P. Dayananda

Manipal Academy of Higher Education

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M. Niranjanamurthy

Visvesvaraya Technological University

C. N. Sowmyarani

R.V. College of Engineering

S Supreeth

REVA University

S Rohith

Acharya Nagarjuna University

Neelambika Basavaraj Hiremath

JSS Academy of Higher Education and Research

B C Anil

Indian Institute of Technology Palakkad

Jimson Mathew

Indian Institute of Technology Patna

G. Shruthi

REVA University

Mrityunjaya V. Latte

JSS Academy of Higher Education and Research

S. Jagannatha

M S Ramaiah University of Applied Sciences

Cooperative Institutions

JSS Academy of Higher Education and Research

Visvesvaraya Technological University

M S Ramaiah University of Applied Sciences

REVA University

Manipal Academy of Higher Education

PES University

Bangalore University

Siddaganga Institute of Technology

International Institute of Information Technology Bangalore

Sri Siddhartha Medical College

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Utilization of alkali-treated areca fibers for stabilizing silty sand soil for use in pavement subgrades: Analysis using IITPAVE software

Emergent Materials (2024)

Muttana S. Balreddy Sanjay S. Sajjan P. Dayananda Sujay Raghavendra Naganna

Abstract The increasing growth of urban areas and the rise in infrastructure development activities have put a strain on the availability of land with desirable soil conditions. This has led to the development of several stabilization techniques that can be used to improve the properties of weaker soils for construction. The research presented here explores the impact of inducing randomly oriented alkali-treated areca fibers for stabilization of silty sand soil. A sequence of experiments was carried out on the soil-fiber mixtures to investigate the strength of the soil after stabilization. At increments of 0.2%, the fiber dose varied between 0 – 0.8% of the dry weight of the soil. The tests conducted includes compaction tests, California bearing ratio (CBR), unconfined compression strength (UCS) tests, and unconsolidated undrained (UU) triaxial tests. The results obtained showed a notable increase in the strength of the soil-fiber mixtures. An increase in fiber content was found to increase the OMC (optimum moisture content) values and decrease the MDD (maximum dry unit weight) values. The maximum strength of the soil-fiber mixture was obtained at 0.6% fiber content. This makes it possible to use silty sand soil subgrades for low-volume roads with a traffic of less than 2 million standard axles based on the IITPAVE analysis. In essence, the test findings indicated that the ideal fiber content to be 0.6%. Stabilization of local on-site soils is one of the sustainable practices that can help extend the life of a pavement and lessen the need for more frequent repairs/maintenance.

California bearing ratio

Soil Compaction

Soil test

10.1007/s42247-024-00710-4

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Detection of Alzheimer’s Disease Based on Cloud-Based Deep Learning Paradigm

Diagnostics (2023)

P. Dayananda Sowmyarani C. Nagaraju M. Niranjanamurthy Mahesh S. Raisinghani Surbhi Bhatia

Deep learning is playing a major role in identifying complicated structure, and it outperforms in term of training and classification tasks in comparison to traditional algorithms. In this work, a local cloud-based solution is developed for classification of Alzheimer's disease (AD) as MRI scans as input modality. The multi-classification is used for AD variety and is classified into four stages. In order to leverage the capabilities of the pre-trained GoogLeNet model, transfer learning is employed. The GoogLeNet model, which is pre-trained for image classification tasks, is fine-tuned for the specific purpose of multi-class AD classification. Through this process, a better accuracy of 98% is achieved. As a result, a local cloud web application for Alzheimer's prediction is developed using the proposed architectures of GoogLeNet. This application enables doctors to remotely check for the presence of AD in patients.

