bluuebunny/crossref_metadata_2025 huggingface dataset:
Enterprise Comprehensive Budget Informatization Management Based on Cloud Accounting and Blockchain Technology
Authors: Liu Yiming
Published: January 2024

With the continuous expansion of enterprise scale and economic development, the traditional comprehensive budget management lacks strategic guidance and information communication is becoming increasingly prominent, making it difficult to adapt to the needs of enterprise development in the new era. In response to this, research is conducted on modeling time series algorithms based on autoregressive moving average models, and a prediction model based on backpropagation neural networks is also established. And the error variance weighted average method is used to organically integrate two single prediction models to obtain a combined prediction model. A combination forecasting model is utilized to predict and analyze the budget of G Company. The analysis results ia applied to budget management. Finally, based on cloud accounting and blockchain technology, the enterprise budget management is optimized, and a comprehensive budget information management model for enterprises is constructed based on cloud accounting and blockchain technology. The experimental findings denote that after the model is put into use, a total of three projects complete 101.1%, 102.3%, and 106.0% of the budget, all of which are able to achieve the budget goals. The model can effectively improve the accuracy of enterprise budgeting and the effectiveness of budget execution.
Sentiment Mining of Consumer Reviews: Evidence from Guangxi Fresh Fruit Supply on China's JD.com E-commerce Platform
Authors: Liurong Pan Et al.
Published: January 2024

This study identifies the product supply chain management issues based on significant data analysis methods by considering user evaluation data from JD.com e-commerce platform, a central e-commerce platform in China. Fresh fruit contributes to substantial sales in e-commerce platforms due to the particular characteristics of freshness. The quality perception of fresh fruits directly affects consumers' trust and willingness to repurchase. Therefore, studying the perceived value of online shopping for fresh fruit consumers in Guangxi, China, is significant to the operation and marketing of fresh fruit e-commerce. In view of these perspectives, we developed a user portrait model for fresh fruits in the Guangxi province based on three key dimensions: user information, fruit category details, and user evaluation information. Utilizing Rost CM software to analyze the content of user comments, including keyword word frequency analysis, semantic network analysis and consumers' shopping sentiment mining, so as to find out the existing problems, and provide decision-making reference for the improvement of the supply chain system of fresh fruits in Guangxi.
Comparative Effectiveness of Modified Agility Training and Perturbation Training in Osteoarthritis Knee: A Randomized Controlled Trial Investigating Pain, Functionality, and Joint Stability
Authors: Smita Patil Et al.
Published: January 2024

Introduction: As a modified hinge joint, the knee joint maintains static and dynamic body stability. The patellofemoral and tibiofemoral joints stabilize the knee by joining the femur, tibia, patella, and fibula. Ligamentum Patella and collateral ligaments support. Primary osteoarthritis (OA) is more common in women and thins knee cartilage. Secondary OA can occur from pathology or injury. Proprioception is impaired in knee OA, affecting balance and coordination. Physiotherapy like agility and perturbation exercises improves neuromuscular control. A knee OA study compares traditional physiotherapy with modified agility and perturbation training to improve treatment and symptoms. Understanding risk factors and symptoms helps prevention and treatment.

Material and Methods: Two groups of sixty osteoarthritis (OA) patients—20 men and 40 women—were formed. In addition to standard physiotherapy, Group B got modified agility and perturbation training. Group A received traditional physiotherapy. For three weeks, interventions were provided three times a week. VAS, TUG, and WOMAC were used to assess pain and function.

Result: According to statistical analysis, both groups' pain and functional outcomes improved dramatically. However, Group B—which got modified Agility and Perturbation training—performed better than Group A. The VAS, WOMAC, and TUG mean pre- and post-session values demonstrated significant pain reductions and functional improvements.

Conclusion: This study shows that modified Agility and Perturbation training is helpful for conservative knee OA treatment. Since it significantly reduced pain and improved functional results, the intervention showed promise as a knee OA treatment. More research is needed to determine the long-term effects and widespread use of this technique in various patient populations.
Optimizations Based Feature Selection Method for Disease Survival Prediction
Authors: Beschi I S Et al.
Published: January 2024

In the realm of survival prediction, identifying relevant features plays a pivotal role in enhancing model accuracy and interpretability. This research proposes a novel feature selection method that leverages the synergies between the Whale Optimization Algorithm (WOA) and Genetic Algorithm (GA) to optimize the selection process. The WOA, inspired by the social behavior of humpback whales, is employed to explore the solution space efficiently, while the GA, inspired by the process of natural selection, is used for refining and evolving potential feature subsets. The proposed hybrid algorithm, termed WOA-GA, introduces a dynamic framework that adaptively adjusts the exploration-exploitation trade-off during the search process. The WOA's exploration capabilities are harnessed in the early stages to efficiently traverse the solution space, while the GA's exploitation capabilities are employed later to fine-tune and evolve promising feature subsets. The synergistic combination of these two optimization techniques aims to mitigate the limitations of each individual algorithm and capitalize on their complementary strengths.
Circular Polarized Antenna for Radio Frequency Energy Harvesting Applications in the Smart Cities
Authors: Bujjibabu Nannepaga Et al.
Published: January 2024

