bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning-Driven Cryptography Automating the Design of Robust Encryption Algorithms
Authors: Subhashini Peneti
Published: November 2024

By automating the creation of strong encryption algorithms, the application of machine learning (ML) to cryptography offers a revolutionary way to improve data security. In order to find weaknesses and improve cryptography systems—thereby enabling quicker, more effective encryption mechanisms—this research investigates the application of diverse machine learning approaches. Our goal is to create powerful encryption systems that can withstand more complex dangers, such as hazards associated with quantum computing and sophisticated cyberattacks, by utilizing algorithms that can evaluate patterns within large datasets. The equilibrium between algorithmic performance and cryptographic security is also evaluated in this work to guarantee that solutions maintain their efficacy and efficiency. Furthermore, we emphasize responsible AI methods in cryptographic applications, which addresses ethical problems. The ultimate goal of this research is to advance the rapidly expanding field of AI-driven cryptography by offering a foundation for upcoming developments that will greatly increase the security of private data against illegal access.
Quantum Computing in Electronics: A New Era of Ultra-Fast Processing
Authors: Surendar Aravindhan
Published: November 2024

Quantum computing has been predicted to radically change the electronics industry, especially by implementing ultra-fast processing. Different from other types of computers, quantum computers use qubits instead of binary bits since they can be in multiple states at once because of superposition and entanglement. This creates the ability to perform various calculations simultaneously thus introducing exponential increases in the processing capability hence the efficiency of quantum computers. The applicability of the findings to electronics is extensive since it can be applied in cryptography, artificial intelligence, and analysis of other complex data. Also, it is necessary to point out the potential for improving efficiency in the use of energy in computing systems due to progress in the area of quantum computing and the possible extension of prospects for miniaturization and the search for new materials. Though there are serious problems that are still unsolved, including qubit stability and error correction, continuous research and development works have the potential to solve those issues, and thus bring about a revolutionary change in the electronics of the future. In this paper, an evaluation of quantum computing in electronics is presented, with a focus on the effects that will be brought about and the relevant fields. This paper aims at giving an evaluation of quantum computing in electronics the effects that will be realized and the fields that will be impacted.
New algebraic structures such as q-rung interval-valued intuitionistic fuzzy set applied to logarithm operator and its extension
Authors: G. Shanmugam
Published: November 2024

We introduce the interval-valued intuitionistic fuzzy set used with the q-rung logarithimic operator (q-LIVIFS). A q-rung interval-valued intuitionistic fuzzy set may be developed by expanding the Pythagorean interval-valued fuzzy set (PIVFS). We discuss the q-logarithimic operator applied to interval-valued intuitionistic fuzzy weighted averaging (q-LIVIFWA), interval-valued intuitionistic fuzzy weighted geometric (q-LIVIFWG), extended q-logarithimic operator applied to interval-valued intuitionistic fuzzy weighted averaging (q-ELIVIFWA), and extended q-logarithimic operator applied to interval-valued intuitionistic fuzzy weighted geometric (q-ELIVIFWG). Several algebraic properties of q-LIVIFSs, such as distributivity, idempotency, and associativity, have been identified.
Modeling GHQ Total and SDQ Difficulty Score to Extract Incomplete Information on One Using the Other
Authors: Alka Sabharwal
Published: November 2024

Background: A natural approach to analyze multidimensional data is use of multivariate statistical analysis. If the number of dimensions/variables is small and variables are correlated, Multivariate Normal Distribution (MVN) is applied frequently. If the distribution of underlying variables is not normal, transformations are applied to convert them to normal variables.
Objective: Many a times, information may be missing on one or more dimensions of the data. The study intended to estimate the missing information through the available information.
Method: In a series of three independent surveys to examine the psychological health of young adults during COVID-19 period, enrolled in higher educational institutions in India, Strength and Difficulty Questionnaire (SDQ) 17+ extended version was used. In addition, General Health Questionnaire (GHQ) was used in third survey. The data was divided into two datasets; based on third survey and based on first two surveys. MVN was used to estimate GHQ scores through difficulty score dimension of SDQ and vice versa. The model was applied to data of first two surveys to estimate GHQ scores at the time of these surveys. The model was applied on 162 respondents who were common in all the three surveys.
Result: The estimated values for the third survey data were consistent with the observed and simulated values. Further it was found that out of 64 respondents with high GHQ scores in third survey, 55 had it during first two surveys also.
Conclusion: The results can be extended to estimate any missing information whenever variables are correlated.
Mathematical Models for Infectious Disease Dynamics and Control Strategies
Authors: Vinayak Kishan Nirmale
Published: November 2024

