bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating Manual Therapy and Exercise: Evaluating the Efficacy of Muscle Energy Technique Combined with Free Exercises in Alleviating Symptoms of Chronic Rheumatoid Arthritis
Authors: Trupti warude Et al. 
Published: January 2024

Introduction: Autoimmune chronic disease Joint pain, inflammation, and irregularities are RA symptoms. Everyone's physical and emotional health is affected by this awful sickness. RA treatments are being investigated to improve life. Even while medications treat RA, physical therapy is growing. Patients can receive MET for musculoskeletal issues. The effects of MET on chronic rheumatoid arthritis quality of life are unknown. Free MET's impact on chronic rheumatoid arthritis sufferers will be studied. We seek to improve treatment options and empower patients through active rehabilitation by studying this unknown territory. Few have studied advanced manual therapy like MET for RA, making this study intriguing. A comprehensive MET and free exercise study may improve long-term RA patients' clinical processes and quality of life. This study addresses a knowledge gap, recommends future research, and promotes awareness of how advanced manual therapy may improve quality of life for chronic rheumatoid arthritis patients.
Objectives:To determine the impact that the muscular energy technique has on the quality of life of people who suffer from chronic rheumatoid arthritis. To investigate the impact that free workouts have on the quality of life of people who suffer from chronic rheumatoid arthritis.
Conclusion:This study found that free exercises and MET enhanced RA patients' quality of life. Therapeutic interventions targeting systemic inflammation and polyarthritis were guided by HAQ and NRS testing to reduce discomfort, strengthen, and enhance function. Hot moist packs reduced stiffness and NRS values declined quickly during MET and free exercises, relieving discomfort. 30–60-year-olds maintained muscle tone and power with medication and targeted exercises. After isometric relaxation, manual treatment harmonized inputs, reduced discomfort, and improved pain, disability, and joint range. High HAQ ratings suggested better quality of life, suggesting rigorous manual treatment and exercise may assist chronic RA. These findings advise treating rheumatoid arthritis using tailored methods and more investigation.
Emotion Detection and Classification using Hybrid Feature Selection and Deep Learning Techniques
Authors: Rahul Subhash Gaikwad Et al. 
Published: January 2024

Image sentiment analysis has gained significant attention due to the increasing availability of user-generated content on various platforms such as social media, e-commerce websites, and online reviews. The core of our approach lies in the deep learning model, which combines the strengths of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNN component captures local dependencies and learns high-level features, while the LSTM component captures long-term dependencies and maintains contextual information. By fusing these two components, our model effectively captures both local and global context, leading to improved sentiment analysis performance. During the execution first select the context and generate visual feature vector for generation of captions. The EfficientNetB7 model is applied in order to construct the image description for every individual picture. The Attention-based LSTM as well as Gated Recurrent Unit (GRU) greedy method are the two approaches that are utilized in the process of classifying sentiment labels. The proposed research has been categorized into three different phases. In Phase 1 describe various data preprocessing and normalization techniques. It also demonstrates training using RESNET-101 deep learning-based CNN classification algorithm. In Phase 2 extract the various features from the selected context of input image. The context has been selected based on detected objects from the image and generates a visual caption for the entire dataset. The generated captions are dynamically used for model training as well as testing to both datasets. The EfficientNet module has used for generation of visual context from selected contexts. Finally in phase 3 classification model has built using a Deep Convolutional Neural Network (DCNN). The proposed algorithm classified the entire train and test dataset with different cross- validations such as 5-fold, 10-fold and 15-fold etc. The numerous activation functions are also used for evaluation of the proposed algorithm in different ways. The higher accuracy of the proposed model is 96.20% sigmoid function for 15-fold cross validation.
Iot Solutions for Enhancing Agricultural Practices and Environmental Sustainability
Authors: K. Vijayalakshmi Et. al 
Published: January 2024

This investigate points to think about the results of way better appropriation of IoT within the field of farming. The think about points to appear how made strides cultivating strategies through IoT can give superior results for the environment. The research was able to extend the keen water system and precisely anticipate the yields through optimization calculations. Agriculturists can make their farms' results way better by taking more brilliant and productive choices through Choice Back Frameworks (DSS). The savvy cultivating (accuracy cultivating) has demonstrated to be useful for the environment. Through detailed studies and evaluations of the life cycle and impact on ecosystems, it has been revealed that this method provides healthier soil, increased biodiversity and lowers carbon emissions. The expenses of cultivation can be minimized by lowering water consumption. This is important because it lowers the issue of the shortage of water. The outputs from the model will highlight the importance of utilizing water resources in an optimized way for farming through IoT based methods. The Decision-Making Systems have been proved to be significantly useful in agricultural activities; they provide an efficient way to make important business decisions. The systems were designed to provide in-time data to the network so that farmers can make their desicions up-to-date.
Role of Artificial Intelligence in Cardiovascular Risk Prediction and Prevention
Authors: Abhijeet Shelke Et al. 
Published: January 2024