Objectives: In this work, the Circularly Polarized (CP) broadband antenna with a simple monopole structure is proposed. Method: The antenna includes the rectangular patch antenna slotted with the four arcs in a circular shape and Top-right, and down-left vertices are removed to obtain the CP and to harvest power from the frequency 1.95-3.05 GHz, it covers the LTE 2100 GHz and Wi-Fi 2.45 GHz, bands. The defected ground structure is considered to design the broadband frequency. The antenna is excited using microstrip feeding. The proposed antenna is simulated, fabricated, and measured. The Dimensions of the antenna are 45 mm x 50 mm x 1.6 mm. Findings: The maximum gain obtained was 4.71dB at a frequency of 2.45 GHz with a Return loss of -43.21 dB, the axial ratio (AR) obtained 1.98dB (less than 3dB) in the same frequency. The impedance bandwidth of 59.8% and 44.89% simulated and measured, respectively, at 2.45 GHz, and VSWR is 0.128 and 1.142 at 2.45 GHz for the simulated and measured, respectively. Novelty: The designed antenna radiation Phenomenon resembles omnidirectional characteristics. Rectenna is designed at 2.45GHz and is connected to the antenna with the help of an impedance matching network. It is used to achieve the RF- DC power conversion efficiency of 84.52%.
A Novel Architecture of ADPLL Using Cordic Algorithm for Low-Frequency Application
Authors: Velamarthi Spandana, Et al.
Published: January 2024

All Digital Phase Locked Loop (ADPLL) has many applications in digital communication. It is difficult for low-frequency applications to achieve the lock state quickly. Therefore, proposed a novel particle swarm based ADPLL (PS-ADPLL) with Coordinate Rotation Digital Computer (CORDIC) algorithm to attain the lock state of the ADPLL for the low-frequency applications. In the proposed architecture, the D flip-flop matches the frequency and the phase of the output and reference pulses and produces an error signals up and down signal. The up/down counter removes the higher frequency part and produces a carry and the borrow signal. These carry and borrow signals are then fed into the increment decrement counters to produce the output signal matching the frequency of the reference signal. However, the time delay is increased for low-frequency applications, which is critical for the lock state. So, the delay line length is calculated by the CORDIC algorithm and is optimized by the particle swarm activated in the phase detector to match the output pulse with the reference pulse and make ADPLL into a locked state. The presented PS-ADPLL is tested in FPGA. Furthermore, the performance parameters are evaluated and compared with other current techniques to calculate the improvement score.
Efficient Record Management System for Higher Educational Institutions using TOE and UTAUT Model
Authors: K. Thilakavalli Et al.
Published: January 2024

Higher Educational Institutions maintain Academic Policy, Non Academic Policy, Students and Staff members’ details and financial records. Information retrieval from the existing records, Records maintaining process and decision making using previous history are crucial tasks in the Higher Educational Institutions. Even though, these process occupies many storage area, man power and Processing time etc., The objective of the proposed research is Academic and non academic activities record management, Academic Syllabus Generation like Outcome Based Education, Handling the students records, marks and attendance, Inventory system for the Institution and Laboratory, Efficient Library Management System, Maintaining the financial records like fees Collection, Financial Statements, Employee Payroll etc., The paper deals with the Academic and non-academic activities record management system by using the UTAUT and TOE Model
Proficient Approach for Intrusion Detection using Behaviour Profiling Algorithm and Prevention Using Statistical Model in Cloud Networks
Authors: D. Rajagopal Et al.
Published: January 2024

Objectives:

The objective of the paper is to discuss the proposed dynamic software model to detect and prevent intrusion in the cloud network.

Methods:

The Behavior Profiling Algorithm (BPA) has been used to detect the intrusion in cloud network. For finding the intruder in the network the Event Log Entries and the network Unique Identification Address (UIA) has been fetched from the server and then the collected attribute values have been transferred to prevention module.  In the prevention module the dynamic statistical approach model has been used to prevent the network systems and data which are available in the Cloud Network.