The study explores mathematical models related to the dynamics of infectious diseases and approaches to manage them. These models are essential resources for comprehending how illnesses spread throughout people and assessing how well control strategies work. The study of infectious disease epidemiology heavily relies on mathematical modeling and analysis. Here, we give a clear overview of the spread of disease, explain how to mathematically model this stochastic process, and show how to utilize this mathematical representation to analyze the emergent dynamics of real-world epidemics. The advancement of mathematical analysis and modeling is crucial to our expanding comprehension of the evolution and ecology of pathogens. This study emphasizes how important mathematical modeling is to improving our knowledge of the dynamics of infectious diseases and how crucial it is to creating efficient management plans to lessen the burden of infectious diseases on public health.
Leveraging Flask API and Machine Learning to Forecast Multiple Diseases
Authors: K Venkateswara Rao
Published: November 2024

The study described in this abstract used machine learning to predict several diseases, and it integrated the model into a Flask API. A user-friendly platform for disease prediction based on patient symptoms and medical history was the goal of the paper. A number of strategies were used to optimize the predictive performance once the machine learning model had been trained on a sizable dataset of patient records. The model was made accessible as a web service through the usage of the Flask API, enabling simple deployment and integration into current healthcare systems. The study's findings demonstrated that the model could accurately forecast diseases with a high degree of precision, and the Flask API gave healthcare professionals an easy way to use the model in real-world settings. The study underscores the significance of developing open and user-friendly platforms for healthcare applications and shows the potential of machine learning and online APIs in improving disease detection. Additionally, the study discovered that the model could correctly identify disorders even in the absence of conclusive diagnostic tests.
Integrating Diverse Data Streams for Enhanced Emotional Intelligence in Mental Health Care
Authors: Asha Priyadarshini.M
Published: November 2024

This research presents a comprehensive system for real-time emotion recognition and analysis using multimodal data, including image, video, audio, and text. The system employs deep learning models to extract features and classify emotions from each modality. By integrating these predictions, we aim to provide a holistic understanding of a user's emotional state and assess potential risks. The system further analyzes the collected emotion data to identify trends, patterns, and indicators of emotional well-being and suicide risk. Visualizations such as time-series plots, distribution charts, and stacked area charts are employed to represent the emotional dynamics over time.Based on the analysis; the system generates personalized recommendations and alerts for users exhibiting signs of distress or emotional instability, including potential suicide risk factors. The goal is to promote proactive well-being management and early intervention. It is crucial to emphasize that this system is a tool for early detection and should be used in conjunction with professional mental health care.
The Development of IV-VIKOR Method Under Hesitant Fuzzy Sets in MCDM
Authors: Rafi Raza
Published: November 2024

Due to its solution correctness and ease of evaluation, the VIKOR approach has already established itself as a popular multi-criterion decision-making (MCDM) method. This strategy focuses on picking and ranking from a list of viable options in order to assist the decision-maker in determining a final course of action. Additionally, it looks for a middle ground to resolve a problem with competing demands. It produces a ranking list of compromises based on a precise measurement of distance from the ideal response. The hesitant fuzzy set (HFS) has furthermore come under increased study because of its potency and ability in capturing ambiguity and uncertainty. This paper introduces the inversion of VIKOR method (IV-VIKOR) to take uncertain fuzzy conditions into account. These novel concepts serve as the foundation for an IV-VIKOR method that we suggest, and a real-world application is given to demonstrate how well our method works when dealing with MCDM problem with hesitant fuzzy preference data.
Predicting Disasters: A Machine Learning Approach
Authors: Rayavarapu Veeranjaneyulu
Published: November 2024

Trends in river water levels that could cause floods are an intriguing and applicable subject of study. They set up shop to lessen the financial and social impact of floods. Water level variations can be predicted using support Vector, a class of machine learning methods that can also be used to detect the likelihood of flooding. The two algorithms use comparable hydrological and flood resource variables in the process of predicting floods, such as the prediction amount, River inflow, seasonal flow, flood frequency, and other pertinent flood prediction variables, because the water level is the most important factor in hydrological research. Machine learning techniques are helpful for predicting floods because they have the ability to classify and regress data from several sources into groups of flood and non-flood. Anyone may use the Machine Learning and Deep Learning features of artificial intelligence thanks to Watson Studio.
Applications of Fuzzy Soft Digraphs in Several Domains
Authors: Honnegowda C K
Published: November 2024

