bluuebunny/crossref_metadata_2025 huggingface dataset:
IoT Enabled Sensory Monitoring System for Fog Optimal Resource Provisioning Method in Health Monitoring System
Authors: Rose J. Dafni , Kumaran J.Chenni , Julalha AG.Noorul , T A Mohanaprakash
Published: September 2023

Fog is data management and analytics service. In this paper gains and most effective novel approach to provide IoT enabled services in healthcare application using Fog Computing. In this research the data is collected from Google Scholar, Science Director and MEDLINE database. IoT based Fog Computing techniques are proposed for delivering quality of services to the user. Optimal Resource Provisioning method is proposed to find edges, service level agreements and administration services for IoT client. The DeepQ residue information processing technique is applied for connecting data centre of the cloud and computing paradigms technique is finding the depth reference of Fog levels. The proposed Optimal resource provisioning algorithm is examining the dataset and TensorFlow tool is used for simulating environment. Fog computing layer consist of IoT sensor data inputs, data centres for the cloud and connected layers for simulations. The Deep belief network is generated based on above inputs using 256 X 256 X 3 layer system and 5000 trained data, 1000 test data are taken for simulations. Each dataset simulation is recording using supervised and unsupervised learning methods. Based on above results IoT enable Fog Computing data management and analytics systems provided 95% accuracy and the compared with existing computing techniques our proposed systems shows better efficiency with respect to safety and convenience.
Grapes Quality Prediction Using Iot & Machine Learning Based on Pre Harvesting
Authors: Sinha Swati Vishal , Patil B.M.
Published: September 2023

Minimizing pesticide use, preserving water, as well as enhancing soil health are just a few of the sustainable farming techniques that must be carefully considered while growing grapes of a high calibre. These practices can help preserve the environment and ensure the longevity of the vineyard. However, it is difficult for the farmers to find the suitability of the soil and its environment to cultivate grapes with high quality. Thus this research aims to evaluate the fitness of the soil for the fitness of growing quality grapes with the aid of machine learning algorithm. The research was done on Nasik region which is called as the “Grape Capital of India” situated in Maharashtra. Total of 154 villages were considered for the examination and soil specimens were collected and sent to the government testing lab in Maharashtra. The soil characteristics by considering both micro and macro nutrients, and the water characteristics were obtained from the lab. Also the climatic features, quality of the petiole and fruit characteristics were included for creating the dataset. These data was given to six different machine learning algorithm to classify the soil by defining whether the soil is fit for grapes or not. Moreover, this research proposed to analyze the correlation between the nutrients by which the relationship and dependency between the different nutrients and features were considered for defining the grapes quality. Also both the micro and macro nutrients were given equal importance in defining the soil quality suitable for obtaining high quality grapes. Based on the results obtained, Pimpalas Ramche contains more nutrients for the grape to grow more successfully based on samples gathered from different vine yards and the decision tree classifier scores better than any other classifiers among the machine learning algorithms employed in terms of accuracy.
ECG Biometric for Human Authentication using Hybrid Method
Authors: T G Keshavamurthy , M N Eshwarappa
Published: September 2023

Recently there is more usage of deep learning in biometrics. Electrocardiogram (ECG) for person authentication is not the exception. However the performance of the deep learning networks purely relay on the datasets and trainings, In this work we propose a fusion of pretrained Convolutional Neural Networks (CNN) such as Googlenet with SVM for person authentication using there ECG as biometric. The one dimensional ECG signals are filtered and converted into a standard size with suitable format before it is used to train the networks. An evaluation of performances shows the good results with the pre-trained network that is Googlenet. The accuracy results reveal that the proposed fusion method outperforms with an average accuracy of 95.0%.
Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality
Authors: Rathod Subhash Gulabrao , Salunke Mangesh D. , Jadhav Hemantkumar B. , Ajalkar Deepika Amol , Satre Dinesh Banurao , Bonde Devyani
Published: September 2023

