bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing the Efficacy of Exercise Interventions and Conventional Therapies in Enhancing Functional Mobility for Rheumatoid Arthritis Patients
Authors: Sandeep Shinde Et al.
Published: January 2024

Introduction: Chronic autoimmune disease rheumatoid arthritis affects well-being and requires careful management. Flexible approaches are needed due to the disease's systemic and age-specific impacts. Exercise is encouraged by the limits of medicinal therapy. Social restrictions may promote inactivity, especially for RA patients, during the COVID-19 pandemic. Traditional settings have downsides, but home training may help. This study fills gaps on home-based exercise's effect on RA pain and mobility. Home-based chronic illness management and patient-centered treatment are emphasised in shifting healthcare dynamics. The study may improve rheumatoid arthritis treatment and teach doctors how to adapt.

Background: Rheumatoid arthritis (RA) affects 0.5% to 1.5% of the world's population and damages synovial joints. Joint destruction and systemic repercussions characterise this chronic inflammatory disease, which mostly affects women aged 30–55. The COVID-19 pandemic has changed RA management, leading to home-based activity programmes for patient safety and long-term health. RA management, COVID-19 difficulties, and flexible, patient-centered therapy are examined in this study.

Result: Systemic RA shortens life, damages joints, disables, and reduces quality of life. The global prevalence of RA is 0.5%–1.5%. RA patients struggle with COVID-19 control strategies that emphasise social distance and home activities. These programmes are useful for lowering socioeconomic restrictions. The study compares home exercise to standard therapy for RA patients' functional mobility. Six weeks of home-based or traditional exercises were given to 30 adults aged 30-60. Statistics showed significant improvements in both groups, notably household activities. This study proposes incorporating home exercise into professional practises for RA patients to improve pain management and quality of life, especially globally.

Conclusion: This study shows that RA patients need home-based fitness routines. Due to RA's chronic and systemic impact on joint health and the COVID-19 pandemic, home-based exercises are an effective treatment. In a six-week study of 30 30-60-year-olds, home-based and conventional treatments improved functional mobility and pain management. The home-based exercise group did well, confirming its efficacy. The study advises adding home-based exercise regimens to RA care to empower individuals to improve their quality of life and control health concerns
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.
Applications of Deep Learning and Machine Learning in Healthcare Domain – A Literature Review
Authors: Beschi I S Et al.
Published: January 2024

In recent years, Artificial Intelligence (AI) has advanced rapidly in terms of software algorithms, hardware implementation, and implementations in a wide range of fields. The latest advances in AI applications in biomedicine, such as disease diagnostics, living assistance, biomedical information processing, and biomedical science, are summarised in this study. Brain-Computer Interfaces (BCIs), Arterial Spin Labeling (ASL) imaging, ASL-MRI, biomarkers, Natural Language Processing (NLP), and various algorithms all help to reduce errors and monitor disease progression. Computer-assisted diagnosis, decision support systems, expert systems, and software implementation can help doctors reduce intra- and inter-observer variability. In this paper, numerous researchers conduct a systematic literature review on the application and implementation of Machine Learning, Deep Learning, and Artificial Intelligence in the healthcare industry.
Carbon Property of Soil Prediction By VIS/NIR Spectroscopy Using DrSeqANN
Authors: Digambar Aggayya Jakkan Et al.
Published: January 2024

Carbon(C) levels have a direct impact on plant health and productivity. 200 soil samples from the Indian state of Uttar Pradesh were utilized in this study as a database to assess the efficacy of employing visible/near-infrared (VIS/NIR) spectroscopy data. The samples wavelengths ranged from 350 to 2,500 nm. The spectral features used to predict C were chosen using Ensemble Lasso Ridge Regression (ELRR), Random Forest (RF), and the more complicated Artificial Neural Network. The preprocessing employed the log derivative, Log10x derivative, and inverse derivative to replicate the wavelength of the spectrum. The essential feature wavelengths for C were discovered to be between 350 and 450 nm, according to the results. The recommended Dropout Sequential Artificial Neural Network (DrSeqANN) technique combined with the Log10x pre-processed data produced the most accurate results.
Enhancing Data Security in Cloud Computing: A Comparative Analysis of Encryption Techniques
Authors: Al-Mukhtar Wijdan Noaman Marzoog
Published: January 2024