Throughout this exploration project, the idea of intuitionistic fluffy delicate sets is coordinated with charts, which at last prompts the improvement of another structure for the organization of intuitionistic fluffy delicate data frameworks. It is feasible to deal with a wide assortment of data in a clearer way by utilizing this system, which makes it conceivable to do as such. The idea of utilizing explicit sorts of intuitionistic fluffy delicate diagrams is presented for the absolute first time in this article. occurrences of diagrams that are remembered for this classification are emphatically edge sporadic intuitionistic fluffy delicate charts and friendly edge standard intuitionistic fluffy delicate diagrams. Both of these charts are cases of something that can be tracked down in this class. With regards to fluffy delicate charts, every one of these classifications has occasions of its particular kinds. To accomplish the target of giving a clarification to these stand-out ideas, we utilize various models, and we additionally explore a portion of the qualities that are straightforwardly related with them. As a feature of the extent of our examination, we expect to apply intuitionistic fluffy delicate diagram to an issue that includes independent direction. our is something that we intend to do. Likewise, we depict our strategies as a calculation that is utilized in this application's execution. Moreover, we guarantee that this data is given.
Advancing Disc-Based Pythagorean Fuzzy Sets with Distinct Radii
Authors: Vinoth Kumar V
Published: November 2024

Disc-Based Pythagorean Fuzzy Sets (D-PFSs) extend traditional fuzzy sets by incorporating the concept of distinct radii to model uncertainty more flexibly. Unlike classical fuzzy sets that use fixed radii, D-PFSs allow each element to have its own radius, which enhances the representation of varying degrees of uncertainty. In implementing D-PFSs, a systematic approach is crucial for effective uncertainty modeling. Each D-PFS element is defined by membership and non-membership degrees, and a distinct radius. The Pythagorean condition ensures that the sum of the squares of membership and non-membership degrees does not exceed the element's radius squared. Union and intersection operations involve combining degrees with specific formulas, where the union uses the minimum radius and the intersection uses the maximum radius of the involved sets. The complement operation swaps the membership and non-membership degrees while retaining the radius. Integrating D-PFSs into the TOPSIS method for multi-criteria decision-making involves replacing decision matrix elements with Pythagorean fuzzy numbers, normalizing these numbers, and calculating ideal solutions and distances using Euclidean measures. Alternatives are then ranked based on their proximity to ideal solutions. The results demonstrate that D-PFSs provide enhanced flexibility by accommodating distinct radii, allowing for a nuanced representation of uncertainty. Validation confirms adherence to the Pythagorean condition, and the varying radii effectively influence the degrees. The comparison of union and intersection operations further showcases D-PFSs' superior capability in managing complex uncertainties.
Impact of Expert Sessions and Industry Talks on Undergraduate Learning and Development: A Kirkpatrick Model Evaluation
Authors: Prerna Srivastava
Published: November 2024

Aim: This study investigates the impact of expert sessions and Industry talks on enhancing undergraduate university student leaning outcomes, professional development and awareness about industry requirement which pave way for bridging academic knowledge with real world insights.
Methods: A mixed-methods approach was used, integrating quantitative and qualitative opinions from over 250 students in various disciplines at Undergraduate level. Patterns and correlation were explored by analysing the quantitative data using Statistical Package for Social Sciences (SPSS). The effectiveness of the training sessions was measured at four levels i.e. Reaction, Learning, Behaviour and Results as per the Kirkpatrick Model of Training Evaluation.
Results: Observational results were suggestive of a positive correlation between session attendance and increased industry awareness. Students indicated that they felt more comfortable applying theoretical material to real-world situations. The Kirkpatrick Model assessment showed results of increased engagement and satisfaction, transferable skills development from the academic setting to real-world application for professional engineering practice upon graduation.
Conclusions: The study concludes that expert sessions make students aware of the industry and their application is beneficial in practical knowledge. These findings provide a basis for academics and administrators looking to incorporate expert interventions within higher education in order to enhance curriculum quality. This study is a detailed assessment of expert sessions and Industry talks in higher education, employing both student perspectives along with an evaluative model focusing their impact on professional development down to the learning outcomes.
A New Subclass of Univalent Functions Defined by Raducanu-Orhan Linear Differential Operator
Authors: Thirucheran M
Published: November 2024