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs.
Addressing the IoT Schemes for Securing the Modern Healthcare Systems with Block chain Neural Networks
Authors: Balakrishna Chinnala , Sapkal Ashwini , Chowdary B.V. , Rajyalakshmi P. , Kumar Varkala Satheesh , Gupta K Gurnadha
Published: September 2023

This paper provides a wide-range of literature review of various IOT with AI based enabling wearable technologies and protocols used for medical (IoT) with a taught of examining the present and future smart health care technologies. Despite recent advances in medical systems, biomedical hardware, the growth of IoT in medicine continues to advance in terms of biomedical hardware, monitoring figures like cancer patient data disease indicators, temperature levels, oxygen levels, and glucose levels. In the near future, medical IoT is expected to replace the old traditional healthcare systems to smart Ai-IoT based healthcare systems. In our paper we provided a theoretical approach of the most relevant protocols and wearable technologies used for the IoT health care medical systems. We also provided a proposed smart AI based intelligent IoT frameworks for hospital systems settings.
Leveraging Machine Learning based Ensemble Time Series Prediction Model for Rainfall Using SVM, KNN and Advanced ARIMA+ E-GARCH
Authors: Nagesh C. , Chaganti Koushik Reddy , Chaganti Sathvik , Khaleelullah S.K. , Naresh P. , Hussan M.I.Thariq
Published: September 2023

Today's precipitation is growing increasingly variable, making forecasting increasingly difficult. The Indian Meteorological Department (IMD) currently employs Composite and Stochastic approaches to forecast spring storm precipitation in Asia. As a corollary, planners are unlikely to predict the macroeconomic effects of disasters (due to excessive precipitation) or famine (less precipitation). The amount of precipitation that drops dependent on a variety of factors, including the temperature of the atmosphere, humidity, velocity, mobility, and weather conditions. This paper would then employ the Hybrid time-series predictive ARIMA+ E-GARCH (Exponential Generalized Auto-Regressive Conditional Heteroskedasticity) to predict precise runoff by taking into account different climatic considerations such as maritime tension, water content, relative dampness, min-max heat, heavy ice, geostrophic tallness, breeze patterns, soil dampness, and barometric force. In perspective of RMSE, MAE, and MSE, the proposed hybrid ARIMA+E-GARCH paradigm outperformed single simulations and latest hybrid techniques.
Multicasting Model for Efficient Data Transmission in VANET
Authors: Kumar Kamlesh , Sharma Bobby
Published: September 2023

VANETs (Vehicle Ad hoc Networks) are networks made up of a number of vehicular nodes that are free to enter and leave the network. The Location Aided Routing (LAR) protocol is the one that is most frequently utilized among them. Here, the route request packets are flooded across many pathways to the source node using the broadcasting strategy. The vehicles that have a direct path to the destination send the route reply packets back to the source. The least number of hops and the sequence number are used to determine the route from source to destination. This research study has used the multicasting approach to construct a path from the source node to the destination node. Within this multicasting strategy, the root nodes from the network are selected for data routing. The path between the source and the destination is chosen using a root node. The suggested approach is put into practice using the NS2, and some parametric values are computed to produce analytical findings.
A Secured Multi Agent Architecture for Grid Computing
Authors: Sivasubramanian R , Malarvizhi N
Published: September 2023

Grid computing provides big possibilities like resource sharing, resource virtualization, and capacity planning since diverse resources that are geographically dispersed are virtualized as a single entity. The associated security concerns are one of the key obstacles preventing grid computing from being broadly adopted and used. Users in a grid are concerned about the security of their assets and the privacy of their data. A host's security in terms of its data or virtual servers may be jeopardised when it interacts with a grid. By providing multilateral security, i.e., security for both the Grid client and the Grid supplier, our building design expands the degree of assurance that can be placed on the accuracy of a Grid calculation and the assurance of client-provided resources. We discuss the issue of ensuring security and present the multi-agent security construction analysis. The paper outlines a multi-agent strategy for protecting the grid environment's resources. The strategy is put forth to address the grid computing industry's growing, serious security issue. The paper defines a multi-agent security architecture that integrates the capabilities of agents with the Grid Security Infrastructure's basic security mechanism (GSI). A security Master agent and a few security task execution agents make up the strategy.
Machine Learning Based Classification Model for Network Traffic Anomaly Detection
Authors: Reddy K. Shyam Sunder , Krishna Vempati , Prabhakar M. , Srilatha Punna , Gupta K.Gurnadha , Kumar Ravula Arun
Published: September 2023

