bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of IoT Framework for Prediction of Heart Disease using Machine Learning
Authors: Swain Satyaprakash , Behera Naliniprava , Swain Anil Kumar , Jayasingh Suvendra Kumar , Patra Kumar Janardan , Pattanayak Binod Kumar , Mohanty Mihir Narayan , Naik Kodanda Dhar , Rout Sefali Surabhi 
Published: October 2023

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.
Fusing Long Short-Term Memory and Autoencoder Models for Robust Anomaly Detection in Indoor Air Quality Time-Series Data
Authors: Brahmam M. Veera , Gopikrishnan S. 
Published: October 2023

People spend most of their time indoors by choice or by need. Carbon dioxide (CO2) accumulation can cause various adverse health effects, including vertigo, headache, and fatigue. Therefore, monitoring indoor air quality(IAQ) is necessary for various health reasons. The market is flooded with air quality monitoring devices. However, the ordinary public does not make use of them because they are expensive and difficult to obtain. Several research studies have been carried out to monitor indoor air quality with the help of the Internet of Things(IoT), which has greatly simplified the method for monitoring IAQ. In this research, we offer an improved IoT based IAQ monitoring system with AI-powered recommendations. Our suggested system relies on the Message Queuing Telemetry Transport(MQTT) protocol for communication between IoT devices. In addition, the gathered CO2 occupancy data is used together with the deep learning approach of Long Short-Term Memory and Autoencoder (LSTM-AE) to detect anomalies or outliers in CO2 concentrations. Due to a close connection between air quality and human health and well-being, the detection of anomalies in the data of IAQ has emerged as an essential topic of study. Anomalies requiring the observation of correlations spanning numerous data points (i.e., often referred to as long-term dependencies) were not detectable by conventional statistical and basic machine learning (ML) related techniques in the sector of IAQ. Hence this research uses the LSTM-AE model to address this issue. In comparison to previous similar models, our experimental results on a generated CO2 occupancy time series reveal a robust and powerful accuracy of 99.49%.
AgriBlock: A Blockchain based Framework for Agricultural Supply Chain Management
Authors: Acharya Deepak Kumar , Shrivastava Manish , Padhi Payodhar 
Published: October 2023

To facilitate complex interactions between regional players and the agricultural environment, modern agriculture supply chains have arisen. Supply chain processes that include several parties, such as production, processing, and delivery, interacting with one another. Lack of transparency in the agricultural supply chain is a major contributor to the incidence of fraud in this industry. A lack of openness on the part of organisations leads to worries about potential financial losses, diminishing customer trust, and dwindling brand value. Several fundamental changes to the existing supply chain architecture are required to foster the development of an effective and reliable trade environment. It is widely recognised that blockchain technology has the potential to increase visibility across agricultural and food supply chains. Businesses are using blockchain and IoT to implement more egalitarian, sustainable, and consumer-friendly methods of food production. Based on what we learned, blockchain technology may be useful in the push for a more transparent and responsible food supply chain. The primary goal of this study is to create a decentralised application framework called AgriBlock that can be used for managing the supply chains of agricultural products using blockchain technology. The developed work AgriBlock will aid in making the supply chain process more open and clear to every participant involved.
Optimizing Pre-Trained Models of Deep Learning for Identification of Plant Disease
Authors: Prakadeswaran S. , Banu A. Bazilla , Kumar R. Senthil , Raj S. Gokul 
Published: October 2023

The Plant diseases should be identified early to prevent the economic loss of farmers and ensure the availability of food for humans. The plant disease identification can be automated by using the Artificial Intelligence techniques. Researchers have proposed many deep learning methods for identifying plant diseases. Deep learning models use an increased number of parameters, it requires higher computational power, training a deep learning model from start requires more time. In this article we utilized transfer learning along with fine tuning for identification of plant diseases. Cassava plant disease dataset was utilised for training. and evaluate the suggested model. The performance accuracy achieved by Resnet50 is 73.12 % and fine-tuned Resnet50 is 80.84 %. The fine-tuned model achieves greater accuracy with a lesser amount of parameters
Impact Statement–Artificial Intelligence is evolving all around the world. The AI techniques are used to automate the process of plant disease identification. Traditional methods are not accurate and time consuming. To help the farmers in diagnosing plant disease and stop economic loss to them, we employ deep learning models to do the work. The pretrained models predict the plant diseases, further we fine-tune them in order to get high accuracy. Early identification of the diseases accurately will avoid loss and improve productivity of the crops.
A Novel Hybrid Security Framework (HSF) with Vshield Based Firewall to Secure Cloud Computing Environment
Authors: Praise J. Jeya , Muthukumaran N. , Raj R. Joshua Samuel 
Published: October 2023