The univalent function is incredibly exciting, as evidenced by the large number of new studies that have been written about it recently. As injective analytic functions, univalent functions do not take the same value at different points inside their domain. Univalent functions are widely used in several branches of mathematics, physics, and engineering and are particularly significant in complicated analysis. Operators of normalized analytic functions are in considerable demand these days, especially differential and integral operators. There are many mathematical and scientific applications for operators. These operators, which are also employed to explain a wide range of physical processes, can be utilized to solve differential equations. A significant amount of material has been studied and debated by numerous researchers for the operators. In this study, the Raducanu-Orhan differential operator defines the new subclass of univalent functions. Furthermore, the subclass’s Fekte-Szego inequality, extreme points, integral means of inequalities, and coefficient inequality have been determined.
Two-Stage Robust Optimization for Multiple Microgrids with Varying Loads under Uncertainty Conditions
Authors: Vijayalaxmi biradar
Published: November 2024

The volatility in electricity consumption caused by the growing integration of Electric Vehicles (EV) might significantly impact the reliability of microgrids. Hence, this study aims to decrease the operational expenses of multi-microgrids and enhance the efficiency of solution algorithms for their enhanced electric power distribution. This is achieved by introducing an innovative two-stage robust optimization dispatch system that considers the uncertainties related to loads, renewable energy sources, and electric vehicle consumption.  The multi-microgrid layer adjusts the limitations based on the number of EVsadded in real-time and manages various energy units to achieve the minimum operational expenses in the most unfavorable situations. The EVAggregator (EVA) layer ensures minimal power outages and optimal charging operations by regulating charging power while preventing safety breaches. The enhanced uncertainty set is derived from a neural network that undergoes training using a substantial amount of past information, thereby eliminating unrealistic worst-case situations. The suggested method effectively captures the system's features, allowing for the substitution of a considerable amount of past information with system attributeswhen creating the uncertainty set. This approach ensures both high dependability and a substantial decrease in convergence time.
Application of Nexus Learning framework for Personalized Medicine and Drug Development
Authors: F Rahman
Published: November 2024

Biological systems provide an enormous amount of knowledge on development and disease. The pharmaceutical industry is facing new possibilities and challenges with the rise of high-throughput methods to study disease and biology. The aim is to develop drugs based on acceptable therapeutic assumptions. Nexus Learning provides a variety of tools that help improve discovery and decision making using extensive, high-quality data and well-specified queries. Potential applications of NLF can be found across the whole drug development process. Clinical study target validation, prognostic biomarker discovery, and digital pathology data processing are a few examples. There has been a wide variety of applications in terms of technique and environment, with some methods producing reliable forecasts and insights. The primary problem with using NLF is that the findings provided by NL tend to be easy to understand or reproduce, which can limit their value. The collection of complete and methodical high-dimensional data remains an essential need across all domains. By addressing these challenges and increasing understanding about what is required to verify NLF methodologies, we can employ NLF to make data-driven decisions, which can speed up drug discovery and development and minimize failure rates.
Blockchain-Based Intelligent Inventory Management for Hospital Supply Chain (HSC) with Deep Learning Techniques
Authors: Nidhi Mishra
Published: November 2024

A medication shortage may have far-reaching consequences, making it an important inventory management concern for healthcare organizations. Intelligent demand forecasting is essential to make cold chain businesses more competitive and save transportation and storage costs. Hence, this paper proposes a blockchain-based intelligent inventory management model (BC-IIMM) with deep learning techniques for hospitals to prevent running out of supplies, run their systems sustainably, and optimize their profits in the hospital supply chain (HSC).Preventing the contamination of pharmaceuticals requires a pharmaceutical cold chain monitoring system that uses IoT devices, blockchain, and cloud computing to track items across their entire supply chain reliably. One other perk is that both doctors and patients may benefit from drug recommendations made by a deep belief network (DBN) model. Further, to construct deep learning (DL), the proposed cold chain monitoring system produces large-scale, high-quality data. Holding cold chain items would become less expensive and cold chain inventory management decisions would be easier with it.Next, they provide a numerical result proving the suggested method works better than the baselines regarding refilling cost and shortage rate.
E-Healthcare System for Stroke Management Using IoT and Machine Learning
Authors: Chandni Sawlani
Published: November 2024

Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.
Cutting-Edge Deep Learning Strategies for Precise Ovarian Disease Detection: A Comprehensive Review
Authors: Soumya Koshy
Published: November 2024

