, primarily focusing on the Information Technology Act, 2000, and its subsequent amendments. It explores the legal definitions of cybercrimes, jurisdictional issues, and the powers conferred upon law enforcement agencies to investigate and prosecute offenders. Special attention is given to recent legal developments and case studies that illustrate the application of cyber laws in real-world scenarios. Furthermore, the research scrutinizes the role of government agencies, such as the Cyber Crime Cells, in combating cyber threats and promoting cyber security awareness. The study also assesses the effectiveness of international collaborations and treaties in addressing transnational cybercrimes, considering the borderless nature of cyber threats. The research investigates the role of Information Technology policies, standards, and best practices in India. It analyses the measures adopted by organizations to safeguard their digital assets and sensitive information, highlighting the importance of cyber security awareness and education. The study synthesizes the findings to provide recommendations for strengthening the existing cyber laws, enhancing cyber security infrastructure, and fostering a collaborative approach among stakeholders to mitigate the challenges posed by cybercrime. The research aims to contribute to the understanding of the dynamic landscape of cyber threats in India and serve as a valuable resource for policymakers, legal professionals, and academia involved in the field of information technology and cyber security.
Brain Tumor Detection and Classification Using Hybrid VGG-XGB model
Authors: B.Kalaavathi Et al.
Published: January 2024

To avoid potential fatalities, it is vital to diagnose brain tumors at its earlier stage itself. Tumors vary widely in shape, size, and location and this makes accurate segmentation and classification difficult even with significant efforts in this field. Therefore, a hybrid method for classifying brain tumors called the hybrid VGG-XGBoost (hybrid VGG-XGB) model is proposed in this work. The K-means technique is used to extract significant characteristics from brain tumor images after they have first been improved using contrast-limited adaptive histogram equalization (CLAHE). Then, for extracting the high-level features segmented regions, VGG-19 model has been used and for classification process, XGBoost supervised learning technique has been utilized. As evidenced by criteria like precision, recall, F1-score, kappa score, and the confusion matrix, our novel approach outperformed baseline models and produced excellent results with a 98% accuracy rate. The suggested methodology for brain cancer diagnosis in healthcare systems is recommended by us, based on highly predictive outcomes
An Intelligent Controller for the Signal Generation of Solar Energy and Battery Storage Supported Multi-Level UPQC
Authors: G. Mohan Babu Et al.
Published: January 2024

This study examines, the solar power and battery energy storage associated diode clamped five-level unified Power quality conditioner (5L-UPQC) to handle the PQ related problems. To eliminate the requirement of the complex transformations like abc, dq0, ?? , the ANN based control scheme with LMBP training method is adopted for the 5L-UPQC to produce the necessary reference signals for the voltage source converters (VSC’s). The prime goal of the proposed scheme is to maintain stable DLCV during load shifting, reduction of THD. In addition, the grid voltage distortions like sag, disturbance and swell were eliminated. The suggested method was demonstrated on two cases with several permutations of loads. However, to reveal the performance of the developed method, the comparison is carried out with the PIC and SMC.
Hybrid Convolution neural Network and Graph Neural network for Detection of Leukocyte
Authors: Khalid Altarawneh Et al.
Published: January 2024

In pathology and medical diagnostics, the identification of leukocytes, white blood cells essential for immune system function, is significant. The hybrid neural network model used in this study, which combines the strengths of convolutional neural networks (CNNs) and graph neural networks (GNNs), gives a unique method for the identification of leukocytes. Due to the intricate spatial and relational patterns visible in microscopic pictures of blood samples, leukocyte identification is difficult. The proposed hybrid model incorporates a GNN module to record the contextual associations among leukocytes in a blood sample and a CNN module to extract fine-grained characteristics from individual leukocyte pictures. Each leukocyte picture is seen in this paradigm as a node in a network, with the edges denoting the interactions between the cells' locations or contexts. While the GNN module uses graph-based representations to capture the connections and dependencies between leukocytes within a blood sample, the CNN module efficiently extracts local visual characteristics from leukocyte pictures. These two modules are used to produce a thorough and context-sensitive depiction of the leukocyte detection issue. The importance of this study resides in its potential to increase the precision and effectiveness of leukocyte identification in pathology and medical diagnostics, leading to better patient care and illness diagnosis. This hybrid CNN-GNN model may also be used for a variety of other medical image analysis tasks that call for taking both local and contextual information into account. In successfully identifying leukocytes in microscopic images, this hybrid CNN-GNN technique shows promising results. Accuracy, precision, recall, and F1-score are common assessment metrics used to assess the model's performance.
Charting the Unseen: Transformative Approaches in Time Series Forecasting in Real World Domains
Authors: Sanjeev Kumar Et al.
Published: January 2024