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations.
Mathematical Modeling of Multi-Server Queueing Model for Vacations and Impatient Customer
Authors: Malik Kanta
Published: January 2024

The combination of excursions and the powerful treatment of fretful clients are at the focal point of this review, which digs into the streamlining of multi-server queueing frameworks. Modified Priority Queue Algorithm (MPQA), Vacation Scheduling Algorithm (VSA), Impatience Handling Algorithm (IHA), and Server Utilization Balancing Algorithm (SUBA) are the four calculations that have been proposed and thoroughly assessed through recreations to decide their viability in decreasing line length, holding up time, and improving framework throughput and server usage. The MPQA shines on clients considering anxiety cutoff points and organization times, showing suitability in decreasing holding-up times. Then again, VSA continuously changes server get-away, yielding lower holding-up times during fame periods. IHA progressively oversees eagerness, diminishing takeoffs brought about by fretfulness and expanding framework throughput in general. SUBA in a perfect world adjusts server use, achieving additionally created throughput and system strength. The extraordinary commitments of each calculation are revealed in examinations with related work. VSA is in accordance with past examinations an extended get-away streamlining, while MPQA works on traditional ways to deal with prioritization. IHA presents dynamic energy dealing with, and SUBA lines up with liability careful server activation systems found in the composition. This assessment basically advances the appreciation of multi-server queueing systems, offering valuable solutions for organization arranged conditions. An establishment for future improvements in queueing hypothesis and genuine applications is given by the calculations, which were assessed with significant outcomes.
Accelerating Choices: Key Factors Shaping Car Ijarah Adoption in Pakistan
Authors: Ejaz Aslam Et al.
Published: January 2024

Car Ijarah is a vital Islamic banking product which is contributing anomalously to the economic growth of Pakistan. The research study is conducted to explore the important factors influencing consumers to use car Ijarah among customers in Pakistan using the Theory of Planned Behavior. A research instrument is used to collect the data of 250 Islamic bank customers who intend to use car Ijarah facility and multiple regression analysis is applied. The results proved that subjective norms and self-identity expressiveness lead to intentions to use the Ijarah facility in Pakistan. On the other side brand attitudes, perceived behavioural control and social identity expressiveness don’t lead to intentions to use car Ijarah. The findings of the study provide policymakers, scholars, and regulators with important information on how to modify their tactics and approaches to increase car Ijarah's acceptance in Pakistan. Moreover, the study renders information in shaping Islamic finance service providers' marketing strategy for an effective demand in relation to car Ijarah.
Investigation of the Stochastic Harmonic Distortion Caused by Multiple Converters in Micro-Grid
Authors: Vaishali P. Kuralkar Et al.
Published: January 2024

The primary emphasis of this inquiry is on the influence of voltage-source converter harmonics on the power quality of a power system. The level of harmonic distortion produced by several VSCs may significantly vary in the presence of uncertainties, making it challenging to predict its behavior due to these fluctuations. These uncertainties may arise due to the selection of design parameters or the adjustment of system characteristics. Therefore, it is essential to use statistical techniques to quantify the levels of VSC harmonic distortion in the presence of uncertainty. The experimental assessment of the UDR's performance was conducted using a practical microgrid lab that included three VSCs. The MCS approach functioned as a benchmark for evaluating the precision of the anticipated UDR results. The UDR consistently achieved the expected results, saving a large amount of time compared to the MCS technique. Furthermore, the UDR findings closely aligned with the MCS strategy.
Application of Laplace Transform in Science and Engineering
Authors: Ibrahim Salisu
Published: January 2024

