bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Innovative Method for Lung Cancer Identification Using Machine Learning Algorithms
Authors: Kavita Singh Et al. 
Published: December 2023

Biological community and the healthcare sector have greatly benefited from technological advancements in biomedical imaging. These advantages include early cancer identification and categorization, prognostication of patients' clinical outcomes following cancer surgery, and prognostication of survival for various cancer types. Medical professionals must spend a lot of time and effort gathering, analyzing, and evaluating enormous amounts of wellness data, such as scan results. Although radiologists spend a lot of time carefully reviewing several scans, tiny nodule diagnosis is incredibly prone to inaccuracy. Low dose computed tomography (LDCT) scans are used to categorize benign (Noncancerous) and malignant (Cancerous) nodules in order to study the issue of lung cancer (LC) diagnosis. Machine learning (ML), Deep learning (DL), and Artificial intelligence (AI) applications aid in the rapid identification of a number of infectious and malignant diseases, including lung cancer, using cutting-edge convolutional neural network (CNN) and Deep CNN architectures, we propose three unique detection models in this study: SEQUENTIAL 1 (Model-1), SEQUENTIAL 2 (Model-2), and transfer learning model Visual Geometry Group, VGG 16 (Model-3). The best accuracy model and methodology that are proposedas an effective and non-invasive diagnostic tool, outperforms other models trained with similar labels using lung CT scans to identify malignant nodules. Using a standard LIDC-IDRI data set that is freely available, the deep learning models are verified. The results of the experiment show a decrease in false positives while an increase in accuracy.
A Reliable Network Routing Protocol Design with an Intelligent Mobile Sink and Energy Efficiency over Wireless Sensor Network
Authors: Sunita S S Patil Et al. 
Published: December 2023

This is crucial for the successful operation of a Wireless Sensor Network, frequently called as WSN as each of the sensor nodes in the network's structure are in charge of transmitting the information from its original source to its final destination. Several researchers have developed a Mobile sink in an effort to enhance transmission quality. Although it has been shown to be beneficial, the network's overall reliability is compromised by the much more energy-intensive operation of the nodes. In this research, we offer a routing strategy that makes use of cluster and source portability to drastically reduce power usage. We have given this protocol its name: the Intelligent Mobile Sink Assisted Routing Protocol (IMSARP). To get started, we divide an entire sensor environment into regions, and within each of them, members cast proportional votes to choose who will serve as the Cluster-Head (CH). To determine how to choose the most efficient choice, nodes in the network evaluate the power consumption of all feasible routes. The proposed IMSARP method uses a cluster-based paradigm to construct a mobile-sink routing protocol. The standard quantity of energy within all the clusters serves as what drives the sink's motion. The outcomes section of this research proves the legitimacy and maintains the credibility of the proposed system by presenting the results, which include throughput, delay reduction, energy efficiency, and data transfer rate.
Trust Score based Optimized Cluster Routing (TSOCR) approach for Enhancing the Lifetime of Wireless Sensor Networks
Authors: Sri S. Suresh Babu Et al. 
Published: December 2023

Energy efficiency is the most significant obstacle that Wireless Sensor Networks (WSN) must overcome, and the desire for solutions that maximize energy efficiency will never go away. There are a variety of methods that can be utilized to improve energy efficiency, with data transmission as the primary driver of maximum energy consumption. The transmission of data from the source to destination nodes uses more energy. When the transmission of data is handled better, the energy efficiency is improved and the lifetime of the network is increased. The purpose of this research is to propose an Trust Score based Optimized Cluster Routing (TSOCR) scheme for WSNs, which is based on Whale Optimization Algorithm (WOA). A total trust score is derived by combining the results of computing three distinct trust scores, such as the direct, indirect, and the most recent trust score. The path that has the highest trust score is chosen as the route and employed for data transmission. The effectiveness of the work is evaluated by looking at factors such as the rate of packet delivery, the latency, the amount of energy consumed and the lifetime of the network.
A Machine Learning Classification Paradigm for Automated Human Fall Detection
Authors: Sarthak Turki Et al. 
Published: December 2023

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.
The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.
Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.
Upset or Collapse Detection System for ASD Children Using Smart Watch with Machine Learning Algorithm
Authors: M.E.L. Subair Ali Et al. 
Published: December 2023