Findings:

For testing the proposed model the 100 cloud network systems were taken and based on the loss of packets (in MB) ranges the samples were classified as 0-100, 101-200, 201-300, 301-400, 401-500, 501-600, 601-700 respectively. The range of data loss is assumed to be an interval of 100 Mbps. It is assumed that the higher the data loss ranges, the more data is lost. The mean, variance, and standard deviation were calculated to verify the data loss ranges. The mean (average) of the data loss in the ranges 0-100 is 060.77 and the mean in the ranges 101-200 is 144.714 data losses, which gradually increases in proportion to the data loss ranges, and in the ranges 601-700 it is 665.769 data losses. From the statistical approach model, the differences between mean and variance indicated that the intruder attacked the files during the data transformation in the network. Therefore, the administrator has to monitor the warning message from the proposed IPS model and get data packet losses in the transformation. If the frequency of data loss is low, the administrator can assume that the data flow is low due to network problems. On the other hand, if the frequency of data loss in the network system is high, he can block the transformation and protect the data file. This paper concludes that the behavioral profiling algorithm combined with a statistical model achieves an efficiency of over 96% in wired networks, over 97.6% in wireless networks, and over 98.7% in cloud networks.

Novelty:

In the previous paper discussed the approach which has been implemented with 40 nodes and the result of the proposed algorithm produced above 90%, 96% and 98% in the wired, wireless and cloud network respectively. Now, the model has been implemented with 100 nodes the result has been increased. This study concluded that, the efficient algorithm to detect the intrusion is behaviour profiling algorithm, while join with the statistical approach model, it produces efficient result.
Predicting the Attacks in IoT Devices using DP Algorithm
Authors: P. Arul Et al.
Published: January 2024

The fundamental goal of this study is to predict cyber-attacks before they occur and to protect the network. Most existing attack detection algorithms cannot identify zero day attacks because they lack previously known data patterns to predict the threat, which is one of the biggest issues in the existing approaches. This research work offers a novel prediction method based on Gaussian regression that identifies cyber-attacks utilizing a unique dual data pattern categorization technique with no false positives. To improve the accuracy of the prediction and to reduce the prediction time consumption, this study introduces a dual prediction technique one locally – at the fog level where non-parametric input data is dealt with two functions namely quadratic & reliability function to ease the prediction and the other universally – cloud level where result of skill mechanism is carried out. Even if the local prediction misses an attack, the universal prediction sniffs it and protects the IoT devices and the data. A detailed comparison regarding accuracy and packet drop is carried out by simulating flooding attacks using on varying numbers of dummy nodes and the proposed system found to outscore the existing methods convincingly.
Optimizing Human Vitality - A Fuzzy Deep Learning Approach for Enhancing Organ Endurance in Healthcare
Authors: S. Senthil Kumar Et al.
Published: January 2024

The application of deep learning techniques in healthcare has shown promising results in improving patient outcomes. This study aims to optimize human vitality by enhancing organ endurance using a novel approach based on Fuzzy Variational Autoencoders (VAEs). Specifically, the focus is on diabetes and cardiac arrest datasets, two prevalent conditions that significantly impact organ function. The proposed framework leverages the power of deep learning and fuzzy logic to capture complex relationships and uncertainties inherent in healthcare data. By integrating fuzzy logic principles into the VAE architecture, the model can effectively handle imprecise and uncertain information associated with diabetes and cardiac arrest cases. The VAE framework is trained using a large dataset comprising medical records, clinical variables, and relevant biomarkers. Through an iterative training process, the Fuzzy VAE learns to encode the data of high-dimensional input into a latent space of lower-dimensional one while preserving the essential features and fuzzy relationships. Moreover, the enhanced organ endurance representations obtained from the Fuzzy VAE provide valuable insights into the underlying factors influencing the conditions, aiding in personalized treatment planning and decision-making. The results demonstrate that the Fuzzy VAE approach significantly improves the prediction accuracy and robustness compared to traditional deep learning models.
A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content
Authors: Vishwajit B. Gaikwad Et al.
Published: January 2024

A Systematic Review of Current Scenario of Diabetic Retinopathy Severity Grading using Image Content
Contour Fractal Dimension Analysis using Square-Box ROI Extraction Approach with Convolution Neural Network Classifier for Palmprint Recognition System
Authors: Krishnaveni K Abirami B,
Published: January 2024

Contour Fractal Dimension Analysis using Square-Box ROI Extraction Approach with Convolution Neural Network Classifier for Palmprint Recognition System (CFDCNNNet) is proposed. To bring about the originality, Contour Fractal Dimension (CFD) feature extraction approach and a Convolution Neural Network (CNNNet) classifier approach are employed. To impart the novelty the CFD feature extraction approach, Two Dimensional-Palmprint Region of Interest (2D-PROI) is captured from five different datasets using Square-Box ROI Extraction approach and point out all the edges/contours of 2D-PROI image (CPI) using Canny edge detection algorithm and then estimate the Fractal Dimension (FD) values using Box-Counting algorithm to create a distinctive feature vector. Classify this feature vector using Convolution Neural Network (CNNNet) classifier approach to identify the authorized person at a higher accuracy rate. This research explores on five different datasets such as CASIA, IITD, BMPD, SMPD and multi--spectral 2D-PROI image databases. The CFDCNNNet System model has been determined the authentication accuracy of different datasets with 98.66% of authentication accuracy.
Role of Biochemical Markers in Early Detection and Prognostication of Cancer Pathology
Authors: Govind Balde Et al.
Published: January 2024