One reliable mathematical tool that is used extensively in many scientific and technical fields is the Laplace transform (LP). Similar to the application of transfer functions in solving ordinary differential equations (ODEs), LPs offer a simple method for tackling increasingly complex engineering problems. LPs are used in physics and engineering, and this research first looks at such uses before concentrating on how they are used in electric circuit analysis. The research also explores more sophisticated uses, such as load frequency control in power systems engineering.
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.
Integrating Machine Learning and Mathematical Programming for Optimisation of Electric Discharge of Machining Techniques
Authors: Ritu Gupta Et. al
Published: January 2024

This study explores the combination of machine guidance and several developing approaches to enhance both precision and effectiveness during Electricity-discharged Machining (EDM) business operations. The studies on quality control, energy efficiency, sustainable development, mathematical modelling within EDM optimization, and machine learning applications in EDM optimisation are all examined in this study. It highlights significant gaps in scientific knowledge, providing a pathway for the development of state-of-the-art EDM methods. The outcomes show that material decrease, energy efficiency, along EDM technique optimisation can all be enhanced. This study offers valuable information for future research within the field and contributes to the ongoing conversation about advanced manufacturing techniques. This project intends to revolutionise EDM by merging mathematical programming and machine learning. Three primary topics are investigated machining parameter optimisation, efficiency improvement using machine learning and environmental effect assessment. The goals of the study are met by using the deductive method, which gives a formal setting in which to examine hypotheses. Descriptive research designs allow for in-depth analyses of previously published works, mathematical models and automated learning programs. Finding commonalities and trends in qualitative data is the goal of the thematic data analysis technique. The results of this study provide useful resources, standards and sustainable perspectives for enhancing EDM procedures in manufacturing settings.
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.
Innovations in Minimally Invasive Techniques for Urinary Stone Management
Authors: Abhijeet Ravindranath Katkar Et al.
Published: January 2024

The therapy of urinary stones has undergone a revolution because to minimally invasive procedures, which offer less recovery time and lower morbidity than open surgery. This extensive study delves into the latest developments in laser lithotripsy, ureteroscopy, shockwave lithotripsy (SWL), percutaneous nephrolithotomy (PCNL), and new technologies. The investigation looks closely at these novel techniques' effectiveness, drawbacks, and changing patterns. Improvements in patient screening criteria and shockwave delivery devices, among other technological advancements in SWL, have maximised therapy results. Advances in URS, such as flexible ureteroscopes and sophisticated imaging modalities, have increased accuracy and broadened the range of stones that may be treated. PCNL's advancements in imaging and equipment miniaturisation have increased access and stone fragmentation efficiency. With its accuracy and variety of fibre shapes, laser lithotripsy is becoming a more attractive option. Looking ahead, the main concerns are going to be technology integration, tailored medication, and all-encompassing patient-centered care. For adoption to be widely adopted, issues with cost, accessibility, and complexities must still be resolved.
Application of Laplace Transform in Science and Engineering
Authors: Salisu Ibrahim Et al.
Published: January 2024

One reliable mathematical tool that is used extensively in many scientific and technical fields is the Laplace transform (LP). Similar to the application of transfer functions in solving ordinary differential equations (ODEs), LPs offer a simple method for tackling increasingly complex engineering problems. LPs are used in physics and engineering, and this research first looks at such uses before concentrating on how they are used in electric circuit analysis. The research also explores more sophisticated uses, such as load frequency control in power systems engineering.
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.
Blockcloud: Merger of two Big Technologies Blockchain & Cloud Computing
Authors: Manju Kaushik, Surendra Kumar Yadav Shalu J. Rajawat,
Published: January 2024