Cloud Computing is an emerging technology that provides an enormous amount of computing resources which includes networks, servers and storages which are accessed through the internet. In addition it allows useful provisioning of the resources based on the user’s demands. A crucial aspect of cloud computing infrastructure is to provide secure and reliable services. The main challenge lies in the security issues is to reduce the impact of third party attacks in the cloud computing environment. Hence a novel Hybrid Security Framework(HSF) based on Reinforcement Learning (RL) Methodology with Vshield Firewall is proposed for securing the cloud environment. The RL method is used for deep packet inspection and VShiled based firewall is established to deny the attacks which are malicious when authenticating the signature of incoming packets. The bipartite pattern matching approach is integrated with the RL method to verify the signatures for obtaining the decisions quickly. The simulation results shows that the hybrid security framework is effective when compared with the existing methods by considering response time, resource utilization and denial of malicious attacks. This indicates that our proposed framework achieves not only better security but also attains better efficiency in cloud computing environment.
DIB - A Novel Optimized VANET Traffic Management Using a Deep Neural Network
Authors: Sankar P. , Joel M. Robinson , Husain A. Jahir 
Published: October 2023

The advancement of the Internet of Things (IoT) establishes the development of the Internet of Vehicles (IoV) and Intelligent Transportation Systems (ITS). An integral part of ITS is the vehicular ad hoc network (VANET) with smart vehicles (SV). In this research, a dynamic method of traffic regulation in VANET is presented using Deep Neural Networks (DNN) and Bat Algorithms (BA). With a reduced average delay, the former (DNN) is utilized to guide vehicles across very crowded routes to increase efficiency. In order to examine the traffic congestion status between network nodes, BA is integrated with the IoT and moved over VANETs. Experiments were conducted to test the effectiveness of the proposed method with various parameters such as average latency, packet delivery ratio (PDR) and throughput and the performance were compared with different machine learning (ML) algorithms. The simulation outputs proved that the proposed technique supports real-time traffic circumstances with less energy usage and delay than existing methods.
Comparision of Different Classifiers for Prediction of Breast Cancer
Authors: P B Tintu , Priya S Manju 
Published: October 2023

The cell formed in the breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set. Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques.
Nurturing Agribusiness: A Sustainable Farming System for Tomato Crop Management Leveraging Machine Learning
Authors: Praksh B. Leelaram , Gundavarapu Mallikarjuna Rao , Latha S. Bhargavi , Raju S Ravi Kumar , Kumar NVS Pavan 
Published: October 2023

The agriculture industry is undergoing a transformative shift with the introduction of IoT technology, enabling global connectivity for farmers. This technology offers a plethora of advantages, ranging from precise seed selection based on soil analysis to efficient crop maintenance, water management, and enhanced marketing support for improved profitability. To further enhance tomato farming practices, we propose the implementation of a smart farmer marketing assistant that streamlines the process of segregating yield based on its growth stage, reducing labor and time requirements.Further, the frame work is capable of early-disease management system that can detect diseases like early blight,light blight, buck eye rot and anthranose and suggest remedy. By adopting this innovative approach, financial losses associated with traditional methods are minimized.The traditional practice of combining all categories of vegetables (ripened, unripened, and partially rotten) in a single container often results in reduced shelf life for the produce. In our framework, we employ color sorting to categorize the vegetables, ensuring proper packing into their respective bins. This valuable data is made accessible through a cloud environment, providing potential buyers with comprehensive information about the yield, its category, and pricing. This increased visibility empowers farmers to reach a global market and sell their produce at competitive prices.
In this context, we present a case study focused on the tomato crop, where we have successfully developed a prototype utilizing ESP32, a color sensor, and Google Firebase. This comprehensive framework effectively harnesses the power of IoT, Machine Learning, and potential marketing strategies, transforming the way farmers manage their crops and connect with buyers on a global scale with highly accurate 87.9% experimental results.
A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset
Authors: Gupta Nikita , Sinha Ripu Ranjan 
Published: October 2023

The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.
An Approach of AlexNet CNN Algorithm Model for Lung Cancer Detection and Classification
Authors: B. C. Kavitha , K. B. Naveen 
Published: October 2023