Biochemical markers play a crucial role in cancer early detection and prognosis, and this study delves into the crucial field of cancer pathology. The study employs a multifaceted strategy by integrating a variety of datasets, including clinical, imaging, and transcriptomic data. To unravel the intricate relationships in the datasets, four leading algorithms—Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Gradient Boosting—are utilized, revealing the distinct advantages of each. The trials, driven by a fastidious preprocessing of information, reveal convincing bits of knowledge. When it comes to deciphering the complexities of transcriptomic data, SVM demonstrates exceptional accuracy (89 percent) and precision (91 percent). RF is adaptable, achieving precision of 93% and accuracy of 92% across a variety of data types. CNN, custom-made for picture examination, achieves an exemplary exactness of 91% and accuracy of 92%. The outfit learning approach of Inclination Helping yields vigorous outcomes, accomplishing an exactness of 90% and accuracy of 92%. Accuracy, precision, recall, and AUC-ROC comparisons highlight the nuances of each algorithm's strengths. SVM succeeds in high-layered transcriptomic information, RF features adaptability, CNN succeeds in picture examination, and Slope Supporting displays vigorous execution. This study contributes to the evolving landscape of cancer research by aligning its findings with related work and highlighting the requirement for tailored algorithm selection based on data characteristics.
Modelling the Process of Building Digital Security in Commodity Markets using Blockchain
Authors: Vivian Brian Lobo Et al.
Published: January 2024

Blockchain technology is acquiring momentous popularity in commodity markets over the past few years. A commodity can be regarded as an article of trade between an importer and exporter nation. Several types of commodities—such as agricultural (i.e., wheat, soyabean, corn, rice, spices, oil, meal products, sugar, dairy, marine, and plantation) and non-agricultural (i.e., base metals and petroleum products)—can be traded. The process of commodity trading or shipment between nations involves several actors like exporters, customs, importer, insurance, and importer bank and activities like bills of lading, letter of credit, factoring, exporting credit, and insurance—which are paper based, interdependent, and require human involvement. Blockchain, on the other hand, is a decentralized and distributed ledger technology that is open, immutable or tamperproof, efficient, verifiable, and permanent. The integration of blockchain in commodity markets can revolutionize the entire trading or shipment process. Considering the abovementioned problems, this study attempts to offer a solution by performing business process modelling in commodity markets using blockchain. The inferences made in this study prove to be quite promising wherein paper-based trading processes can be replaced with paperless trading processes via smart contracts—self-executable codes—which operate on top of blockchain, thereby eliminating intermediaries or middlemen and improving not only efficiency but also transparency of existing trading processes.
Application of Frequency Estimation Techniques to Minimize Power Quality Problem
Authors: Krutuja S. Gadgil Et al.
Published: January 2024

Through the use of a technique known as frequency estimation, the objective of this study is to explore a topic that pertains to power quality and the elimination of harmonics in the power system. Under a wide range of less-than-ideal conditions, such as voltage imbalance, harmonics, dc-offset, and so on, the objective of this research is to investigate the performance of a number of different methodologies for estimating phase and frequency. The bulk of the techniques have been shown to be incapable of estimating the frequency of the grid signals when the grid signals are characterized by dc-offset. This has been demonstrated via the use of a number of different methods. An approach to frequency estimation that is referred to as modified dual second order generalized (MDSOGI) is presented in this article. Under all of the circumstances that are not perfect, this technique makes an accurate prediction as to the frequency with which it occurs. It has been shown by experiments that the results are correct. It is necessary to further integrate the control scheme with the notion of instantaneous reactive power in order to construct a control scheme for a shunt active power filter that is capable of functioning under such conditions. A prototype for the experimental system is now being developed in order to show improved performance capacity.
Optimization Mobility in Sensor Actor Networks: A Comprehensive Framework
Authors: R. Ponnusamy M. Venkatalakshmi,
Published: January 2024