Globally, cardiovascular diseases (CVDs) continue to be the leading cause of death, making precise risk assessment and efficacious preventative measures imperative. Although essential, traditional cardiovascular risk assessment instruments like the Framingham Risk Score have shortcomings when it comes to precisely identifying individual risks. The use of Artificial Intelligence (AI) into the prediction of cardiovascular risk presents a revolutionary strategy to overcome these constraints. Artificial Intelligence (AI), which includes deep neural networks and machine learning algorithms, improves risk assessment through the analysis of large datasets, allowing for personalised risk forecasts that go beyond traditional risk indicators. The transition from population-based risk assessment to individualised profile is signalled by this integration, which will increase accuracy and facilitate prompt actions.
AI-powered models outperform conventional approaches in detecting complex risk variables and trends, providing higher forecasting accuracy. These models provide personalised risk profiles by utilising a variety of data sources, such as lifestyle, medical imaging, and genetic information. This allows for more focused preventative actions. In addition, AI applications in preventive cardiology include risk assessment, customised care plans, and early diagnosis via sophisticated imaging analysis.
Widespread adoption is hampered, nevertheless, by issues with data quality, AI model interpretability, generalizability across different populations, and ethical issues. In order to fully utilise AI to transform preventive cardiology and emphasise openness, morality, and ongoing technological breakthroughs, it will be essential to overcome these obstacles.
Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction using Machine Learning Algorithms
Authors: Sarat Kumar Chettri, HMM. Naleer CMM. Mansoor, 
Published: January 2024

Throughout history, humanity has been plagued by several outbreaks that have claimed numerous lives. Since coronary artery disease is among the most fatal illnesses that humanity has faced in the modern era, it has been recognized in our time. It links several Coronary Artery Disease (CAD) risk factors to the critical requirement for precise, reliable, and workable methods for early identification and management. In light of this, we suggest a technique called Hybrid Ensemble Stacking that combines Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Ada Boosting for the prediction of CAD illnesses. To combine the forecasts of the basis models, a meta-logistic regression model is utilized. According to a quantitative study, the ensemble model and brute force feature selection method together produce a classification accuracy for heart disease of up to 92.66%. The suggested stacking model has demonstrated its effectiveness and outperforms current methods in the categorization of cardiac disorders. Several classification issues have been solved successfully using ensemble techniques. The suggested method was constructed using the Sani dataset, which contains 303 nearly completed records. Using Min-Max Normalization, the data are pre-processed to making it suitable for a Machine Learning (ML) model. SMOTE and SelectKBest technique were applied to increases the accuracy and efficiency of a model. Using the metrics such as accuracy, precision, recall, F1, ROC and log-loss, the outcomes produced by the suggested model had the greatest performance.
Applying Ensemble Machine Learning Techniques for Fake News Identification
Authors: Vipul Devendra Punjabi Et al. 
Published: January 2024

The sharing of information has entered an unprecedented era in human history due to emergence of the Internet With the widespread use of social media sites like Facebook and Twitter. As these platforms enjoy extensive use, users are generating and disseminating a wealth of information, some of which is inaccurate and devoid of factual basis. Detecting false or misleading information within textual content poses a significant challenge. Before arriving at a judgment regarding the accuracy of an article, it is imperative to consider various factors within a specific domain. This paper proposes an Ensemble method for the identification of fraudulent news stories. We leverage different textual features found in both authentic and fake news articles. Our dataset comprises 72,134 news articles, with 35,028 being genuine and 37,106 being false, categorized as binary 0s and 1s. To evaluate our approach, we employed well-known machine learning classifiers including Logistic Regression (LR), Decision Tree, AdaBoost, XGBoost, Random Forest, Extra Trees, SGD, SVM, and Naive Bayes.
To enhance the precision of our findings, we devised a multi-model system for identifying fake news the Ensemble approach and the aforementioned classifiers. Experimental analysis conclusively demonstrates that our suggested ensemble learning technique surpasses the performance of individual learners.
A Deep Learning Framework for Early Detection of Potato Plant Diseases
Authors: Harsh Pratap Singh Et al. 
Published: January 2024