This paper addresses the growing complexity of forecasting in an era where data volumes are expected to reach 180 zettabytes by 2025. It aims to bridge the gap between the theoretical aspects of time series analysis and their practical applications in fields like finance, healthcare, and environmental studies. The research covers foundational concepts such as Autocorrelation and White Noise and spans various methodologies from traditional models like ARIMA to advanced techniques involving machine learning. Special attention is given to the challenges of applying these theories to real-world, often irregular or incomplete data. The paper also explores the integration of technologies like AI in forecasting, emphasizing the need for robust and interpretable models. Concluding with a call for greater academia-industry collaboration, it suggests new research directions for innovative, practical forecasting solutions in a data-intensive world.
Handwritten Number Recognition Using CNN and Comparison of Performances of Models Varying by Layers
Authors: Gayatri Nanasaheb Narale Et al.
Published: January 2024

Now-a-days, the emerging technologies like Deep Learning (DL) leads a drastic turn in the area of Machine Learning (ML) by translating into Artificially Intelligent (AI). DL has several fields of applications viz. medicine, health, surveillance, drones, sports, robotics. In DL, Convolutional Neural Network (CNN) is at the centre of amazing advances that accelerates the field of visual imagery classifications. CNN has utilized broadly in face-recognition, pattern-recognition, handwritten digit-recognition, text categorization, document analysis. The objective of this work is to detect the accuracy distinction of CNN for the classification of handwritten digits via several numbers of layers and epochs. In addition to this, evaluation of the accurateness is incorporated. CNN performance was validated by Modified National Institute of Standards and Technology (MNIST) dataset. By comparing all models varying by layers, a highest validation accuracy of 99.64 % has been reported and the lowest loss gained is 0.0239.
Biological Approaches to Bone Regeneration: Innovations and Clinical Implications
Authors: Nitin S. Patil Et al.
Published: January 2024

In orthopaedics, bone regeneration is still a major difficulty that calls for creative solutions for efficient tissue repair. Modern biological techniques, such as scaffolds, growth factors, tissue engineering, and their therapeutic applications in bone regeneration, are examined in this study. Growth factors—in particular, platelet-derived growth factors (PDGFs) and bone morphogenetic proteins (BMPs)—are essential for promoting osteogenesis and improving bone regeneration. Clinical settings have shown their therapeutic potential; nonetheless, there are ideal doses, administration modalities, and safety profiles to take into account. With their exact designs and variety of biomaterials, scaffolds provide structural support and foster the cellular activity that is essential for bone repair. The functioning and interactions between cells and scaffolds are improved by a variety of manufacturing approaches, including 3D bioprinting and surface changes. Tissue engineering techniques combine scaffolds, cells, and signalling molecules to create useful tissue constructions for bone mending. In tissue-engineered structures, the integration of growth factors and mesenchymal stem cells (MSCs) exhibit potential for augmenting osteogenesis. Clinical applications provide a variety of settings for regenerative therapies, including fracture healing, non-unions, and significant bone defects. However, obstacles to their wider clinical application include safety assurance, scalability, regulatory compliance, effectiveness validation, and personalised therapy. By tackling these obstacles with thorough investigation and translational work, novel biological strategies to improve bone regeneration treatments will become possible.
Advancements in Joint Replacement Surgery: Improving Long-Term Outcomes and Patient Satisfaction
Authors: K.L. Gaonkar Et al.
Published: January 2024

Orthopaedic procedures have changed dramatically in the last several years due to the spectacular developments in joint replacement surgery. The purpose of this review article is to clarify the various developments in joint replacement surgery with an emphasis on improving patient satisfaction and long-term results. The study looks at five important topics: patient selection, prosthetic materials, surgical techniques, technological advancements, and long-term outcomes.

Precision and implant longevity have been transformed by technological advancements such as 3D printing, robotic-assisted surgery, and computer-assisted navigation. Modern surgical methods have a strong emphasis on tissue preservation, less invasive procedures, and improved recovery protocols, which greatly lower postoperative problems and speed up recovery. Prosthetic material advancements including highly cross-linked polyethylene and customised designs have enhanced patient-specific results and implant longevity. In order to maximise surgical success and satisfaction, patient selection criteria, customised care regimens, and psychological support have become essential. When long-term results are assessed, functional abilities, implant durability, and patient satisfaction all show notable improvements; nonetheless, problems with complications and revision operations still exist.
Impact of Mild Level Exercise Program on Incidence of Falls, Gait and Balance Among Elderly People at Selected Rural Areas of Belagavi District.
Authors: Shakeelahmed Mujawar Et al.
Published: January 2024

Context: Exercises that are low-intensity can aid with balance and gait. Regular low-intensity exercise is a beneficial substitute for treating balance and gait issues brought on by ageing. Objective: To assess incidence of falls, gait and balance of elderly people before and after low intensity exercise program and to assess effectiveness of low intensity exercise program on incidence of falls, gait and balance of elderly people.

Setting and design: The study was conducted in selected rural areas of Belgaum district and pre experimental one group pretest post test study design was used.

Materials and Methods: Population was comprised of elderly people aged 60 years and above. The sample size was 30 and non probability purposive sampling technique was used to select the samples. Berg balance scale and Performance oriented mobility assessment scale for gait was used to assess gait and balance..

Statistical Analysis used:Descriptiveand inferential statistics were used with the helpof SPSSversion 20.