This work combines theoretical understanding with real-world applications to optimize mobility in sensor-actor networks. An extensive study of the literature examines the field for wireless sensor networks, including everything from sophisticated threat detection of mobile devices to methods for reducing congestion. With TinkerCAD for simulation & Arduino in hardware control, the suggested methodology emphasizes practicality and highlights servo motors as vital parts for actuation. This innovative method incorporates hardware control, providing a concrete connection between theoretical concepts and practical implementations. This methodology offers a comprehensive view of the operational elements of sensor-actor networks by addressing mobility optimization concerns in dynamic situations. The suggested model is demonstrated by Figures 1 through 5 and includes TinkerCAD visualizations, servo control of the motor, and an Arduino setup. The hardware specs and code samples that are provided show how mobility optimization is actually achieved in practice. The findings highlight the importance of real-world applications, improving the comprehension and suitability of sensor-actor systems in dynamic environments. This paper highlights the value of practical experimentation by providing a novel way for sensor-actor network mobility optimization. For those in the field looking to improve the efficiency and efficacy of dynamic networked systems, the suggested model is a useful tool.
Analysis of Touchless Mouse Technology for Physical Disabilities
Authors: Renu Ahlawat Et al.
Published: January 2024

We provide a touchless mouse system that surpasses past attempts by utilising deep learning models such as DenseNet169 and DenseNet201, in addition to an ensemble model. For feature extraction in the touchless mouse system, we use two different state-of-the-art convolutional neural network architectures, namely DenseNet169 and DenseNet201. These models, which were trained using massive datasets, perform remarkably well regarding computer vision tasks. Touchless mouse technology's sophisticated feature extraction capabilities make the exact recognition and interpretation of hand motions and movements possible. An ensemble model is developed by integrating the results of DenseNet169 and DenseNet201. This is done to make the system's performance even more effective. The ensemble technique improves the accuracy, stability, and generalizability of hand gesture detection by capitalising on these distinctions and using them to its advantage. Comparisons are made between the DenseNet169 and DenseNet201 models, the Ensemble model and several other deep learning and ensemble learning models. Additional deep learning and ensemble learning models are also displayed. The Ensemble model reached the maximum attainable accuracy of 99.62 per cent.
Quantum Blockchain: Unraveling the Potential of Quantum Cryptography for Distributed Ledgers
Authors: Kishor Madhukar Dhole Et. al
Published: January 2024

The examination investigates the joining of quantum-safe cryptographic calculations into blockchain innovation, zeroing in on grid-based cryptography and hash-based marks. Because of the inescapable danger presented by quantum processing, this study proposes a quantum-safe blockchain system intended to upgrade the security and flexibility of circulated records. The cross-section-based cryptography calculation uses the computational intricacy of grid issues, offering protection from quantum goes like Shor's calculation. Simultaneously, hash-based marks give lightweight and quantum-safe choices for advanced marks, supporting the general validity of blockchain exchanges. The examination includes a multi-staged approach, incorporating a complete writing survey, hypothetical system improvement, algorithmic execution, and exhaustive investigation of versatility, execution, and information security. Reproduction results will illuminate ensuing equipment executions, approving the down-to-earth attainability of the proposed quantum-safe blockchain. Besides, the review digs into moral and administrative contemplations, adding to the foundation of capable rules for quantum-safe blockchain innovation. Insights into the performance of lattice-based cryptography and hash-based signatures, as well as the provision of a blueprint for future research in quantum-resistant distributed ledger systems, are among the anticipated contributions. The powerful idea of quantum advancements and blockchain requires continuous investigation, and the exploration makes way for future examinations concerning quantum-safe agreement components, upgraded Quantum Key Dispersion, and interdisciplinary coordinated efforts.
EMAML: Design of an Efficient Ensemble Model for Detection of Adversarial Attacks in Machine Learning Environments
Authors: Chetan Patil Et al.
Published: January 2024

In the realm of cybersecurity, the escalating sophistication of adversarial attacks poses a significant threat, particularly in the context of machine learning models. Traditional defensive mechanisms often fall short in identifying and mitigating such attacks, primarily due to their static nature and inability to adapt to the evolving strategies of adversaries. This limitation underscores the necessity for more dynamic and responsive approaches. Addressing this critical gap, our research introduces an innovative Active Machine Learning Adversarial Attack Detection framework process. Central to our approach is the strategic amalgamation of data collection and preprocessing techniques. We meticulously gather a diverse dataset encompassing both genuine and adversarial user feedback, which is then carefully annotated to differentiate between the two scenarios. This data undergoes rigorous preprocessing, including tokenization and conversion into numerical features through methods like TF-IDF and word embeddings, paving the way for more nuanced analysis. The core of our model employs a variety of machine learning algorithms—Logistic Regression, Random Forest, SVM, CNN, and XGBoost—each fine-tuned through meticulous hyperparameter optimizations. The novelty of our approach, however, lies in the integration of an active learning strategy for efficient results. By employing uncertainty sampling and query-by-committee, our model actively identifies and learns from instances of highest informational value, continuously evolving in its detection capabilities. Our framework further stands out in its post-training phases. The models are not only retrained with newly labeled data but are also subjected to a comprehensive evaluation on separate test datasets. Metrics such as accuracy, precision, recall, F1-score, and AUC are meticulously computed, ensuring the robustness of our results. Deployed in a real-time environment, the model demonstrates remarkable efficacy in detecting adversarial attacks in user feedback. Continuous monitoring and periodic retraining allow the model to adapt and respond to new adversarial tactics. The impact of our work is quantitatively significant—our model outperforms existing methods with a 9.5% improvement in precision, 8.5% higher accuracy, 8.3% increased recall, 9.4% greater AUC, 4.5% higher specificity, and a 2.9% reduction in detection delays for different scenarios.
Advancements in Otology: Hearing Restoration and Cochlear Implant Innovations
Authors: Vikas S. Kulkarni Et al.
Published: January 2024