ASD is characterised by severe and violent behavioural issues that are referred to as "meltdowns (upset) or tantrums (collapse)" and can include aggression, hyperactivity, intolerance, unpredictability and self-injury. This research work intends to develop and implement a non-invasive real-time Upset or Collapse Detection System (UCDS) for people with ASD. With a certain model of smart watch, the non-invasive biological indications such as Pulse Rate (PR), Skin Temperature (ST), and Galvanic Skin Reaction (GSR) can be artificially captured. In order to create the UCDS, deep learning algorithms like CNN, LSTM, and the hybrid of CNN-LSTM are given the physiological signals that are captured to a server. The deep learning algorithm could recognise aberrant upset or collapse states from real-time physiological signs after being trained. Deep learning algorithms including CNN, LSTM, and CNN-LSTM are used to train and test the proposed UCDS system, and it is discovered that hybrid CNN-LSTM beat them all with an average training and testing accuracy of 96% and a low mean absolute error (MAE) of 0.10 for training and 0.04 for testing. Furthermore, the suggested UCDS system is supported by 93% of the ASD caretakers.
Investigating the Impact of Multiple Inputs on Predicting Glucose Level in Individuals with Type-1 Diabetes through Deep Learning Technique
Authors: Sunandha Rajagopal Et al. 
Published: December 2023

Type-1 Diabetes is a chronic auto-immune disorder featured by the disability of human body to produce insulin, an enzyme helps to digest carbohydrates. Insulin works with the take-up of glucose from the circulatory system into cells. Without sufficient insulin, glucose aggregates in the blood, prompting hyperglycemia. Uncontrolled hyperglycemia, whenever left untreated, can bring about dangerous intricacies, which can harm the vital organs. Managing type-1 diabetes presents huge difficulties for impacted people, as it requires a sensitive equilibrium of insulin administration, sugar consumption, active work, and cautious observing of blood glucose levels. Despite the progress made in type-I diabetes management, challenges persist. Achieving optimal blood glucose control remains elusive for many individuals. Understanding the complex interplay between these variables and their impact on blood glucose regulation is vital for emerging more effective treatment strategies.
Accurate and timely prediction of glucose level plays a critical role in the effective management of diabetes. Through this paper we aim at investigating the impact of multiple inputs on forecasting of glucose level in individuals with type-1 diabetes by means of deep learning techniques. In our paper, we use the Long-Short-Term-Memory (LSTM) network to study the forthcoming blood glucose pattern when various combinations if inputs, previous blood glucose reading, insulin administrated and carbohydrate intake are provided as inputs. We used the ShanghaiT1DM dataset for the study.
Evaluation of Garbage Management Based on IoT
Authors: Naveen Raja S.M Et al. 
Published: December 2023

Smart Waste Monitoring: To track the amount of waste in bins and containers, IOT-enabled garbage management systems use sensors and connected devices. These sensors can communicate real-time data to a centralized monitoring system and can identify the fill level. This data aids in streamlining waste collection routes, cutting back on pointless pickups, and enhancing garbage management effectiveness as a whole. Effective Resource Allocation: By giving precise data on waste generation patterns and trends, IOT-based garbage management systems enable optimal resource allocation. This information can be used by municipal authorities to make well-informed decisions on waste collection schedules, resource deployment, and staffing levels. IOT-based waste management solutions have the potential to make trash management procedures more effective and efficient while also being more affordable. The best garbage collection routes, operational cost reductions, and resource utilization may all be achieved with the aid of research into the best deployment strategies for IOT sensors and devices. Environmental Impact and Sustainability: Research Objective: Clearly identify the research objective, for example, by assessing how well IOT-based garbage management systems gather waste and allocate resources. Data gathering: Compile pertinent information on the methods used for trash generation, collection, and resource use. On-site observations, employee interviews, and database access for waste management operations are all effective ways to accomplish this. Gather information on IOT sensor technologies and their capabilities as well. Taken As alternative for Smart Waste Bins, Waste Level, Sensors, AI Recycling, Robots, E-Waste Kiosks. Taken for Evaluation preference is Reliability, Mobility, Service Continuity, User Convenience., and Energy Efficiency. Smart Waste Bins has performed more when compare to with other Real-Time Monitoring: The Internet of Things (IOT) can be used in waste management to enable real-time monitoring of trash cans or bins can be used to enhance garbage sorting procedures. Smart bins with cameras and sensors can automatically recognize and sort various types of rubbish. These smart bins can identify and categorise rubbish by utilizing IOT technology. on their material composition or recycling category.
Reducing Bit Error Rate in WiMAX using Spread Spectrum Techniques in Comparison with OFDM based Modulations
Authors: Simardeep Kaur Et al. 
Published: December 2023