Accurate and timely diagnosis of ovary-related diseases is of paramount importance in the realm of medical imaging. Utilizing deep learning technology has emerged as a viable method to improve the precision and effectiveness of medical image segmentation, which directly impacts the detection of ovarian diseases. Ovarian disease detection through medical image segmentation is critical for early diagnosis and effective treatment. This study evaluates cutting-edge deep learning strategies using the MMOTU ovarian tumour ultrasound dataset, which includes OTU 2D and OTU CEUS subsets. We implemented various models, including U-Net, its variants with ResNet and DenseNet backbones, CR-Unet, and Ocys-Net, along with ensemble learning and transfer learning techniques. Results show that while the baseline U-Net is effective, advanced models, particularly CR-Unet and DenseNet, significantly improve segmentation accuracy. The best performance was achieved using an ensemble model and a fine-tuned pre-trained network, highlighting the potential of these approaches in enhancing the precision and reliability of ovarian disease detection.
Enhancing IoT Security with Activity-Based Attack Modeling and Hybrid Classification Techniques
Authors: Sayali Renuse
Published: November 2024

The proliferation of Internet of Things (IoT) devices in industrial environments (Industrial IoT or IIoT) has brought about significant advancements in automation and data analytics. However, the integration of these devices also introduces new security vulnerabilities, making them prime targets for cyber-attacks. This study aims to enhance the security of IIoT systems by employing an activity-based attack modeling approach coupled with hybrid classification techniques. Our proposed method leverages a hybrid GRU-LSTM model to detect and mitigate security threats in real-time. Activity-based attack modeling involves the analysis of device behavior and the identification of deviations from normal activity patterns. By focusing on the contextual behavior of IIoT devices, we can more accurately detect anomalies indicative of potential security breaches. The hybrid GRU-LSTM model, which combines the strengths of Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) networks, is utilized to process the sequential data generated by IIoT devices. This combination enhances the model's ability to capture both short-term and long-term dependencies in the data, improving the detection accuracy of complex attack patterns. Our experimental results demonstrate that the proposed hybrid GRU-LSTM model achieves an impressive accuracy rate of 98.18% in identifying various types of cyber-attacks on IIoT systems. The implementation of this model at the edge of IIoT networks ensures real-time threat detection and response, minimizing the latency and reducing the dependency on centralized cloud computing resources. In conclusion, this research presents a robust and efficient approach to enhancing IIoT security through the integration of activity-based attack modeling and advanced hybrid classification techniques. The high accuracy of the proposed method highlights its potential for widespread adoption in securing IIoT environments against evolving cyber threats. This work contributes to the growing body of knowledge in IoT security and paves the way for further innovations in protecting industrial systems from cyber-attacks.
Mathematical Analysis of Power Quality Disturbances in Electrical Distribution Systems: Detection, Classification, and Mitigation Strategies
Authors: Shruti Helonde
Published: November 2024

Maintaining a high-quality power source is an important part of modern electricity distribution systems for making sure that many industrial, business, and domestic uses work reliably and efficiently. Power quality disruptions, on the other hand, make this goal much harder to reach. This essay gives a thorough statistical study of power quality problems, focused on how to find them, classify them, and find ways to fix them. The first part of the study talks about how to find power quality problems, which means finding changes in voltage, current, or frequency that don't make sense. Different signal processing methods, like Fourier analysis, wavelet transform, and advanced machine learning algorithms, are tested to see how well they can find and describe different kinds of problems. Second, the paper talks about how to classify power quality problems, which is important for figuring out what causes them and choosing the best ways to fix them. There are different kinds of disturbances, such as voltage sags, swells, interruptions, harmonics, and transients. Each type needs a different way of being analyzed and dealt with. Researchers are looking into whether statistical methods, pattern recognition tools, and artificial intelligence can correctly group events based on their unique features. Lastly, the paper talks about ways to lessen the bad effects of power quality problems on electrical distribution systems and loads that are related to them. These methods include both preventative steps, like making equipment better designed and rearranging networks, and defensive ones, like using active power filters, voltage controls, and energy storage systems. Coordinating spread energy resources with smart grid technologies is also being looked into as a way to make the system more resistant to disruption. In general, the mathematical analysis in this paper gives us useful information about how complicated power quality problems are and gives us a way to find them, sort them into groups, and fix them. Electrical companies, equipment makers, and system workers can come up with better ways to make sure that modern distribution systems have a stable and high-quality power source by using advanced mathematical methods and new technologies.
Advanced Deep Learning and Nonlinear Mathematical Analysis for Precision Detection and Targeted Treatment of Plant Diseases
Authors: Sanjay Balwani
Published: November 2024