Otology has made significant strides in recent years, most notably in the development of cochlear implants and the restoration of hearing. This thorough analysis looks at the most recent advancements in cochlear implant technology, treatment modalities, regenerative medicine, auditory rehabilitation techniques, and future prospects in otology. A variety of therapies are included in the field of audiology rehabilitation, such as digital signal processing in sophisticated hearing aids, assistive listening devices, and transformational cochlear implants. Cochlear implant innovations include improved electrode arrays, signal processing algorithms, and brain interface technologies that greatly increase patients' ability to perceive speech and enjoy music. Stem cell therapy, tissue engineering, and gene editing techniques are promising avenues for rebuilding damaged auditory components in regenerative medicine, which may lead to eventual restoration of hearing. Therapeutic options include gene-based medicines, pharmaceutical interventions that target certain biological pathways, and personalised medication plans that are customised to each patient's unique auditory profile. Emerging technology, multidisciplinary cooperation, a better comprehension of auditory difficulties, and the creation of biocompatible materials for implant technologies are critical to the future of otology. On the other hand, obstacles include the need to translate research into clinical practice, accessibility inequities, and regulatory issues. This review emphasises the need to solve enduring issues in the area of otology while also highlighting the possibility for revolutionary advances in the field.
Cutaneous Immunology: Novel Therapeutic Approaches for Skin Disorders
Authors: Balkrishna Nikam Et al.
Published: January 2024

When it comes to comprehending and treating different skin conditions with focused therapeutic approaches, cutaneous immunology plays a crucial role. The complex immunological processes causing vitiligo, atopic dermatitis, psoriasis, and bullous pemphigoid are examined in this study. Immunotherapy has completely changed the paradigms of dermatological treatment, including targeted biologics, small compounds, and customised methods. Targeted immunotherapies are important because of the immunological foundation of many illnesses, which are characterised by dysregulated immune responses and cytokine-mediated pathways. In psoriasis, biologics that target TNF-?, IL-17, and IL-23 are very effective in reducing inflammation and stopping the disease's development. In a similar vein, medications such as dupilumab regulate allergic reactions and show promise in treating atopic dermatitis. New treatments that target immune regulation and tolerance induction have promise for treating bullous pemphigoid and vitiligo. However, issues with patient selection, immunological diversity, accessibility, and long-term safety continue to exist. Innovative drug delivery methods, combination therapy, precision medicine, and enhanced target identification are the main focuses of future developments. The incorporation of cutaneous immunology into clinical practice is a revolutionary strategy that prioritises the needs of the patient, improves therapy effectiveness, and transforms patient care. The clinical implications, difficulties, and prospects for using cutaneous immunology's promise for the efficient treatment of skin conditions are explained in this study.
Hybrid Ensemble Stacking Techniques for Coronary Artery Disease Prediction Using Machine Learning Algorithms
Authors: CMM. Mansoor Et al.
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.
Solution of First-Order Differential Equation Using Fourth-Order Runge-Kutta Approachand Adams Bashforth Methods
Authors: Salisu Ibrahim Et al.
Published: January 2024

In this research, we investigate the solution of first-order differential equations(DEs) using Runge- Kutta fourth-order method (RKM) and Adams-Bashforth methods (ABMs). In this work we considerfourth-order RKM and ABMs for solving first order DEs. The method proof to be simple, easy, accurate and efficient technique for solving first order DEs. Moreover, there are unlimited application of fourth-order RK4 and ABMs for solving first-order DE in science, engineering, economics, social science, biology and business. These play an important role in science and engineering. Some examples are giving and solved to support the efficiency of our methods which are demonstrated by figures.
Chinese Remainder Theorem based Performance Analysis of RSA Cryptosystem
Authors: Yogesh Suryawanshi Et al.
Published: January 2024