In current days, cloud environments are facing a huge challenge from the attackers in terms of various attacks thrown to the cloud service providers. In both industry and academics, the problem of detection and mitigation of DDoS attacks is now a challenging issue. Detecting Distributed Denial of Service (DDos) threats is mainly a classification problem that can be addressed using data mining, machine learning and deep learning techniques. DDoS attacks can occur in any of the seven-layer OSI model's network. Hence, detecting the DDoS attacks is an important task for cloud service providers to overcome dangerous attacks and loss incurred to stake holders and also the provider.
Artificial Immune System Implementation for Predicting WM Presence from MYD88 and CXCR4
Authors: Rajesh Nichenametla , Vurukonda Naresh
Published: September 2023

Waldenstrom’s Macroglobulinemia (WM) is a rare malignancy that affects human blood cells and spreads slowly. The development of WM occurs whenever the blood cells undergo genetic changes. Better therapies can be offered by the healthcare sector to get rid of the symptoms that cannot be cured. Everyone in the healthcare sector is aware that genetic abnormalities cause WM, but they are unsure of what causes the alterations. The risk factors that increase the number of WM's aberrant cells have been found. The greatest risk variables have a fatal impact on humans. The healthcare sector is working to save lives by offering better care. Only when WM is discovered earlier when it is treatable with better care and potent medications, is it very likely. For analysing the healthcare data associated with WM, a number of prior research studies have suggested both standard and unique software models and techniques. However, the accuracy is subpar and inefficient in terms of both time and money. To analyse the genomic dataset and detect Waldenstrom's Macroglobulinemia or its symptoms, this research explored this issue and suggested an Artificial Immune System (AIS) approach. Software written in Python is used to conduct the experiment and validate the findings. by contrasting the trial outcomes with other performance assessment techniques. The analysis reveals that the suggested AIS algorithm works better than the others.
A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks
Authors: G V Ashok , Kumari P. Vasanthi
Published: September 2023

Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system.
An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques
Authors: S Janarthanan. , Kumar T. Ganesh , Uma K. , Arvindhan M. , K Anandhan. , Kumar C. Ramesh
Published: September 2023

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%.
Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning
Authors: Tambat Priyanka M. , Bhura Sohel A. , Amdani Salim Y. , Asole Suresh S.
Published: September 2023

The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives.
3-WAY Secured WSN with CSDSM-DNN based Intrusion Detection Model
Authors: Santhi K. , Sowmiya B. , Shalinirajan R. , Vijayashanthi V. , Vasudhevan V.
Published: September 2023

In Wireless Sensor Networks (WSNs), intrusion aims indegrading or even eliminating the capacity of these networks for providing their functions. Thus, in recent years, several ideas are brought and employed. However, these techniques still did not fulfill their requirements in attaining better classification accuracy. This paper proposes a novel Cosine Similarity Distance integrated Sammon Mapping learning layer-Deep Neural Network (CSDSM-DNN)-centricIntrusion Detection Model (IDM) in WSNfor attaining better outcomes. Initially, the nodes are clustered; after that, utilizing Binomial Distribution based Dwarf Mongoose Optimization (BD-DMO), the cluster heads are selected. Then, theIdentity Matrix Function-Kalman Filter (IMF-KF) identified the optimal route. Subsequently, the data is transferred via the secured route. The transferred data is pre-processed and then, the important features are selected. Lastly, to classify whether the data is attacked or non-attacked, the selected features are given into the CSDSM-DNN. Therefore, with the prevailing approaches, the experiential outcomes are evaluated and analogized and it exhibits the proposed model’s higher reliability and efficacy.
Risk Mitigation and Monitoring Challenges in Software Organizations: A Morphological Analysis
Authors: Hasib Nida , Rizvi Syed Wajahat Abbas , Katiyar Vinodani
Published: September 2023

Context: The present body of research on risk mitigation focuses mostly on sparse and dispersed research using various approaches during project development.