In our rapidly evolving world, technology has become an inseparable aspect of daily life, with digital tools serving as essential aids for various tasks. Despite this, individuals and farmers encounter persistent challenges arising from sluggish internet connectivity and needing external assistance to manage online resources. These obstacles significantly hinder their capacity to oversee crops and promptly address potential plant diseases. A noteworthy predicament farmers face involves sluggishly identifying diseases within their crops, often compounded by a lack of knowledge about appropriate remedies. Traditional techniques for pinpointing plant diseases tend to be time-intensive and demand specialised expertise. Such circumstances present formidable obstacles for farmers lacking access to expert guidance or those grappling with limited time for effective crop management.
We have developed a Deep Convolutional Neural Network (DCNN) model to address this predicament to detect disease. The core purpose of our application is to expedite and enhance disease recognition for burgeoning plants, ultimately benefiting farmers. The mobile/web interface provides users with a user-friendly means of querying plant diseases and their potential treatments by capturing images of their plants. Basic functionalities encompass disease identification, enabling users to ascertain afflictions swiftly. Furthermore, the application includes provisions for storing crop-related data and tracking progress, thereby empowering farmers to make well-informed decisions concerning crop management.
At its essence, our project seeks to empower individuals and farmers with tools indispensable for proficient crop oversight. The application is a conduit for expedited disease detection and prompt mitigation, fostering healthier plants and elevated yields. Ultimately, our innovation holds the potential to substantially contribute to sustainable agricultural practices, effectively addressing the modern challenges encountered by farmers within our technology-driven milieu.
Novel Design of High Performance of MIMO antenna for 5G Bio-Telemetry applications
Authors: B.Santhi Kiran Et al. 
Published: January 2024

A MIMO antenna with 4 L-shaped elements and 2 E-shaped elements for the ISM band and also for wireless communication is proposed in the paper. This antenna contains 4 L slots in an H manner and 2 E slots are cut in the H slot. The dimensions of this proposed antenna are 40*30*1.6mm^3. The substrate used in this antenna is an FR4 substrate with a permittivity of 4.4. The operating frequency of this antenna is 2.4GHz and 5.1GHz. All four L-shaped elements are placed in an orthogonal manner. The maximum gain is -4.4dB and the minimum gain is -16.2. The efficiency of this proposed antenna is 2.45. The radiation efficiency is 2.45dB and 75%. Fed can be provided using CPW. There is no need to concentrate on the isolation issue as we do not have several notches. This antenna is probably the most efficient antenna for the ISM band and also for wireless communication as a single-notch antenna is able to resonate at both frequencies.
Electrical Muscle Rehabilitation Apparatus Using a Optocoupler and Mega-Microcontroller
Authors: Sinan S. Mohammed Sheet Et al. 
Published: January 2024

Muscle atrophy is a prevalent issue observed in individuals who experience immobility or restricted mobility as a result of accidents or illnesses. Physical therapy and exercise, often employed interventions for muscle atrophy, may occasionally lack applicability. The utilization of muscle electrical stimulation has demonstrated potential as a viable therapeutic option; however, further investigation is important to optimize its effectiveness and ensure its safety. This study investigates the possible advantages of implementing electrical muscle stimulation (EMS) and explores the utilization of a transformer, optocouplers, and MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) to enhance the efficacy of a high-capacity microcontroller-based EMS device. The issue of malfunctioning power transistors was successfully addressed through the implementation of an optocoupler-MOSFET circuit as a replacement for the power transistor. The findings of this investigation indicate that a direct electrical linkage between the signal-generating circuit and the transformer input is absent. The aforementioned features serve to ensure the safeguarding of the microcontroller circuit, establish electrical isolation between different stages of the system, and enhance the overall reliability of the EMS-designed system. The proposed framework introduces a novel technique that demonstrates a deliberate and considered approach when compared to existing studies in the field.
Protein Secondary Structure Prediction and Perceptions of Complexities using Deep Neural Network
Authors: Subhendu Bhusan Rout Et al. 
Published: January 2024