Message security and authenticity is a very important issue which cannot neglect in wireless network. This paper explains how the RSA algorithm can be used to achieve both, over a wireless network. The RSA-CRT technique was premeditated for data decryption and operative illustration of cryptograpghy using the Chinese Remainder Theorem (CRT) for message security which is nearly four times faster.
Characterization of Bismuth Titanate by Line Profile Analysis
Authors: Anchana Joseph Reenu Jacob,
Published: January 2024

Bismuth Titanate (BIT), Bi4Ti3O12, was synthesized using the solid-state reaction process from the oxide mixture of Bi2O3CO3 and TiO2. The characterization analysis of the prepared sample was done by X-ray diffraction technique. The X-ray diffraction data revealed an orthorhombic perovskite phase that was clearly explained. The Scherrer method, modified W-H analysis, and the SSP studies were conducted to figure out the crystallite size and micro strain in Bi4Ti3O12. Morphological studies included SEM and TEM analysis.  FTIR spectral investigation supports the M-O coordination of the synthesized Bi4Ti3O12 sample. The phase formation patterns of Bi4Ti3O12 determined by different methods are highly connected.
GEO based Adaptive Dynamic Key Generation Scheme for Speck-R in IoT Applications
Authors: M. Abinaya Et al.
Published: January 2024

By integrating lightweight properties with lightweight cryptography in Intenet of Things (IoT) applications, information confidentiality and dependability can be guaranteed. In order to cut down on the number of rounds and speed up execution, a dynamic key-substitution layer was included to the original Speck's structure to create the ultra-lightweight encryption algorithm Speck-R.In this paper, we propose to design Golden Eagle Optimizer (GEO) based adaptive dynamic key generation scheme for Speck-R. In this algorithm, the block size and key size are adaptively chosen based on the IoT application and data size. Then the dynamic key (DK) is generated using GEO algorithm in which a fitness function is derived in terms of the metrics LPF, DPF and BIC.  Then the key with optimum fitness is chosen as the dynamic key for further encryption /decryption process of Speck-R. By simulation results, we show that the proposed technique enhances the security.
Energy Aware Clustering System for Wireless Sensor Networks utilizing Rider Sunflower Optimization Approach
Authors: Sambu Anitha Et al.
Published: January 2024

Wireless Sensor Networks (WSN) are spatially disseminated sensors that are utilized for monitoring physical or environmental factors, like sound, temperature, pressure, and so on, to collectively drive their information from the networking to the base station. The WSN is composed of hundreds or thousands, where all the nodes are interconnected with other Sensor Node (SN). Clustering is the most popular topology management technique in WSN, grouping nodes to manage them or execute different tasks in a distributed manner, like resource management. It includes grouping sensors and selecting Cluster Heads (CHs) for every cluster. Therefore, this study presents a new Rider Sunflower Optimizing Model-Based Energy Aware Clustering Approach (RSFOA-EACA) for WSNs. The prime goal of the RSFOA-EACA technique is in the optimum selection of CH for data transmission in the WSN. With Rider Optimization Algorithm (ROA) and Sunflower Optimization (SFO) incorporation, the RSFOA-EACA technique mainly depends upon the RSFOA. Furthermore, the RSFOA-EACA algorithm derives a Fitness Function (FF) by the computation of distance, Residual Energy (RE), Node Degree (ND), and network coverage. The CH selecting enables proper inter-cluster transmission in the network. The experimental analysis of the RSFOA-EACA method is investigated by implementing a sequence of simulations. The simulation values emphasized the promising energy efficiency outcomes of the RSFOA-EACA approach.
A Novel Context Aware Privacy Provision Algorithm for Hand Held Devices
Authors: Et al. Thyagaraju G S
Published: January 2024

Context-awareness is an emerging area in the current technical world as it adapts mechanisms and interfaces of applications based on consumer preferences as well as the environmental conditions. Various applications of Internet of Things can be perceived in the fields such as but not limited to agriculture, Industry, Medical and Healthcare, Smart Home Appliances, etc. Since last couple of decades use of mobile devices have grown tremendously everywhere in this world. Adding computing capabilities has increased their application to many folds and has made task as well as life simpler than earlier days. These smart-phone embedded with computing features have become an integral part of daily life of people globally. Unprecedented rates of growth were noticed in use of mobile applications on these handheld devices. But adding to the wows, subscribers are usually requested to provide personal and context data, while making use of these mobile utilities. The permission have to be given to access the user location, the contact details as well as the gallery of the mobile storage whenever the new application have to be installed. Due to these conditions applied by the application providers the privacy concern came into being for security experts and organizations. In order to render better services to the users, privacy is an important concern employed. The privacy is enhanced in the context aware applications through the proposed encryption strategies to offer efficient services. In the proposed approach, an active encryption strategies are employed for enhancing the network security.
Skin Cancer Prediction using Convolutional Neural Network
Authors: L.Sherin Beevi Et al.
Published: January 2024