Objectives: This paper's goals are to conceptualize and construct a morphological analysis (MA) framework, examine the literature that has already been published on these risks and how to mitigate them in software development, and identify research gaps that suggest areas for further study.

Methods: Based on a survey of 102 works, we provide an MA framework. The MA framework, which is composed of five dimensions and 17 variants, identifies 33 distinct research gaps as the maximum number of potential areas for future study. The implications for future research are covered in the paper's conclusion. Risk reduction during the project development process is extremely beneficial for project managers at an organization in delivering high-quality goods on time and within budget.

Results:  Based on a review of the literature, it was discovered that the development of software projects used a very low percentage of official risk mitigation and elimination methods. Research also revealed the significance of people-related risks in the risk mitigation process for the benefit of the organization. Some key research demonstrated experimentation, case studies, and empirical approaches for evaluation. Future work may include examining project managers' perceptions and knowledge of risk management. Managers of software development teams require greater risk-mitigation strategies.

Conclusion: Scholars and researchers need to put more effort into developing quantitative and intelligent risk models. It is crucial to identify major risks so that they won't compromise the project's future success. In our future work, we can employ more models that are helpful for combining quantitative and intelligent risk models using various AI strategies to reduce hazards.
A Smart System for Detection of Road Lane and Divider
Authors: Bhatlawande Shripad , Shilaskar Swati , Patil Nikita , Raj Prashant , Pujari Rohit
Published: September 2023

Road Lane and divider detection is a core part in environmental perception for driver assistance system. The paper discusses about machine learning and computer vision approach to identify road lanes and dividers and proposes a system for detection of road lane and divider. Driving assistance systems heavily rely on the road lane and divider recognition. Voting classification was implemented using 7 different classifiers. The combination of Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) provided feature extraction. Principle Component Analysis (PCA) provided dimension reduction. The performance has been examined on the basis of accuracy (95.50%), precision (78.81%), recall (65.71%), and F1 score (71.67%). The proposed solution is helpful in solving issues related to road safety and reducing road accidents.
Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity
Authors: Yaswanth Raparthi , Babu M Rajasekhara , Vineetha Borra , Shakeer Shaik Mahamad , Potti Vivekananda
Published: September 2023

The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies.
An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents
Authors: Thota Sunil Kumar , Kumar G V S Raj , Koti B. Raja , Kumar K. Naveen
Published: September 2023

Users have recently had more access to information thanks to the growth of the www information system. In these situations, search engines have developed into an essential tool for consumers to find information in a big space. The difficulty of handling this wealth of knowledge grows more difficult every day. Although search engines are crucial for information gathering, many of the results they offer are not required by the user because they are ranked according on user string matches. As a result, there were semantic disparities between the terms used in the user inquiry and the importance of catch phrases in the results. The problem of grouping relevant information into categories of related topics hasn't been solved. A Ranking Based Similarity Learning Approach and SVM based classification frame work of web text to estimate the semantic comparison between words to improve extraction of information is proposed in the work. The results of the experiment suggest improvisation in order to obtain better results by retrieving more relevant results.
Salp Swarm Optimized Hybrid Elman Recurrent Neural Network (SSO-ERNN) based MPPT Controller for Solar PV
Authors: Jeeva C. , Mishra Ambarisha
Published: September 2023