Plant diseases are a big problem in agriculture because they lower yields around the world. Regular ways of finding and treating diseases usually require a lot of work, take a long time, and aren't always accurate. For these problems, this study shows a combined method using advanced deep learning methods like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and MobileNet, along with nonlinear mathematical analysis to improve the accuracy of finding plant diseases and making the best targeted treatment plans. Image-based diagnosis of plant diseases using deep learning methods, especially CNNs, is a quick and accurate method. They learn to spot tiny visual clues that point to different diseases by looking at large collections of images of healthy and sick plants. Addition of LSTMs and MobileNet with optimization improves the model's ability to handle time cycles and complex patterns, making it more reliable and usable for a wider range of plant types and weather conditions. The deep learning models are also fine-tuned using an optimization method, like the Adam optimizer, which ensures the best performance and faster resolution. Additionally, complex mathematical models are created to show how plant diseases spread and get worse in crop fields. Based on things like disease transfer rates, weather conditions, and plant chemistry, these models can predict how diseases will spread and help with treatment plans. This system gives dynamic, site-specific advice on how to control diseases by combining these models with real-time data from field devices and remote sensing technologies. Testing the suggested method's effectiveness in the field on many different crops shows that it works much better than traditional methods at both finding problems and treating them.
Smart Nutrient Management in Hydroponics: IoT-Driven Optimization for Enhanced Crop Yield and Resource Efficiency
Authors: Pradnya Vishram Kulkarni
Published: November 2024

Hydroponics is a potential option to farming in dirt because it gives you more control over how much water and nutrients are used. However, managing nutrients optimally in hydroponic systems is still a difficult task. This research suggests a smart nutrition management system that uses Internet of Things (IoT) technologies to get the most out of resources and increase food growth. The framework has sensors built in that can check external factors like pH, electrical conductivity, and nutrient amounts in the hydroponic solution in real time. These sensors send constant streams of data, which lets the exact amounts of nutrients be changed to meet the needs of different plants at different times of growth. Machine learning techniques are used to look at monitor data and guess what the best food mixes will be. The system changes with the times by using past data and weather models. This keeps resources from going to waste and increases food yield. The IoT system also allows tracking and control from afar, which makes upkeep and strategic actions easier. Automated alerts let farmers know when conditions aren't as good as they should be, so they can make changes quickly to avoid crop damage or nutrition shortages. Experiments are done in a controlled growing setting to see how well the planned system works. Compared to standard ways, the results show big gains in food growth and resource economy. It can also be scaled up or down, and it can be changed to work with different types of crops and environments.
Predicting Lifestyle-Related Diseases Early: The NNSVM Methodology
Authors: Ms. Apeksha V. Sakhare
Published: November 2024

Chronic conditions including diabetes, heart disease, and hypertension are becoming major worldwide health issues, and a growing number of them are related to lifestyle decisions. Even though conventional medical statistics like blood pressure and glucose levels are significant markers, they only provide a portion of the picture. The key to early disease identification and prevention is frequently found in lifestyle choices, such as what we eat, how much exercise we get, and whether or not we smoke. In this work, we explore how machine learning might be used to anticipate these illnesses by including a person's everyday routines and behaviors in addition to reviewing medical records. In doing so, we developed a prediction model that is more accurate and comprehensive, revolutionizing the way we approach illness prevention in a society that is becoming more and more data-driven. The study develops a more thorough method of illness prediction by combining standard medical data with lifestyle habits. Algorithms like Support Vector Machine (SVM), Random Forest, Decision Trees, and Artificial Neural Networks (ANNs) are evaluated for their accuracy, precision, recall, and F1-score. According to the study's findings, lifestyle decisions have a significant impact on the development of chronic illnesses, and including them in disease prediction models increases the accuracy of those predictions overall. Combining many data sources is crucial for more accurate health outcomes, as the Random Forest model shows to be the most effective in forecasting diseases connected to lifestyle choices due to its capacity to manage intricate, non-linear interactions between variables. Early detection lowers the chance of serious health consequences by enabling prompt medical intervention, lifestyle modifications, and preventive measures. This strategy lowers healthcare expenses by reducing the need for costly follow-up therapies, while also improving patient outcomes.
Smart Grid Management Enhanced by IoT Device Integration to Improve Energy Efficiency and System Reliability
Authors: Monica Kalbande
Published: November 2024