A Blockchain is a continuous list - a structure like a file containing blocks connected by links, and the storage of blocks is done with the help of cryptography whereas Cloud computing is the need for computing resources, especially data storage (cloud storage) and computing power, without the direct control of users. Large cloud operations are often distributed across multiple locations, each with a data center. Both these technologies have their individual working and responsibilities and plays their roles very perfectly which makes them high demanding in today’s world but if they both work together then it will upgrade the future of IT market because Blockchain makes a difference in cloud storage which keeps information secure and tamper-proof. Companies can be certain that their information is secure; erasing information from a computer does not erase information held on other blockchain arrange gadgets. In this way, information put away within the cloud utilizing blockchain innovation is secure and secure. In cloud computing, blockchain can make an organize of hubs that share data and handle vitality. This guarantees that companies don't require a central benefit supplier. Instep, they can depend on organized computers that are not controlled by a single company which is beneficial in all aspect. This paper showcase all benefits by merging these two big technologies Blockchain and Cloud computing and propose new term for this Merger as “BlockCloud”.
Ontology Creation and Development in Proposed Multi-Tenant Cloud Architecture
Authors: Manju Kaushik, Barkha Bahl Dimpy Jindal,
Published: January 2024

There is a need for efficient administration of shared resources and data across different tenants as a result of the fast rise of cloud computing and the rising usage of multi-tenant systems. The use of ontologies as a strong tool for organizing and describing knowledge in a variety of fields has recently gained traction. In the context of a multi-tenant cloud architecture that has been presented, the establishment and development of ontologies is the primary emphasis of this research work. The study provides a complete examination of the difficulties and possibilities associated with the process of ontology building, as well as the advantages that this process might offer to multi-tenant cloud settings. The design that is being presented has the goal of improving resource allocation, data integration, and knowledge exchange among tenants, which will ultimately result in increased productivity and cooperation.
The Prediction of GDP by Aggregate Accounting Information. A Neural Network Model
Authors: Mu Sun Et al.
Published: January 2024

Currently, accounting information plays a key role in the economic world and bridges the gap between macroeconomics and microeconomics. The existing literature corroborates that aggregate-level accounting earnings data encapsulates information regarding GDP growth. Nonetheless, accounting data derived from GDP components have not been given due consideration. Consequently, this research proposes an aggregate-level model grounded in the four components of the GDP income-based method, intending to assess the predictive power of aggregate-level accounting data concerning GDP. Furthermore, this paper scrutinizes the forecasting performance of a neural network model built upon the conventional linear regression framework. Finally, a comparison of the outcomes derived from both models is conducted. The findings reveal that both the present value model and the value-added model corroborate the notion that accounting information derived from the four components of the income-based GDP accounting framework encapsulates data pertinent to future GDP. Furthermore, the model demonstrates heightened sensitivity towards the performance of the subsequent second quarter's GDP. Among the variables, Depreciation and Income exhibit the most substantial impact, while Salaries exhibit the least impact. Concurrently, this research noted an improvement in the fitting performance of the neural network model as compared to that of the traditional linear model.
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.
Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art
Authors: Sumaiya Shaikh Et al.
Published: January 2024

Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos.
IoT-Enabled Smart Robotic System for Greenhouse Management using Deep Learning Model with STS Approach
Authors: Kamlesh Kalbande Et al.
Published: January 2024

A significant component of any country's Gross Domestic Product is made up of farming and agriculture. Utilizing IoT in agriculture and farming methods is essential as the global population is projected to reach around 9.6 billion by 2050. To meet such high demand, an improvisation and optimization of the current farming technologies is the need of the hour. Numerous researchers developed different application specific system for agriculture but less attention was paid towards critical aspects such as intelligence, modularity and human centric design. There is lacuna in existing developed system in the utilization of advanced technologies to their full potential. The agricultural sector wants autonomous systems that are smarter and more effective. Therefore, this research paper introduced the smart solution as an intelligent modular autonomous system with human-centric design approach for agricultural application. The developed system able to detect plant disease with more than 96% accuracy with the help of novel deep learning model designed with sharpening to Smoothening approach. The disease detection and classification results has been verified through confusion matrix method of evaluation. An intelligent robotic system has been developed to detect plant diseases using novel deep learning model and perform multiple functions like greenhouse monitoring, pesticide sprinkling etc. The robotic system has control over internet through web control system so that farmer can monitor greenhouse and control robot activity from remote place. This smart farming solution able to make farmer life simpler and perform difficult task like plant disease detection and pesticide sprinkling easily.
An IoT-Based Framework for Enhanced Construction Material Management and Tracking
Authors: Sameer Jain Et al.
Published: January 2024