Renewable energy technologies provide clean and abundant energy that can be self-renewed from natural sources; more support from the public to replace fossil fuels with various renewable energy sources to protect the environment. Although solar energy has less impact on the environment than other renewable sources, the output efficiency is lower due to the different weather conditions. So to overcome that, the MPPT controller is used for tracking peak power and better efficiency. Some conventional methods in MPPT controllers provide less tracking efficiency, and steady-state oscillations occur in maximum power tracking due to the sudden variations in solar irradiance. Thus, in this work salp swarm optimized (SSO) based Elman recurrent neural network (ERNN) controller is proposed to track the maximum power form PV with high efficiency. The weight parameter of ERNN layer is optimized with the help of SSO, which solve the complex problems and give maximum efficiency. The proposed method is performed in MATLAB/Simulink environment, which differs from existing plans and gives a better output efficiency. Using this proposed controller, the system can achieve high tracking efficiency of 99.74% compared to conventional processes.
An Adaptive Technique for Crime Rate Prediction using Machine Learning Algorithms
Authors: Rao V. Chandra Shekhar , Spandhana Kallepelly , Srinivas C. , Sujatha M. , Vani Bojja , Venkatramulu S.
Published: September 2023

Any country must give the investigation and preventive of crime top priority. There are a rising amount of cases that are still pending due to the rapid increase in criminal cases in India and elsewhere. It is proving difficult to classify and address the rising number of criminal cases. Understanding a place's trends in criminal activity is essential to preventing it from occurring. Crime-solving organisations will be more effective if they have a clear awareness of the patterns of criminal behavior that are present in a particular area. Women's safety and protection are of highest importance despite the serious and persistent problem of crime against them. This study offers predictions about the kinds of crimes that might occur in a particular location using ensemble methods. This facilitates the categorization of criminal proceedings and subsequent action in a timely manner. We are applying machine learning methods like KNN, Linear regression, SVM, Lasso, Decision tree and Random forest in order to assess the highest accuracy.
A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System
Authors: Panchal Jinal , Vanjale Sandeep
Published: September 2023

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach..
Feature Classification and Extreme Learning Machine Based Detection of Phishing Websites
Authors: Bhagat Pallavi M. , Waghmare Surendra , Waje Manisha , Patil Rupali , Joshi Kavita , Bakuli Meeta
Published: September 2023

Phishing is a cyber-attack that uses a phishing website impersonating a real website to deceive internet users into disclosing sensitive information. Attackers using stolen credentials not only utilize them for the targeted website, but they may also be used to access other famous genuine websites. This paper proposes a novel approach for detecting phishing websites using a feature classification technique and an Extreme Learning Machine (ELM) algorithm. The proposed system extracts various features from the website URL and content, including text-based, image-based, and behavior-based features. These features are then classified using a feature selection technique, which selects the most relevant features to improve the detection accuracy. The selected features are then fed into the ELM algorithm, which is a powerful machine learning method for classifying and predicting data. The ELM algorithm It trains upon a huge set of data legitimate & phishing websites, and final outcome model is applied to classify unknown websites as either legitimate or phishing. The proposed approach is evaluated on several benchmark datasets and compared with other state-of-the-art phishing detection methods.

The experimental results demonstrate that the proposed approach achieves high detection accuracy and outperforms other methods in terms of precision, recall, and F1-score. The proposed approach can be used as an effective tool for detecting and preventing phishing attacks, which are a major threat to the security of online users.
A Combined Dual Leader and Relay Node Selection for Markov Cluster Based WSN Routing Protocol
Authors: Rajeshwari K. , Rajendran N.
Published: September 2023

The major challenge in Wireless Sensor Networks (WSNs) is to increase the node’s lifespan and decrease energy utilization. To avoid this issue, many Clustering Routing Protocols (CRPs) have been developed, where Cluster Head (CH) in each cluster accumulates the data from each other node and transfers it to the sink through Relay Nodes (RNs). But both CHs and RNs dissipate more energy to aggregate and transfer data. As a result, it is vital to choose the appropriate CHs and RNs concurrently to reduce energy utilization. Hence, this article proposes a Weighted Markov Clustering with Dual Leader and Relay node Selection based CRP (WMCL-DLRS-CRP) in WSNs. This protocol aims to lessen energy dissipation during inter- and intra-cluster communication. Initially, a Markov Clustering (MCL) algorithm is applied by the sink to create nodes into clusters based on a threshold distance. Then, a dual leader selection scheme is proposed to elect dual CHs in each cluster according to the node weighting factor that considers the node’s remaining energy, the distance between CHs and sink, the distance among all nodes, and abundance. Also, an RN selection scheme is proposed to choose the appropriate RNs based on a new Predicted Transmission Rate (PTR) factor. Moreover, the elected RNs transfer the data from the CHs to the sink, resulting in a tradeoff between the node’s energy utilization and lifetime. At last, extensive simulations illustrate that the WMCL-DLRS-CRP achieves better network performance compared to the existing protocols.
Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning
Authors: Elango S. , Revathi V , Deepthi S. Aruna , Kodali Ananya Bhargavi , Gopila M. , Jose Bibin K
Published: September 2023