Leverage (statistics)

Transfer of learning

Modality (human–computer interaction)

Contextual image classification

10.3390/diagnostics13162687

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Citations (16)

Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique

Scientific Reports (2024)

Punita Panwar Sandeep Chaurasia Jayesh Gangrade Ashwani Bilandi P. Dayananda

Abstract Knee osteoarthritis (KOA) represents a well-documented degenerative arthropathy prevalent among the elderly population. KOA is a persistent condition, also referred to as progressive joint Disease, stemming from the continual deterioration of cartilage. Predominantly afflicting individuals aged 45 and above, this ailment is commonly labeled as a “wear and tear” joint disorder, targeting joints such as the knee, hand, hips, and spine. Osteoarthritis symptoms typically increase gradually, contributing to the deterioration of articular cartilage. Prominent indicators encompass pain, stiffness, tenderness, swelling, and the development of bone spurs. Diagnosis typically involves the utilization of Radiographic X-ray images, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) Scan by medical professionals and experts. However, this conventional approach is time-consuming, and also sometimes tedious for medical professionals. In order to address the limitation of time and expedite the diagnostic process, deep learning algorithms have been implemented in the medical field. In the present investigation, four pre-trained models, specifically CNN, AlexNet, ResNet34 and ResNet-50, were utilized to predict the severity of KOA. Further, a Deep stack ensemble technique was employed to achieve optimal performance resulting to the accuracy of 99.71%.

Joint disorder

Knee pain

Economic shortage

10.1038/s41598-024-78203-x

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Transmission Control Protocol Off Path Exploits in Web

Journal of Computational and Theoretical Nanoscience (2020)

K Kavyashree C. N. Sowmyarani P. Dayananda

The network community in the current scenario has faced lot of potential threats like restricted access to unauthorized network; break in through other organizations, making the system unavailable and so on. An off-path attacker can perform various attacks like Browser page read, Web phishing, website Spoofing and defacement to know the 4 tuples in Transmission Control Protocol. The attacker can also carry out Cross Site Scripting to know the sensitive information on websites. Cross Site Request Forgery which performs defective exploit on web. This helps in detecting the four tuples Sequence number, Acknowledgement number, Global IPID Counter and ports.

Spoofing attack

Acknowledgement

10.1166/jctn.2020.9006

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Citations (1)

Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning

International Journal of Cloud Applications and Computing (2021)

B C Anil P. Dayananda B. Nethravathi Mahesh S. Raisinghani

Liver cancer is one the most common forms of cancer. As per statistics in 2018 published by World Health Organization, a quarter of all cancer cases are caused by infections, particularly prevalent in developing countries, including hepatitis B, which is linked to liver cancer. The mortality rate is higher in liver cancer as compared to other types of cancer. Quick and reliable diagnosis tools are of paramount importance for detecting and treating liver cancer in early stage, thus improving the likely course of a medical condition of patient. We have developed a cloud-based solution for liver tumour Segmentation, Classification and Detection in CT images based on GoogleNet architecture of Convolutional Neural Network. Experiment is carried out with training and test sets derived from TCIA repository. The results yield 96.7% accuracy for classification of tumour cells. GoogleNet architecture is used for implementation. The GoogleNet has 70,000 images in diagnosis of malignant tumor in liver cancer, providing a rich database for testing. Our algorithm has been deployed in Azure cloud.

Liver Cancer

10.4018/ijcac.2022010109

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Citations (25)

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

SN Computer Science (2024)

Surbhi Bhatia Ankit Kumar Arwa Mashat P. Dayananda Mohammad Khalid Imam Rahmani

Abstract The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Next-generation network

10.1007/s42979-024-02778-5

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Citations (1)

A Machine Learning Approach for Environmental Assessment on Air Quality and Mitigation Strategy

Journal of Engineering (2024)

Chetan Shetty S. Seema B. J. Sowmya N Rajesh S Supreeth

Air pollution has a significant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of finding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters—Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big difference in the clustering. There is a need to find Eps value in as minimum time as possible. In this work, the goal is to find the Eps value in less time. For this purpose, a search tree technique is used for finding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to find Eps is quicker and efficient in comparison to the widely used KNN algorithm. The time reduction to find Eps makes a significant impact as the dataset size increases. The input parameters are fed to DBSCAN algorithm to obtain clustering results.