Results: In pretest12 (40%) of elders had incidence of fall during the one month prior to pre test and is reduced in post test as 7(23.3%) were had incidence of fall after one month to interventional program. The obtained value of ‘ t’ (29) = 6.97 for gait and ‘ t’ (29) = 2.65 for balance are found significant at 0.05 level of significance, indicating that the low intensity exercise program has helped elderly to improve their gait and balance.

Conclusions: Low intensity exercise program helped elderly people to improve their gait and balance. So, such programs need to be implemented in community area to prevent incidence of fall and improve gait and balance among the elderly population.
“Impact of Mild Level Exercises on Gait, Balance, Functional Status to Prevent Fall Among Elderly In Selected Areas of Belagavi District, Karnataka – A Study Protocol”
Authors: Shakeelahmed Mujawar Et al.
Published: January 2024

Context: Falls, a frequent and preventable cause of morbidity, raise the likelihood of functional independence loss and the requirement for hospitalization and institutionalization, placing a strain on the healthcare system. The annual expenses for acute care linked to fractures from falls are estimated at $10 billion, despite the fact that the entire expenditures for acute care connected to falls remain unclear.

Aims: To administer the mild level exercise programme of elderly among experimental group and to determine the effect of mild level exercise program on improvement of gait, balance and functional status of elderly among experimental group.

Settings and Design: The samples from the selected rural areas of Belagavi district were selected using convenient sampling technique. A quantitative approach with post test only control group design was adopted for the study.

Subjects and Methods: Elderly above the age of 60 years of selected rural areas of gokak taluka belagavi district and total 250 samples selected by using convenient sampling technique and my research approach is Quantitative Research Approach, Research deign is Post test only control group design and Sampling technique is Non-Probability; Convenient Sampling Technique respectively.

Conclusions: The mild level exercise program was very much effective and the elders had great interest to learn and to do the exercises to improve their gait, balance, functional and to reduce the incidence of fall.  Continuing nursing education programs can be conducted in hospitals and community regarding the importance of low intensity exercise program in improving gait, balance, functional status and in reducing the incidence of falls in the elderly people.
Effectiveness of Learning Package on Impact of COVID-19 Pandemic on Physical, Mental, Social and Economic Status among Adults at Selected Villages of Gokak Taluk, Karnataka.
Authors: Prakash Hiremath Et al.
Published: January 2024

Context: As of march 11, 2021 more than 11 million people in India had been infected with COVID-19, and about 158,000 had died. The spread of COVID-19 in India is of great concern due to the country’s large and densely populated areas with widespread poverty and high migration rates, coupled with a high prevalence of chronic conditions that are generally poorly controlled.

Objective: To develop and evaluate learning package to overcome from the impact of COVID-19 among adults.

Setting and design: The study was conducted in selected villages of Gokak Taluk and pre-experimental research design was used.

Materials and Methods: Population was comprised of adults aged between 18-60 years old. The sample size was 06 and simple random sampling technique was used to select the samples. Self constructed assessment scales were used. Learning package was developed and evaluated for its effectiveness.

Statistical Analysis used: Descriptive and inferential statistics were used.

Results: The pre-test mean score for functional status was 11.50 and SD was 2.429, anxiety status was 13.67 and SD was 2.685, stress status was 30.00 and SD was 2.898, social status was 26.50 and SD was 4.370 economic status was 5.67and SD was 0.816. In post-test mean score for functional status was 7.50 and SD was 2.258 with mean difference score of 4.00, anxiety status was 7.33 and SD was 1.366 with mean difference score of 6.33, stress status was 7.33 and SD was 0.81565 with mean difference score of 22.66, social status was 17.00 and SD was 2.098 with mean difference score of 9.50, economic status was 3.33 and SD was 0.516 with mean difference score of 2.33. The statistical paired ‘t’ implies that the difference in the pre test and post test score was found to be statistically significant at 5% level. There exists a statistical in the enhancement of score indicating the positive impact of learning package.

conclusions: The impact of COVID-19 was more before distributing the learning package. Hence the learning package was effective.
Recurrent Spontaneous Pneumothoraxes as A Complication of Osteosarcoma Metastases: A Case Report
Authors: Nandish A L. Et al.
Published: January 2024

The most prevalent primary malignant bone tumour in children and adolescents is osteosarcoma. Osteosarcomas are extremely aggressive tumours with a poor prognosis historically. However, with the addition of adjuvant and neoadjuvant chemotherapy, the survival rate has significantly increased. This case report describes a 18-year-old boy who had a left fibula osteosarcoma whose progress was worsened by recurring pneumothoraces associated with the tumour. Recurrent pneumothoraces are a relatively uncommon complication of osteosarcoma, but they provide a significant obstacle to patient care that improves outcomes and quality of life.
Design, Simulation and Analysis of a Replacement-free Implanted Thermoelectric Generator based Pacemaker
Authors: Shwetha.M Et al.
Published: January 2024

This paper deals with a novel design approach to pacemakers that will enable the implanted pacemakers to be replacement free. The major drawback of the current design of pacemakers is their expiry date of around twelve years. This timeframe is intensified by the lowered age range when heart disease turns critical. Pacemaker failure is one of the most common forms of heart disease, and implanting an artificial pacemaker is the most acceptable and viable solution.