Effective inventory management is essential for determining a project's growth and success in the construction industry. Recent studies have revealed that conventional techniques for managing construction materials and components still depend on human capacities, despite the fact that they account for 50%–60% of the entire cost of a typical project. This results in error-prone scenarios including product misplacement, stock replenishment, inappropriate handling, and counterfeiting. Because of this, real-time data collecting using developing technology like RFID (Radio Frequency Identification) is highly desired. In this paper, an Internet of Things (IoT)-based framework for managing and tracking construction materials is presented. It is equipped with a GSM-based GPS module and a number of sensors, including the DHT 11 sensor, and can effectively synthesize all dynamic data regarding the real-time location, ambient temperature, and humidity along the supply chain associated with various types of resources. The prototype of the system uses the aforementioned components to collect the real-time location data. The information gathered using this prototype model enables us to monitor the materials effectively from a distance and provides us with a viable method of deployment for related tasks.
Detection of Pathogens:A Comprehensive Study to Improve the Precision Agriculture
Authors: Shaista Farhat Et al.
Published: January 2024

For human livelihood, agriculture is an extremely important sector in Indian Agronomy. Environmental toxic farm impact affects all fields, making it difficult to manage numerous challenging situations. In order to get benefited of a crop for farmers and end user’s point of view, agriculture must adapt different technologies according to the day to day life environmental changes. Early identification of crop diseases will help farmers instead of entering into dangerous life threatening situations. For finding crop diseases along with close observation of a farmer, computer technologies will help a lot to maintain sustainable and healthy crop. Among several computing technologies, Deep Learning techniques create a major impact. In this paper we review various existing methods including machine learning, deep learning and AI for precision agriculture. The research insights provide understanding of state-of-the-art techniques, their limitations and the research gaps for further investigation towards precision agriculture.
Secure Framework for Cyber Data Using Cryptographic and Steganographic Algorithms
Authors: Bhawna Et al.
Published: January 2024

The e-commerce industry has recently seen enormous growth on a global scale. Due to the rising popularity of online shopping, consumers, businesses, and depository financial institutions are extremely worried about debit/credit card fraud and the protection of personal information. It is essential to prevent unwanted access to and use of the information since it is disseminated over insecure channels. Cryptography and steganography are most frequently employed to avoid unauthorized access to sensitive data. However, the combined qualities of these two approaches are not secure enough in the modern world. It might lead to some vulnerability. It is possible to add additional layers of protection and achieve high levels of information security if visual cryptography is used in conjunction with the abovementioned combination. This research work proposed a multi-level information security framework for cyber data using random public key cryptography for the secret text, color image steganography for concealing secret encrypted text in the cover image, visual cryptography for slicing the cover image into two shares, and image steganography again to hide both the shares into two color images, respectively. These security processes significantly increase the confidentiality, dependability, and efficiency of secret messages. While there isn't a parameter to demonstrate the level of security achieved using cutting-edge techniques, the accuracy of the received text data is calculated in terms of MSE and correlation coefficient by comparing the sent and received text data. To evaluate the effectiveness of the suggested strategy, the time required at the transmitter and receiver ends is also calculated. The MATLAB environment is utilized in the implementation, demonstrating that the suggested system has improved robustness when considering steganalysis.
A Novel Approach for Hand-written Digit Classification Using Deep Learning
Authors: Tamanna Sachdeva Et al.
Published: January 2024

Humans' control over technology is at an all-time high, with applications ranging from visual object recognition to the dubbing of dialogue into silent films. Using algorithms for deep learning and machine learning. Similarly, the most crucial technologies are text line recognition fields of study and development, with an increasing number of potential outcomes. Handwriting recognition (HWR), also identified as Handwriting Text Acknowledgment, is the capacity of a computer to understand legibly handwritten input from bases such as paper documents, screens, and other devices. Evidently, we have performed handwritten digit recognition using MNIST datasets and SVM, Multi-Layer Perceptron (MLP), and CNN models in this research. Our primary purpose is to compare the accuracy and execution times of the aforementioned models to determine the optimal model for digit recognition.
Real-Time Visual Inspection System for Identification of Fruits and Veggies Using Computer Vision
Authors: Mukesh Kumar Tripathi Et al.
Published: January 2024