10.1155/2024/2893021

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Citations (1)

Weighted full binary tree-sliced binary pattern: An RGB-D image descriptor

Heliyon (2020)

Y. B. Ravi Kumar C K Narayanappa P. Dayananda

We present an Algorithm to understand Inter-pixel similarity, which shall be observed in images with the help of a data structure Full Binary Tree. The Full Binary Tree has certain properties like every node must have 2 children or none. Based on this property of Binary Tree, the method of Sliced Binary Pattern is proposed. The inter-pixel similarity may be observed by converting any pixel information of an image within a block of size 3 × 3 to its binarized form, as the pixel information, whose similarity with neighboring pixel cannot be exploited, when it is in decimal form. Thus, we convert all pixel information within a block of size 3 × 3 to its binarized form then we compare the binary pattern of a central pixel with its 8-nearest neighbors. If there is a binary pattern match between central pixel and its 8-nearest neighbors of a block, we assign weights to it, where the weights are determined by the position of match that exist between central pixel and 8-other neighboring pixels of an image. This process helps in determining the inter-pixel similarity of 8-nearest neighbors with respect to central pixel of a block. Every block of 3 × 3 pixels is processed with this strategy to obtain the similarity between patterns in an image. The erected Weighted Full Binary Tree-Sliced Binary Pattern analyzes an image in RGB-Dimensions based on patterns of Inter-Pixel Similarity by tracing the similarity path. The proposed RGB-D texture based inter-pixel similarity addresses the verification of facial similarity. Further, the proposed WFBT-SBP has yielded a good classification accuracy of 77.4%, 77.3%, 77.98%, and 77.94% over a relations of F-S, F-D, M-S, M-D of KinfaceW-I and 76.89%, 76.72%, 77.01%, 76.99% over a relations of F-S, F-D, M-S, and M-D of KinfaceW-II respectively.

RGB color model

Random binary tree

Tree (set theory)

10.1016/j.heliyon.2020.e03751

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Citations (15)

An Automated Office and Vehicle Management System

H Kavitha M. Rahul Raj Mohana Kumar S Saijal Shankar P. Dayananda

Keeping track of the entry and exit time of vehicles and people in an office is an essential aspect of office management. This paper highlights the importance of a vehicle time logging system, which records the entry and exit time of vehicles and people, in improving the security, attendance management, billing and payroll, planning and scheduling, and compliance of an office. We propose a system that will generate a barcode for all the employee vehicles which will be on the vehicles. System records the vehicle entry and exit timings in the database by scanning the barcode on the vehicle by cameras on the gates. The system also helps to manage employee attendance, billing and payroll, and plan tasks more effectively. Additionally, in some industries, specific regulations around record-keeping and compliance must be followed, which can be facilitated by a vehicle time logging system. The paper concludes that an automated system, such as an access control system, can make the process of recording entry and exit times more efficient and accurate, leading to a more organized and secure office management system. By recording the time of arrival and departure, the system helps to maintain security by identifying any unauthorized or suspicious activity.

Payroll

Barcode

Ticket

Attendance

10.1109/nmitcon58196.2023.10276100

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(p+, α, t)-Anonymity Technique Against Privacy Attacks

International Journal of Information Security and Privacy (2021)

C. N. Sowmyarani Veena Gadad P. Dayananda

Privacy preservation is a major concern in current technology where enormous amounts of data are being collected and published for carrying out analysis. These data may contain sensitive information related to individual who owns them. If the data is published in their original form, they may lead to privacy disclosure which threats privacy requirements. Hence, the data should be anonymized before publishing so that it becomes challenging for intruders to obtain sensitive information by means of any privacy attack model. There are popular data anonymization techniques such as k-anonymity, l-diversity, p-sensitive k-anonymity, (l, m, d) anonymity, and t-closeness, which are vulnerable to different privacy attacks discussed in this paper. The proposed technique called (p+, α, t)-anonymity aims to anonymize the data in such a way that even though intruder has sufficient background knowledge on the target individual he will not be able to infer anything and breach private information. The anonymized data also provide sufficient data utility by allowing various data analytics to be performed.

k-Anonymity

Data publishing

Data anonymization

Privacy software

Information sensitivity

Private information retrieval

10.4018/ijisp.2021040104

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Citations (4)