The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security.
Intrusion Detection Mechanism for Empowered Intruders Using IDEI
Authors: Jinisha J. Josephin , Jerine S.
Published: September 2023

In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available.
Design and Performance Study of Improved Fuzzy System with Genetic Algorithm
Authors: Singh Jyoti , Fatima Shahnaz , Chauhan Alok Singh
Published: September 2023

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method.
Violation of Traffic Rules and Detection of Sign Boards
Authors: Venkatramulu S. , Yugasri Bairy , Sadala Triveni Mohan , Revathi Garidepalli , Rao V. Chandra Shekhar
Published: September 2023

Today's society has seen a sharp rise in the number of accidents caused by drivers failing to pay attention to traffic signals and regulations. Road accidents are increasing daily as the number of automobiles rises. By using synthesis data for training, which are produced from photos of road traffic signs, we are able to overcome the challenges of traffic sign identification and decrease violations of traffic laws by identifying triple-riding, no-helmet, and accidents, which vary for different nations and locations. This technique is used to create a database of synthetic images that may be used in conjunction with a convolution neural network (CNN) to identify traffic signs, triple riding, no helmet use, and accidents in a variety of view lighting situations. As a result, there will be fewer accidents, and the vehicle operator will be able to concentrate more on continuing to drive but instead of checking each individual road sign. Also, simplifies the process to recognize triple driving, accidents, but also incidents when a helmet was not used.
Classification of Acute Lymphocytic Leukemic Blood Cell Images using Hybrid CNN-Enhanced Ensemble SVM Models and Machine Learning Classifiers
Authors: Vijayan Saranya , Venkatachalam Radha
Published: September 2023

Acute Lymphocytic Leukemia is a dangerous kind of malignant cancer caused due to the overproduction of white blood cells. The white blood cells in our body are responsible for fighting against infections, if the WBC increases the immunity will decrease and it would lead to serious health conditions. Malignant cancers such as ALL is life threatening if the disease is not diagnosed at an early stage. If a person is suffering from ALL the disease needs to be diagnosed at an early stage before it starts spreading, if it starts spreading the person’s chances of survival would also reduce. Here comes the need of an accurate automated system which would assist the oncologists to diagnose the disease as early as possible. In this paper some of the algorithms that are enhanced to detect and classify ALL are incorporated. In order to classify the Acute Lymphocytic Leukemia a hybrid model has been deployed to improve the accuracy of the diagnosis and it is termed as Hybrid CNN Enhanced Ensemble SVM for the classification of malignancy. Machine Learning classifiers are also used to design the system and it is then compared with enhanced CNN based on the performance metrics.
Hybrid Cryptography and Steganography-Based Security System for IoT Networks
Authors: Suguna T. , Padma C. , Rani M. Janaki , Priya G.Padma
Published: September 2023

