Papers by Rajanikanth Aluvalu
Computational intelligence methods and applications, 2024
Recent advances in computer science and communications, Mar 1, 2024
Concurrency and Computation: Practice and Experience, Jun 27, 2022
SummaryLoad balancing and task scheduling in cloud have gained a significant attention by many re... more SummaryLoad balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.
Computational Intelligence and Neuroscience, May 13, 2022
With the advent of the Internet of ings (IoT), human-assistive technologies in healthcare service... more With the advent of the Internet of ings (IoT), human-assistive technologies in healthcare services have reached the peak of their application in terms of diagnosis and treatment process. ese devices must be aware of human movements to provide better aid in clinical applications as well as the user's daily activities. In this context, real-time gait analysis remains to be key catalyst for developing intelligent assistive devices. In addition to machine and deep learning algorithms, gait recognition systems have signi cantly improved in terms of high accuracy recognition. However, most of the existing models are focused on improving gait recognition while ignoring the computational overhead that a ects the accuracy of detection and even remains unsuitable for realtime implementation. In this research paper, we proposed a hybrid gated recurrent unit (GRU) based on BAT-inspired extreme convolutional networks (BAT-ECN) for the e ective recognition of human activities using gait data. e gait data are collected by implanting the wearable Internet of ings (WIoT) devices invasively. en, a novel GRU and ECN networks are employed to extract the spatio-temporal features which are then used for classi cation to realize gait recognition. Extensive and comprehensive experimentations have been carried out to evaluate the proposed model using real-time datasets and also other benchmarks such as whuGait and OU-ISIR datasets. To prove the excellence of the proposed learning model, we have compared the model's performance with the other existing hybrid models. Results demonstrate that the proposed model has outperformed the other learning models in terms of high gait classi cation and less computational overhead.
PeerJ, Apr 21, 2023
In the medical era, wearables often manage and find the specific data points to check important d... more In the medical era, wearables often manage and find the specific data points to check important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length, interruptions, and intensity, and physical activity like kind, duration, and levels. These digital biomarkers are created mainly through passive data collection from various sensors. The critical issues with this method are time and sensitivity. We reviewed the newest wireless communication trends employed in hospitals using wearable technology and privacy and Block chain to solve this problem. Based on sensors, this wireless technology controls the data gathered from numerous locations. In this study, the wearable sensor contains data from the various departments of the system. The gradient boosting method and the hybrid microwave transmission method have been proposed to find the location and convince people. The patient health decision has been submitted to hybrid microwave transmission using gradient boosting. This will help to trace the mobile phones using the calls from the threatening person, and the data is gathered from the database while tracing. From this concern, the data analysis process is based on decision-making. They adapted the data encountered by the detailed data in the statistical modeling of the system to produce exploratory data analysis for satisfying the data from the database. Complete data is classified with a 97% outcome by removing unwanted data and making it a 98% successful data classification.
International journal of computer applications, Mar 1, 2014
Web Usage Mining used to extract knowledge from WWW. Nowadays interaction of user towards web dat... more Web Usage Mining used to extract knowledge from WWW. Nowadays interaction of user towards web data is growing, web usage mining is significant in effective website management, adaptive website creation, support services, personalization, and network traffic flow analysis and user trend analysis and user's profile also helps to promote website in ranking. Agglomerative clustering is a most flexible method and it is also used for clustering the web data in web usage mining, there are do not need the number of clusters as a input. Agglomerative have many drawbacks such as initial error propagation, dimensionality, complexity and data set size issues. In this paper we have introduced solution for data set size problem that helpful for information retrieve from large web data, web log data files are as a input for agglomerative clustering algorithms and output is efficient clustering that will be used further for information extraction in web usage mining.