This research investigated the Bit Error Rate (BER) performance of the WiMAX(Worldwide Interoperability for Microwave Access), a wireless system which uses IEEE 802.16 standard. For the implementation of OFDM model and analyzing the performance of WiMAX system using the Bit Error Rate (BER) and Signal to Noise Ratio (SNR) MATLAB software tool is used. We have used different adaptive modulation techniques such as QPSK, 16-QAM and 64-QAM and spread spectrum techniques such as FHSS (Frequency Hopping Spread Spectrum) and DSSS (Direct Sequence Spread Spectrum). In the current research the Bit Error Rate is improved by using spreading signals. Spread spectrum enables a signal to be transmitted across a frequency band that is much wider than the minimum bandwidth required by the information signal. The simulation results of this research show that FHSS and DSSS have given low BER at low SNR as compared to other modulation techniques.
Virtual Eye – Revolutionizing Vision Assistance For People With Disabilities
Authors: Venkatesh . S Et al. 
Published: December 2023

Visually challenged individuals have faced numerous challenges in their daily lives. These challenges include: Visually challenged individuals have difficulty reading printed materials, including books, magazines, and newspapers. This limitation can significantly impact their education, as they may not have access to all the materials they need to learn. Moving around in unfamiliar places can be a daunting task for the visually impaired. They may struggle to access digital or printed materials, as these are often not available in accessible formats. It might be difficult for those who are blind to identify objects. This can be frustrating, especially in situations where they are alone and need to identify objects.
To address this issue, we are developing a mobile application for visually challenged individuals by providing a range of features such as text-to-speech, speech-to-text, image-to-audio, and PDF-to-audio. It enables visually challenged individuals to access information, read books, identify objects, communicate, and navigate with ease and independence. The app's user-friendly interface can be accessed both manually and by voice command, making it easy to use for people with varying levels of technical expertise. Overall, the Virtual Eye app helps visually challenged individuals lead more fulfilling and independent lives.
Overall, Virtual Eye application is an essential tool for visually challenged individuals, helping them navigate their daily lives with ease and independence. With this app, they can access information, communicate, and identify objects without the need for a third party, enhancing their quality of life and sense of autonomy.
The Impact of Project-Based Learning on High School Education—Based on Systematic Literature Review
Authors: Huang Manhua 
Published: December 2023

Project-based learning (PBL) is a student-centered pedagogical approach that empowers students to actively engage in learning by integrating theoretical knowledge with practical application. It encourages students to synthesize their skills and knowledge to develop practical solutions for real-world problems. Currently, some scholars have begun to explore the impact of the implementation of PBL on high school education. Consequently, a substantial body of literature on PBL in high school education has emerged. A systematic literature review can systematically analyze the research status of the implementation of PBL in high schools, and comprehensively explore the core influencing factors and implementation effects of PBL. In this study, a systematic literature analysis method was employed to compile a total of 47 empirical journals published between 2019 and 2023, focused on the application of PBL in high school education. The analysis served to provide an overview of the research landscape surrounding PBL in high school education. This overview encompassed various aspects, including the number of article publications, sources of journals, research methodologies, academic disciplines, and key factors influencing the of PBL in high school education.
The results showed that the number of publications had been decreasing over the past five years. The analyzed journals predominantly focused on practical subjects like geography, science, chemistry, and physics, with the majority of studies originating from China. Research primarily employed mixed methodologies with sample sizes typically under 100 samples. The predominant research theme centered on STEM subjects. Key factors influencing successful PBL implementation included a relaxed and enjoyable learning environment, a collaborative learning approach, and engaging students through intriguing driving questions. Effectiveness in high school PBL was notably influenced by both students and teachers. In conclusion, this review offered recommendations for PBL in high schools, encompassing various grade levels, preferred databases, optimal search periods, and language considerations. It served as a valuable resource for future research and the development of PBL teaching models in high schools.
Use of Convolutional Neural Network and SVM Classifiers for Traffic Signals Detection
Authors: Snehal Chaudhary Et al. 
Published: December 2023