Skin cancer is a highly death-dealing and life-threatening disease. It mostly appears in the form of melanoma (malignant) and benign tumours; diagnosing these in the early stages necessitates the use of an efficient algorithm that can be predicted employing an enormous data set that has been trained. The goal of this study is to predict skin cancer using Keras classification model and CNN which uses machine learning algorithms to accurately diagnose and predict the type of skin cancer. We are utilising an optimizer defined for expressing the loss and hyper parameters that have a substantial impact on the model's performance. Our Results show that the suggested approach performs better than the other options, with an accuracy of around 92%.Therefore as an outcome, the goal of this paper is to develop an accurate Keras classification model and CNN model with Optimizers to detect skin cancer with greater than 80% accuracy and a false predictively rate of less than 10%, as well as to visualize skin lesion images from the ISIC dataset.
Analyze Predict and Classify Water Quality and Usage of Water using Machine Learning Techniques
Authors: Roshini L Et al.
Published: January 2024

As important as water is for humans, it is also crucial for all livestock and crops. Direct groundwater consumption by crops and livestock can affect both, perhaps causing crop failure or sickness in livestock if the quality is subpar or becomes unusable. Knowing whether the groundwater is usable will allow for the proper usage of the water. Specific crops that can survive that water quality can be grown by farmers. The main objective of this paper is to determine where and how the water can be used while also classifying the water's quality into one of several classifications. data is collected from open source of Telangana ground water quality data 2020. Water quality is identified and assessed with it target parameters as WQI, Classification 1, RSC, TDS, classification. WQI gives the one value for n number of parameters the water and its usage is assessed with its grades as good, moderate, very good and poor whereas Classification 1 is assessed with 2 values as mineral rich MR and poor safe PS, RSC When RSC usage surpasses the permitted limit, irrigation suffers (>2.5). TDS as the target variable assess the salinity of water which assessed with grades. Classification is assessed with 9 types of parameters.
Subgraph Anomaly Detection in Social Networks using Clustering-Based Deep Autoencoders
Authors: Yallamanda Rajesh Babu Et al.
Published: January 2024

Social networks are becoming more prevalent all across the globe. With all of its advantages, criminality and fraudulent conduct in this medium are on the rise. As a result, there is an urgent need to detect abnormalities in these networks before they do substantial harm. Traditional Non-Deep Learning (NDL) approaches fails to perform effectively when the size and scope of real-world social networks increase. As a result, DL techniques for anomaly detection in social networks are required. Several studies have been conducted using DL on node and edge anomaly detection. However, in the current scenario, subgraph anomaly detection utilizing Deep Learning (DL) is still in its nascent stages. This paper proposes a method called Clustering-based Deep Autoencoders (CDA) to detect subgraph anomalies in static attributed social networks. It converts the input graph into node embeddings using an encoder, clusters these nodes into communities or subgraphs, and then finds anomalies among these subgraph embeddings. The model is tested on seven open-access social network datasets, and the findings indicate that the proposed model detects the most anomalies. In the future, it is also recommended that the present experiment be aimed at dynamic social networks.
Novel Classification and Prediction of Heart Disease using CDMA Algorithm
Authors: Tintu George Et al.
Published: January 2024

Chronic illness is a long-term condition that lasts a lifetime. In most cases, immunizations and medications cannot heal them, or they do not work.  The most common chronic illnesses are heart disease. The first step in stopping the progression of these disorders is patient diagnosis and prognosis. The identification of individuals with heart disease may be made easier with the machine learning (ML) and deep learning (DL). Finding people who is at risk for these well-known illnesses is often influenced by a variety of circumstances. High precision is provided by deep learning. Machine learning, however, provides less precision. Deep learning also needs a lot of data. However, machine learning can be trained on less data. By doing so, we may determine that one technique's flaw is fixed by another. To classify and forecast heart disease, this research developed an algorithm by combining ML and DL algorithm that is Combination of Machine Learning and Deep Learning Algorithm (CMDA). The data set for the work was taken from UCI data repository. The CMDA algorithm uses the Dl4jMlpClassifier and the Support Vector Machine (SVM). The technique like stacking classifier is used to integrate above two algorithms in the CMDA. The classification method utilized Naive Bayes as a meta-classifier in the CMDA algorithm, uses a stacking classifier strategy for final prediction. After prediction finally, the CMDA method utilizes the Min-Max normalization approach to determine risk factor. According to the experimental findings, the proposed CMDA algorithm effectively classifies and forecasts heart disease and produce high results while comparing with existing methods.