As a potential way to improve energy economy and system stability, putting Internet of Things (IoT) devices into smart grid control has become clear. This study looks at the full effects of adding IoT devices to smart grid control, focusing on the good and bad things that might happen. The smart grid is an updated electricity grid that uses digital communication technologies to track and manage power flows. This makes energy supply more efficient, reliable, and long-lasting. The standard smart grid, on the other hand, has problems like not being able to see how the grid works, using energy inefficiently, and being easy to disrupt. IoT devices offer an answer by giving real-time information on how well the grid is working, which allows for proactive repair and improvement. A mixed-methods approach was used for this study, which includes both quantitative analysis of data on energy use and qualitative conversations with experts in the field. Machine learning techniques are used to predict how much energy IoT devices in a testbed smart grid will use. This lets us look closely at how adding IoT devices affects system stability and energy efficiency. The study finds that incorporating IoT devices into smart grid control greatly enhances energy economy, resulting in an average 15% drop in energy use. IoT devices also make systems more reliable by letting you see how the grid is doing in real time and responding quickly to problems. The results show that adding IoT devices could change how smart grids are managed, letting companies make better use of energy, make systems more reliable, and improve grid performance overall. But problems like data protection and communication still exist, which shows that this area needs more study and development.
Deep Learning-Enabled Optimization of Polyp Frame Detection and Segmentation
Authors: Sunita B. Borse
Published: November 2024

Polyp segmentation is a method for identifying and characterizing polyps, or abnormal tissue development, as seen in frame images from medical procedures such as endoscopic or colonoscopy frame images. Polyp segments are reliant on high-quality imaging data, which can be intermittently accessible, causing lower detection and precision in segmentation. To overcome these challenges, we propose a novel deep learning (DL) method called intelligent chameleon swarm optimized-adaptive fully convolutional neural network (ICSO-AFConvNet) for polyp segmentation and frame detection. Initially, we gather the dataset named (KVasir-SEG), which contains a thousand frame images, and then contrast limited adaptive histogram equalization (CLAHE) technique, is used to preprocess the data. Experimental findings on the KVasir-SEG dataset reveal that ICSO-AFConvNet outperforms previous approaches in measures such as Dice, Interception over union (IoU), Recall and Precision. These findings show that the suggested ICSO-AFConvNET technique has tough generalization power and learning ability, making it an appealing alternative for practical applications with significant data variances.
Review of Medicinal Plant classification System
Authors: Prateek Meshram
Published: November 2024

Previous studies in the fields of botanical sciences and medicine have looked closely at the categorization and identification of therapeutic plants. In the past, scholars have delved into conventional knowledge bases with the goal of deciphering the botanical characteristics and medicinal applications of many plant species, establishing the foundation for contemporary research. Nevertheless, distinguishing and categorizing a wide range of medicinal plant species proved to be a significant obstacle for studies. The challenge was precisely identifying and classifying the subtle and unique characteristics shared by various plant species. These obstacles impeded the creation of thorough and accurate classification schemes designed especially for medicinal plants. Our suggested Medicinal Plant Classification System uses a novel strategy that makes use of convolutional neural networks (CNNs) to overcome these difficulties. The CNN-based method greatly improves our classification system's accuracy and versatility while making it easier to identify subtle botanical features. For our work, CNNs seem to be an essential answer, demonstrating unmatched effectiveness in capturing various complex properties present in photos of medicinal plants. The flexibility, capacity for feature extraction, and hierarchical learning that CNNs possess highlight their critical role in creating a strong and adaptable classification system designed especially for the identification of medicinal plants.
Mathematical Evaluation of Deep Learning Architecture with Feature Fusion for Cervical Cancer Detection Classification
Authors: Manohar K. Kodmelwar
Published: November 2024