Road signals are crucial for preserving a safe and effective flow of traffic. They give directions to cars, warn them of potential dangers, and notify them of the conditions of the road ahead. Road signs make roadways safer for both vehicles and pedestrians by regulating traffic and reducing accidents. Failure to obey traffic signals can be harmful and result in collisions. Drivers must always be conscious of their surroundings and pay attention to traffic signs. If a driver misses a signal, they should proceed with caution and safety to prevent injuring themselves or others, and they should seek assistance to reroute themselves. Through the use of machine learning techniques, this project will create a traffic signal recognition system that will identify the traffic signals that are present on the road and inform the driver if the system determines that the motorist has missed a traffic signal or is thus violating traffic laws.
Towards Constructive Cost Analysis for demand based Reusable Domain Specific Components
Authors: N Md Jubair Basha Et al. 
Published: December 2023

The prevailing software development methodology embraced by a majority of organisations is characterised by its agility. The neglect of normal analysis and design procedures is a consequence of the significant pressures associated with designing a product within specified time and budget constraints. This phenomenon could potentially result in a death of software of superior quality, while simultaneously impeding the constructive reuse of components. In the majority of component approaches, the demand of domain specific sofwatre components occurs during the later stages. In this paper, various components can be identified as demand based reusbale domain specific software components, which might also help in reusing these components in the subsequent increments. The strategy for extraction of components & procedure for reusing the existing components is described and a sample case to realize the same is presented.Still there is a dire need to early identify the demand based domain specific sofwatre components and perform the constructive cost analysis for the reusable domain specific software components. The issues related to the estimation of cost reuse measures are still challenging. This paper presents the constructuve cost analysis for the demand based reusable domain specific sofwtare components and proposes reuse measures for the family of applications with the quantized values. By analyzing these cost measures, the budget and effort in the development can be reduced. The results are estimated from the HR Portal domain specific softwrae application as a case study and its respective scenario has been explored in a better manner.
Harnessing Nature’s Green Potential: Nanotechnology Driven Fabrication of Plant-Derived Nanoparticles
Authors: Verma Poonam , Kumar Vikas , Dubey Sakshi , Sao Priyanshu , Tiwari Anjali 
Published: December 2023

Millions of tons of metallic nanoparticles (MNPs) are synthesized each year and used in various fields like nano-enabled devices, personal care, medicine, food, and agriculture. The reaction chemicals and processes employed in the industrial synthesis of MNPs are well acknowledged to be hazardous to the environment.Green synthesis has been recommended as a means of minimizing the usage of harmful substances and adverse chemical modification in the synthesis of MNPs. Extracts of organic chemicals, microorganisms, plants and plant-derived materials have all been employed as reducing agents in green synthesis.One of the most efficient, easy, cost-effective, and environmentally acceptable techniques for reducing the usage of toxic chemicals is to employ plant extracts in the synthesis of MNPs. Several eco-friendly procedures for the fast production of MNPs have been reported by utilizing aqueous extracts of plant components including the leaf, bark, flower, peel, shoot, tuber, resin, roots etc.Every year, new scientific publications are released, with each one highlighting the advantages of the green methodover classic synthesis. However, over two decades after the preliminary reports about the new technique exploded, commercial production of green-synthesized nanoparticle (NPs) does not appear to have found a means to scale up. This review covers green MNPs synthesis from various plant extracts and their potential applications as antimicrobial agents covering the literaturesince 2015. While highlighting the use of MNPs in a variety of fields and the potential impact of phytochemicals associated in MNPs production.
Influencia del aceite de oliva extra virgen en los niveles de tensión arterial de una población de hipertensos
Authors: Donado Rosales Francisco , Rudas Benítez Vanesa , Bilbao Ramírez Jorge Luis , Parody Muñoz Alexander 
Published: December 2023

Objetivo: Determinar la incidencia del consumo de Aceite de Oliva Extra virgen en el nivel de la tensión arterial en pacientes hipertensos. Materiales y métodos: Tipo de estudio: Estudio Experimental modalidad Ensayo Clínico Controlado, mediante la conformación de un Grupo Experimental (50) y un Grupo Control (25). Resultado: Analizándose la incidencia del Aceite de Oliva Extra virgen en los niveles de tensión en los grupos en estudio (experimental – control), se encontró diferencias significativas (p< 0.05) en los cambios en los niveles de Tensión Arterial en el Grupo Experimental en relación con el Grupo Control, resultado que se analizó en relación con las variables: género y edad. Conclusiones: Luego de la intervención al Grupo Experimental, la tensión arterial presentó una disminución significativa. La Tensión arterial Sistólica disminuyó en 32mmHg y la Tensión Diastólica en 24mmHg; las cifras tensionales del Grupo control no presentaron diferencias. Se evidencia, que, en un periodo de 90 días, el uso de aceite de oliva en los pacientes hipertensos reduce significativamente los valores de la tensión arterial.