This paper presents the framework for identifying fruits and veggies with a Fused descriptor-based approach by applying computer vision techniques. The construction of the proposed system is isolated into three phases: 1) Derivation, 2) extraction 3) portrayal. From the start, K-infers gathering techniques were done for establishment derivation. The subsequent step applies the variety, surface, and shape-based highlight extraction strategy. At last, A "consolidating" combination highlight is investigated with a C4.5, SVM, and KNN. In general, the acknowledgment framework creates a sufficient exhibition exactness with upsides of 97.89, 94.60, and 90.25 rates by using C4.5, SVM, and KNN separately. The trial and error bring up that the proposed combination plan can uphold precisely perceiving different soil products.
Feature Selection based Sentiment Analysis on US Airline Twitter Data
Authors: Shiramshetty Gouthami Et al.
Published: January 2024

Review document emotions are classified using sentiment analysis. Researchers grade features to remove non-informative and noisy attributes with low grades to improve classification accuracy. This paper utilizes six different NLP models to predict user sentiments based on Twitter reviews about airlines, using the Twitter US Airline Sentiment dataset. The best-performing models from both machine learning (K-nearest neighbor, Random forest, and Multinomial Naive Bayes) and deep learning (Artificial Neural Networks, LSTM, and Bidirectional LSTM with Glove embeddings) were implemented through Anaconda and Google Colab platforms. This paper introduces a new type of feature dimensionality technique termed "inquiry extension grade (IEG)," inspired by the inquiry extension term weighting technique. Additionally, we modified the traditional TF-IDF method, referred to as "improved TF-IIDF (IFFIDF)," specifically tailored for processing unbalanced text collections. To assess the effectiveness of the proposed methods, a series of simulations were conducted. The results indicate that the combination of IEG-ITFIDF Vectorization and Bi-LSTM with Glove embeddings yielded the best accuracy of 94.26% in sentiment classification for the Twitter US Airline Sentiment dataset.
Intelligent FMI-Reduct Ensemble Frame Work for Network Intrusion Detection System (NIDS)
Authors: Ch. Kodanda Ramu Et al.
Published: January 2024

The era of computer networks and information systems includes finance, transport, medicine, and education contains a lot of sensitive and confidential data. With the amount of confidential and sensitive data running over network applications are growing vulnerable to a variety of cyber threats. The manual monitoring of network connections and malicious activities is extremely difficult, leading to an increasing concern for malicious attacks on network-related systems. Network intrusion is an increasing issue in the virtual realm of the internet and computer networks that could harm the network structure in various ways, such as by altering system configurations and parameters. To address this issue, the creation of an efficient Network Intrusion Detection System (NID) that identifies malicious activities within a network has become necessary. The NID must regularly monitor network activities to detect malicious connections and help secure computer networks. The utilization of ML and mining of data approaches has proven to be beneficial in these types of scenarios. In this article, mutual a data-driven Fuzzy-Rough feature selection technique has been suggested to rank important features for the NIDS model to enforce cyber security attacks. The primary goal of the research is to classify potential attacks in high dimensional scenario, handling redundant and irrelevant features using proposed dimensionality reduction technique by combining Fuzzy and Rough set Theory techniques. The classical anomaly intrusion detection approaches that use individual classifiers Such as SVM, Decision Tree, Naive Bayes, k-Nearest Neighbor, and Multi Layer Perceptron are not enough to increase the effectiveness of detecting modern attacks. Hence, the suggested anomaly-based Network Intrusion Detection System named "FMI-Reduct based Ensemble Classifier" has been tested on highly imbalanced benchmark datasets, NSL_KDD and UNSW_NB15datasets of intrusion.