Cervical cancer is still one of the main reasons women die around the world, which shows how important it is to have accurate and quick ways to diagnose the disease. This study looks into how to improve the accuracy of finding cervical cancer using a deep learning-based classification system that combines features. In this mixed classic feature extraction methods, like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT), with deep learning architectures, like Graph Convolutional Networks (GCNs) and Capsule Networks. Each of these added its own benefits to the fusion model. Both HOG and SIFT are good at capturing important texture and structure features, even when the image conditions change. GCNs are used to learn relationship data between picture areas. This helps get a better sense of how things fit together in space, which is very important for medical imaging. Capsule Networks, on the other hand, let you understand the subtleties of hierarchical patterns in pictures of cervical cells. They do this by looking at spatial hierarchies and rotational changes that are very important for finding cancer. Our suggested fusion model combines these multi-level features, taking advantage of how the best features of deep learning and standard learning methods work together. Validation on standard cervical cancer image datasets showed that this fusion approach was much more accurate and precise at classifying than individual methods. Quantitative testing showed that the fusion model had higher sensitivity and specificity, which shows that it can reduce the number of fake negatives and positives, which is very important in clinical diagnosis. Our results show that the feature fusion model not only gives us a strong and accurate way to classify cervical cancer, but it also lays the groundwork for more uses in other complicated areas of medical imaging.
Performance Analysis of Multi-Core Systems in Multistage Interconnection Networks: Investigating Challenges in Inter-Processor Communication
Authors: Abhay B. Rathod
Published: November 2024

For high-performance computers and data-heavy apps, how well multi-core systems work in multistage interconnection networks (MINs) is very important. As the need for parallel processing grows, inter-processor communication (IPC) has become a major issue that affects both the speed of work and the ability of the system to grow. This essay looks into the problems with IPC in multi-core systems set up with MINs, mainly delay, bandwidth limits, and network congestion. imulation-based performance review is used to test multi-core systems with different amounts of traffic, message sizes, and route techniques. A mix of mathematical models and simulation tools are used to test different MIN designs on measures like delay, speed, and fault tolerance. It is also looked at how network structures and methods for controlling crowding affect IPC efficiency. According to the results, MINs can be used to connect multiple devices, but when there is a lot of traffic, the delay goes up a lot because of bottlenecks in some parts of the network. Also, in higher-dimensional networks, processors fighting over bandwidth causes speed to drop. Some of these problems can be solved with optimized transport methods and adaptable congestion control systems, which also make IPC work better overall. This study shows how important it is to keep improving how processors talk to each other in multi-core systems, especially when it comes to making network switching and managing congestion more efficient.
Analysis and Exploration of Carbon Emission Dataset using Machine Learning Techniques
Authors: Rashmi B. Kale
Published: November 2024

One of the primary environmental climate issues in metropolitan cities is that most people are faced with low air quality. Climate variance is impacting the world, triggering drought, storms, and extreme weather events. The primary contributor to climate change is affected greenhouse carbon gas emissions, of which carbon dioxide (CO2) and carbon monoxide (CO) make up the majority. The carbon emissions are expected to rise steadily worldwide. Many factors, including the burning of fossil fuels in transportation and manufacturing sectors, cause climate variance. Fast urbanization has used a high rate of motor vehicles as compared to a rural area. In metropolitan cities across the world, automobiles are the primary cause of air pollution. The use rate of vehicles keeps increasing, and that results in traffic congestion. This work is focusing on collecting two real-time vehicle emission datasets from two different devices to predict and compare the emissions of different light-duty vehicles by using the machine learning techniques. This research also analyzes the Canadian government emission dataset It shows the results of emission by fuel type and vehicle model. It shows the comparison graph of, mean square error, root mean square error and accuracy comparison of different machine learning algorithms. The work suggests some policies for reducing the carbon emissions. Even the government can adapt certain policies for mitigating the carbon emissions of individual vehicles. In this research work issues for reducing the emission of individual vehicles have been addressed. This work helps the automobile sector to reduce the emission and play a part to save the environment.
Adjacent Buildings Subjected to Seismic Pounding: A systematic Comprehensive Review
Authors: Amruta Killol
Published: November 2024

Buildings next to one another that are too close together or have differing dynamic properties may vibrate out of phase during earthquakes. This leads to slamming of these buildings against one another. Provision to avoid this pounding effect is given in various codes. When the structures are exposed to external excitation, the dynamic properties of the nearby structures cause the structures to oscillate out of phase. Impacts will happen if the space between the structures is not large enough to support the lateral displacements brought on by this out-of-phase oscillation. The excitation characteristics, the current separation gap size, and the degree of variation between the dynamic properties of the impacting structures all influence the impact forces and impact locations along the heights of the structures. This work provides a detailed investigation of the impact of pounding in both structures that have undergone site-specific analysis and those that have not. If the interaction between the soil and structure is not taken into account during the designing process, the effects of seismic pounding are exacerbated. In addition, this paper analyses a number of research that examine the connection between soil structure and seismic pounding.