As a reliable tool for identifying and classifying different illnesses, including lung cancer, deep learning has grown significantly in popularity. It is crucial to quickly and accurately diagnose lung cancer because different treatment options depend on the type and stage of the disease. Deep learning algorithms (DLA) are used to speed up the critical process of lung cancer detection and lessen the burden on medical professionals. In this study, the feasibility of employing deep learning algorithms for the early detection of lung cancer is explored, using data from the Lung Imaging Database Consortium (LIDC) database. The study introduces the VGG-16 and AlexNet models specifically to identify the presence of cancer in lung images. The AlexNet model is chosen for additional classification tasks based on performance. The suggested technique displays considerable increases in both the prediction and classification accuracy of cancer. The results from using the AlexNet model show the highest levels of accuracy, with classification accuracy of 97.76% and prediction accuracy of 97.02%, both verified using a 5-fold cross-validation method. Moreover, when classifying the forms of cancer, the model gets a remarkable area under the curve (AUC) value of 1 for the Adenocarcinoma class, signaling extraordinary performance. Notably, the proposed model achieves an accuracy exceeding 90% across all classes.
Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks
Authors: Vivek Bandi , Sangeetha Tupili , Nawaz S. M. Mustafa , Jemina S. Lincy , Nihal S. 
Published: October 2023

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact. In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost.
Quality of Service Aware Dynamic Bandwidth Allocation for Rate Control in WSN
Authors: Vanitha G. , Amudha P. 
Published: October 2023

Different types of data can be generated by Wireless Sensor Networks (WSNs) in both Real-Time (RT) and Non-RT (NRT) scenarios. The combination of these factors, along with the limited bandwidth available, necessitates careful management of these categories in order to reduce congestion. Due to this, a Proficient Rate Control and Fair Bandwidth Allocation (PRC-FBA) method has been created that prioritizes certain types of traffic and creates a virtual queue for them.In PRC-FBA, the Signal-to-Noise and Interference Ratio (SINR) model is applied to the problem of bandwidth allocation in WSN in an effort to find a compromise between equity and performance. Then, a brand-new bandwidth utility factor is defined with regard to equity and effectivenes. The FBA method in PRC-FBA is devoped for only improving throughput, but not considering delay. However, delay is the main factors for trasnmiitng NRT packets. This paper offers a PRC with Quality of Service (QoS) aware Dynamic Bandwidth Allocation (PRC-QDBA) approach for allocating bandwidth while prioritizing packets based on their traffic classes. This model employs a QoS associated dynamic bandwidth allocation strategy which efficiently distributes the unused time slots among the required nodes. The distribution technique is performed based on hierarchical manner utilizing a parent-child association of tree topology. The parent node receives traffic indication maps (TIMs) from the children nodes and adopts them to allocate time slots based on their demamds. If the parent node is unable to allocate the required slots, it creates a TIM that indicating the demands and transfer it to its immediate parent node. This increases the entire performance rate of RT traffic. Furthermore, this model assures the packet forwarding for previously accepted flows by allowing node transmission based on ancestral connection capabilities. Finally, simulation results demonstartes that the suggested model significantly increases the throughput and delay for bandwidth allocation while also enabling QoS support for RT traffic in WSNs.
End to End Encrypted Smart Lock Using RSA based on Opinion from Social Media Review Comments
Authors: Revathi S. , Shreedevi Shreedevi , Monica K. M. , G Bindu , Menaga D. 
Published: October 2023

In the modern era, from big apartments to small houses, startups to corporate buildings, protecting assets or preventing unauthorized persons are crucial problems. Often traditional locks like padlocks are prone to security risks since they can be easily bypassed. Existing smart lock systems are prone to Man in middle Attacks where digital keys can easily be duplicated. The review comments about prevailing smart locks technologies have been collected from various sources such as blogs and microblogs. The data set is analyzed to discover the opinion of the people about the smartlock product. In this proposed system, an innovative smartlock system prototype is designed using current technologies. A smart lock system has been proposed which is encrypted end-to-end using the RSA algorithm. This system uses a one-time password sent to registered users combined with the master code to unlock the door. This system is designed as such only the users who are connected to a wireless local area network are able to access the smart lock system, this adds an additional layer of security. It is connected to the cloud and logs all the activity from booting to shutting down. The breach detection system along with image capture is also included to detect forced intrusions. The client functionality can be easily ported to any platform which supports HTTP protocol which tends to be the major advantage of the proposed work.
Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles
Authors: Kayarga Tanuja , Kumar S Ananda 
Published: October 2023

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians. Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics. In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works.