Lecture notes in networks and systems, 2021
Business organizations and individual users are using cloud storage for storing their data and fi... more Business organizations and individual users are using cloud storage for storing their data and files. Cloud storage is managed by cloud service provider (CSP) being third party person to the data owners. Cloud storage consists of user's confidential data. After storing data in cloud, the owner of data cannot have control over data, where owner cannot trust the CSP because possibility of a malicious administrator. Based on this, different schemes are proposed. Security is a major concern for cloud stored data, and CSP has to provide trust to the data owner on security of the cloud stored data. In general, security to data and applications is provided through authentication and authorization. Security through authentication is provided by distributing user name and password to data users. However, the organizational user is not allowed to access all the organizational data. Authorization for accessing the data is provided by using access control models. Regular models are not enough to use the CSP based on the models uses dynamic method and proposed different models using attribute-based encryption (ABE). Earlier access control models cannot be used because of multiple disadvantages. This chapter will discuss dynamic access control model named as RA-HASBE. This model is proved to be scalable and flexible, due to sub-domain hierarchy. It is also proved to be dynamic by permitting user to access the data by risk evaluation using risk engine.
3rd Smart Cities Symposium (SCS 2020), 2021
Journal of Telematocs and Informatics (e-journal), Mar 1, 2015
Cloud computing is known as "Utility". Cloud computing enabling users to remotely store their dat... more Cloud computing is known as "Utility". Cloud computing enabling users to remotely store their data in a server and provide services on-demand. Since this new computing technology requires user to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. We can increase security on access of the data in the cloud. Morever we can provide encryption on the data so third party can not use the data. In this paper we will be reviewing various encryption based access control model for enhancing cloud security along with their limitations. We will be concluding with a proposed access control model to enhance cloud security.
International Journal of Computer Applications, Feb 18, 2015
Cloud computing is a new computing paradigm in which an application can run on connected Cloud Se... more Cloud computing is a new computing paradigm in which an application can run on connected Cloud Server instead of local server. Cloud computing provides efficient data storage, resource sharing and services in a distributed manner with great ease. However Cloud computing is having issues like security and privacy of data when sensitive data is stored under third party cloud service providers. Various access control models have been proposed to resolve the security issue in cloud computing. So in this paper we have discussed various access control models starting from the traditionally DAC (Discretionary Access Control), MAC (Mandatory Access Control), RBAC (Role Based Access Control) and ABAC (Attribute Based Access Control) to the latest ABE (Attribute Based Encryption) models like CP-ABE (Ciphertext Policy-Attribute Based Encryption), KP-ABE (Key Policy Attribute Based Encryption), HABE (Hierarchical Attribute Based Encryption) and HASBE (Hierarchical Attribute-Set Base Encryption).
International Journal of Sociotechnology and Knowledge Development, Apr 1, 2019
Peopleinthemodernworldareattractedtowardssmartworkingandearningenvironmentsrather thanhavingalong... more Peopleinthemodernworldareattractedtowardssmartworkingandearningenvironmentsrather thanhavingalong-termperception.Thegoalofthisworkistoaddressthechallengeofproviding betterinputstothecustomersinterestedtoinvestinginthesharemarkettoearnbetterreturnson investments.TheTwittersocialnetworkingsiteischosentodeveloptheproposedenvironmentasa majorityofthecustomerstweetabouttheiropinions.Ahugesetofdataacrossvariouscompanies thattakeinputsfromTwitterareprocessedandstoredinthecloudenvironmentforefficientanalysis andassessment.Astatisticalmeasureisusedtosignaltheworthofinvestinginaparticularstock basedontheoutcomesobtained.
Intelligent Decision Technologies, Jul 11, 2023
Lung cancer is one of the dangerous diseases that cause shortness of breath and death. Automatic ... more Lung cancer is one of the dangerous diseases that cause shortness of breath and death. Automatic lung cancer disease identification is a challenging operation for researchers. This paper, presents an effective lung cancer diagnosis system using deep learning with CT images. It also decreases lung cancer’s misclassification. Initially, the input images are gathered from online resources. The collected CT images are given to the detection stage. Here, we perform the detection using a Multi Serial Hybrid convolution based Residual Attention Network (MSHCRAN). Using a deep learning framework lung cancer detection using CT images is effectively detected. The performance of the developed lung cancer detection system is compared to other conventional lung cancer detection models According to the analysis, the implemented deep learning-based detection of lung cancer system had a precision higher than 95.75% compared to CNN with 90.04%, ResNet with 89.62%, LSTM with 92%, and CRAN with 93.4% using dataset-1. The analysis with Dataset-2 shows a precision of 90.43% with CNN, ResNet with 90.12%, LSTM with 92%, and CRAN with 93.7%, with the proposed method precision of 95.8%.