The Protein molecule is known as the large biological molecule in a living organism. The protein performs several works like transporting molecules, catalysing metabolic reaction, responding to stimuli etc in a human body. Protein Structure analysis and prediction is very much essential to make any research about the same protein molecule. The basic intention of protein structure prediction (PSP) is to predict the three dimensional structure that generate by the amino acid sequence. The very peculiar matter is only twenty amino acid found in a living body where as approximately one lakh protein molecules can be framed from the same amino acid compositions in different percentages. The three dimensional structure framed by the amino acid compositions generally changes its shape and size due to the effect of external agents or medicines that comes in contact with these protein molecules. The basic intention behind the prediction of structure of the protein is to design new drugs or medicines. From the structures the medicine researchers working for the development of medicines may easily detect the changes in the living body or the requirement of drugs or medicines. The detection of the structure and the prediction of perfect structure is always a challenging task. The protein structure is basically a three dimensional structure in its secondary transformation. The structure may be in the form of ? Helix, ? sheets or loop etc. In this paper the identification of the secondary structures and the percentages of ? Helix, ? sheets or loop structures are being predicted and the probable complexities that may occur during the prediction is discussed. Deep neural network is a deep structured learning process is an application of the broader family machine learning. Deep learning architectures has a number application in various fields like medical science, bioinformatics, medical image analysis etc. A novel method is being proposed in this research article for the detection, correction and removal of various complexities during prediction using deep neural network. This technique will be helpful for different researchers working in the field for drug design and medicine research.
Topological Data Analysis for Software Test Cases Generation
Authors: Mani Padmanabhan Et al. 
Published: January 2024

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.
Compact-size and Broadband Microstrip Patch U slot Antennas for C band Application and Stable Radiation Pattern overall band
Authors: Rajeev Kumar Thakur Et al. 
Published: January 2024

A wide band antenna using U slot and truncated ground plane antenna is presented, The antenna used four U slot at the reflecting patch and via hole is shorted in between radiating and reflecting patch of an antenna, the antenna is all four corner truncated and four U shape is printed on ground plane. The proposed antenna is suitable for C band applications and stable radiation pattern to overall band. The antenna is resonance at a band of 7.1 to 14 GHz. The VSWR bandwidth of the proposed antenna is 65.40% and achieved a directivity of 6 dBi and gain 3.9 dBi. The size of the of the antenna is 18.18x15.541x1.5 mm3. The antenna is suitable for Wimax, WLAN and other C band applications.
Automated Detection of Autism Spectrum Disorder Using Bio-Inspired Swarm Intelligence Based Feature Selection and Classification Techniques
Authors: S.Saravana Kumar Et al. 
Published: January 2024

Autism spectrum disorders, or ASDs, are neurological conditions that affect humans. ASDs typically come with sensory issues like sensitivity to touch or soundor odour. Though genetics are the main causes, their early discovery and treatments are imperative. In recent years, intelligent diagnosis using MLTs (Machine Learning Techniques) have been developed to support conventional clinical methods in the domain of healthcare. Feature selections from healthcare databases consume nondeterministic polynomial timesand are hard tasks where again MLTs have been of great use. AGWOs (Adaptive Grey Wolf Optimizations) were used in this study to determine most significant features and efficient classification strategies in datasets of ASDs. Initially, pre-processing strategies based on SMOTEs (Synthetic Minority Oversampling Techniques) removed extraneous data from ASD datasets and subsequently AGWOs repeat this procedure to find smallest features with maximum classifications values for recall and accuracy. Finally, KVSMs (Kernel Support Vector Machines) classify instances of ASDs from the input datasets. The experimental results of suggested method are evaluated for classifying ASDs from datasets instances of Toddlers, Children, Adolescents, and Adults in terms of recalls, precisions, F-measures, and classification errors.
SM-DBERT: A Novel Symptom-based Technique for Chronic Disease Classification using DISTILBERT
Authors: Swati Saigaonkar Et al. 
Published: January 2024

Machine learning and deep learning models when applied on EHR systems are considerably augmenting the prediction tasks performed on medical data. Humongous amount of information lies in the free form clinical texts. But there exist challenges associated with such kinds of unstructured data. Transformers based models like Bidirectional Encoder Representations from Transformers (BERT) has revolutionized the work. DISTILBERT, a lighter version of BERT, is even promising as the time required is reduced to nearly one-third without losing the performance. In this research work, we present SM-DBERT, Symptom-based Modified DistilBERT architecture designed for Chronic Diseases. The foundation of SM-DBERT is symptomatology, as an optimal model should prioritize symptoms as they are the key indicators. The existing DISTILBERT architecture has been modified by introducing additional layers and extra embeddings of external knowledge and presented along with input ids and attention masks. These extra knowledge helps the model to learn more relevant information. SM-DBERT has demonstrated notable improvement in the results. The accuracy obtained with this novel approach is 0.98 as against the basic DISTILBERT model.