Despite the fact that many IoT devices are publicly accessible to everyone on the network, understanding the security risks and threats posed by cyber attacks is critical; as a result, it should be safeguarded. Plain text is constructed into encrypted text, before being delivered by using cryptography, and is then reconstructed back to plain text after receiving a response from the recipient. The steganography technique can be used to hide sensitive information incorporated in a text, audio, or video file. One approach is to hide data in bits that correspond to successive rows of pixels with the same color in an image file.  As a consequence, the image file retains the original's appearance while also containing "noise" patterns made out of common, unencrypted data. To do this, the encrypted data is subtly applied to the redundant data. In this work, it is suggested that IoT network data be encrypted using cryptography, and that an encrypted message be concealed inside an image file using steganography. Additionally, it is suggested to enhance the number of bits that may be stored within a single picture pixel.  The payload that may be sent through an image is significantly increased by incorporating Convolutional Neural Networks into the classic steganography technique. In this work, we propose, design, and train Convolutional Neural Networks (CNN) to enhance the amount of data that can be securely encrypted and decrypted to show the original message.
Empowering the Visually Impaired: Arduino Mega Enhanced Accessibility Device
Authors: Sherly S. Irin , Kavitha P. M. , Jayachandran S. , Joel M. Robinson , Jeena R.
Published: September 2023

People with vision impairments find their life to be so difficult that they must rely on others even for the most basic tasks. When a user is in trouble, the developed system may identify obstacles and persons, play an audio message, and notify the carer. It delivers auditory output in order to help blind individuals recognise persons and text using Open CV and face recognition. Real-time image processing requirements are met by using a small amount of memory and extremely little processing time. The suggested system delivers a very competitive performance that matches the advantages of portability and ease of use, according to the performance comparison.
Block Chain based Contribute Sequence Tracking System for Secure Data Ascription Management
Authors: Poo Low Jin , Qi Xu Xiao
Published: September 2023

Block chain technique is developed from a distributed, dependable application development platform to an irreversible record of crypto currency transaction history. Block chain technology's emergence has sparked a number of possible changes in how corporate operations are managed across numerous industries. However, to the best of research experience, no prior effort has concentrated on integrating block chain to establish a secure and unchangeable data and evidence attribution maintenance architecture that autonomously checks the origination records. Therefore, the research work proficiently introduces the novel Block Chain based Contribute Sequence Tracking (BCCST) system with amalgamation of Trail based Smart Indenture (TSI) and Release Attribution Model (RAM). The study begins by outlining a practical application of the proposed TSI for the disintermediation of commercial activities utilising a notional, shared information ledger. This ledger not only makes monitoring data exchange easier, but it also encourages supply chain participants to cooperate together on a multilateral network. The novel RAM to create immutable data trails that make it easier to gather, maintain, and verify reliable data provenance.
Implementation of Decoy Deception based Detection System for Ransomware Attack
Authors: Salunke Mangesh D. , Rathod Subhash G. , Jadhav Hemantkumar B. , Yashwante Meghna , Rewaskar Vaibhav D. , Deshmukh Pranjali V.
Published: September 2023

Ransomware poses a dangerous threat to  cybersecurity. Data as well as rights owned by the user are  adversely impacted. The situation has become considerably  more critical as a result of the emergence of new ransomware  varieties and Ransomware-as-a-Service. In this paper, we  presented a novel deception-based and behaviour-based  method for real-time ransomware detection. In order to avoid  any loss before ransomware is discovered, we build pretend  files and directories for nefarious behaviours. We conducted a  pilot study using Locky, and the results demonstrate the  effectiveness of our strategy with little system resource usage  and geographical cost.
Optimized Deeplearning Algorithm for Software Defects Prediction
Authors: A. J. Anju , Judith J. E.
Published: September 2023

Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%.
Optimal Framework for Level Based Access Control for VM Auditing on Cloud
Authors: Garikamukkala S. Rekha , Sankar V. Ravi
Published: September 2023

The growth in the cloud computing have motivated and enable lot of application developer to deploy the applications on cloud. The major challenge of hosting on cloud is the service provider or the application provider must comply to a good number of rules. These compliance reports are time to time validated and checked by external auditors. The auditing process for the cloud services are critical and the access controls must be enabled. Due to the higher complexity and less flexibility of the virtual machines, most of the cases this access control mechanism is compromised. This work proposes four algorithms to identify and enhance the LBAC mechanism for cloud services with access updates based on time variant characteristics analysis and predictive analysis with selective cryptographic methods. The proposed model produces significantly improved results to overcome three major issues in the cloud service management as selective LBAC, static privileges and open access control for the auditors.