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Aug 1, 2017
Cloud computing is a distributed commodity system. Authorized users can get access to the virtual... more Cloud computing is a distributed commodity system. Authorized users can get access to the virtualized resources on demand over the internet. It refers to the utility computing model. Cloud computing virtualizes system by pooling resources from commodity hardware and supports multi tenancy. It is a highly scalable and flexible virtualized technology is flexible and scalable virtualized technology. Cloud computing provides services on demand over the internet. Users can acquire the required services in a very short time from the cloud. Services provided by the cloud are categorized based on purpose. The cloud provides services like Software as a service (SAAS), Platform as a service (PAAS), and Infrastructure as a service (IAAS). Virtual machines are employed as computing resources for high-performance computing in IAAS. Scheduling of virtual resources and virtual machines (VMs) is the key issue to be handled. Efficient virtual machine allocation is essential for effective utilization of cloud computing infrastructure and increasing resource utilization and efficient deployment of applications in the virtual machine. In this paper performance evaluation of various clustering algorithms for dynamic VM allocation is discussed. The results are verified by simulating the model in workflowsim.
Nowadays the amount of data generated from various device sources and business transactions is ve... more Nowadays the amount of data generated from various device sources and business transactions is very huge. Most of the transactional, business data generated is unstructured. Business organizations use the data to perform analytics for decision making. Performing Analytics on such huge unstructured data has become a challenge for organizations. Enough tools and techniques both with free ware and proprietary license release are available to handle structured data are available. In earlier systems, unstructured data is converted into structured data and then stored in Database Management System (DBMS) for performing further analytics. This is a time consuming process. As the amount of data being generated is increasing tremendously, it has become impossible to transform huge amounts of data into structured data. In order to perform analytics of the digital data, we require different business processes to handle unstructured data directly and efficiently. In this paper, a skillful mechanism is being proposed to handle unstructured data using MongoDB and perform required analytics. The experimental approach and the results are presented.
EAI/Springer Innovations in Communication and Computing, Dec 16, 2021
2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon), Aug 1, 2017
With the evolution of technology, people are migrating from rural areas to cities for better empl... more With the evolution of technology, people are migrating from rural areas to cities for better employment, healthcare, education and for amenities. Rapid urbanization poses environment and social concerns. Smart city concept will manage the problems associated with urbanization. India with cultural diversity, huge population, and diverse potentials among 29 states like natural resources, employment, and economy, social and cultural values needs smart cities for overall development of the country. It is expected that by year 2030, India's economy will grow by five times. Smart cities are imperative to India. Govt. of India has taken smart India mission to transform existing cities to smart cities. India struggles with some barriers which hamper the development of smart cities. This paper addresses various critical issues and challenges faced by smart cities and suggested some solutions.
Procedia Computer Science, 2017
In recent years information and communication technology (ICT) has become an important part of hu... more In recent years information and communication technology (ICT) has become an important part of human life. But ICT brings a lot of cyber risks. New threats and vulnerabilities are created to attack network system. Intrusion detection system (IDS) is used to detect these attacks. Machine learning (ML) and Data Mining (DM) techniques are widely used for IDS. Current IDS algorithms result in high error rate and less accurate to classify various attacks. This paper deals with a novel ensemble classifier (RFAODE) for intrusion detection system. Proposed ensemble classifier is built using two well-known algorithms RF and AODE. Average One-Dependence Estimator (AODE) resolved the attribute dependency issue in Naïve bayes classifier. Random Forest (RF) improves accuracy and reduces the error rate. The performance of proposed ensemble classifier (RF+AODE) is analyzed on Kyoto data set. With accuracy of 90.51% and FAR of 0.14, proposed ensemble classifier outperforms AODE, Naïve bayes, and RF algorithms and efficiently classifies the network traffic as normal or malicious.
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Papers by Rajanikanth Aluvalu