Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques
breast cancer; deep CNN; DenseNet121; ensemble; fuzzy; Gompertzfunction; Inception V4; ResNet-164; VGG-11
Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis. 2022 by the authors.
Altameem A.; Mahanty C.; Poonia R.C.; Saudagar A.K.J.; Kumar R.
Multidisciplinary Digital Publishing Institute (MDPI)
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.3390/diagnostics12081812 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137404869&doi=10.3390%2fdiagnostics12081812&partnerID=40&md5=eccc4042fccd07e514cff67c3508b976
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Mahanty C., Department of Computer Science and Engineering, GIET University, Odisha 765022, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia; Kumar R., Department of Computer Science and Engineering, GIET University, Odisha 765022, India
Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
convolutional neurol networks; deep learning; higher grade gliomas; image segmentation; lower grade gliomas; voxel
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022.
Altameem A.; Mallikarjuna B.; Saudagar A.K.J.; Sharma M.; Poonia R.C.
Journal of Computational Biology, Vol-29, No. 6, pp. 530-544.
ISSN: 10665277; PubMed ID: 35235381; CODEN: JCOBE
https://doi.org/10.1089/cmb.2021.0280 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131665529&doi=10.1089%2fcmb.2021.0280&partnerID=40&md5=86242a97b59a579c63394dfb7f07852c
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi Arabia; Mallikarjuna B., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Sharma M., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
Biometric Authentication Framework; biometric data; fingerprint authentication; Hybrid AES (Advanced Encryption Standard) with Chaotic Map Encryption; Industry 4.0; Internet of Things; privacy; security
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors.
Altameem A.; P P.; T S.; Poonia R.C.; Saudagar A.K.J.
https://doi.org/10.3390/systems11010028 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146813957&doi=10.3390%2fsystems11010028&partnerID=40&md5=94f51ebc4f2867c823a7bde1187741e0
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; P P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; T S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets
Clustering approaches; K-histogram; K-means algorithm; ROCK; Unsupervised learning
Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable. 2023, Tech Science Press. All rights reserved.
Altameem A.; Poonia R.C.; Kumar A.; Raja L.; Saudagar A.K.J.
Intelligent Automation and Soft Computing, Vol-35, No. 1, pp. 553-566.
All Open Access; Hybrid Gold Open Access
Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, 11533, Saudi Arabia; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumar A., Department of Computer Engineering and Applications, GLA University, Mathura UP, India; Raja L., Department of Computer Application, Manipal University Jaipur, Rajasthan, 303007, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
Brain signal; Brain-computer interface; Convolutional neural networks; Hand motion recognition; Machine learning
Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, Dense-Net201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 2022 CRL Publishing. All rights reserved.
Altameem A.; Sachdev J.S.; Singh V.; Poonia R.C.; Kumar S.; Saudagar A.K.J.
Computer Systems Science and Engineering, Vol-42, No. 3, pp. 1095-1107.
https://doi.org/10.32604/csse.2022.023256 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124945126&doi=10.32604%2fcsse.2022.023256&partnerID=40&md5=3e9a4ac7e2155cc4705f1e8f9b8b6b63
Altameem A., Department of Computer Science, College of Applied Studies, King Saud University, Riyadh, 11495, Saudi Arabia; Sachdev J.S., Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, 303007, India; Singh V., Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, 303007, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Mathematical foundations based statistical modeling of software source code for software system evolution
Bidirection encoders transformers; Knowledge graphs; Requirements management; Software development; Transfer learning
Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling. 2022 the Author(s), licensee AIMS Press.
Althar R.R.; Alahmadi A.; Samanta D.; Khan M.Z.; Alahmadi A.H.
Mathematical Biosciences and Engineering, Vol-19, No. 4, pp. 3701-3719.
American Institute of Mathematical Sciences
https://doi.org/10.3934/mbe.2022170 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124794243&doi=10.3934%2fmbe.2022170&partnerID=40&md5=57925132fd06f3bc9a526f681401319f
Althar R.R., Department of Data Science, CHRIST University, Karnataka, Bangalore, India, QMS, First American India Private Ltd., Karnataka, Bangalore, India; Alahmadi A., Department of Computer Science and Information, Taibah University, Madinah, Saudi Arabia; Samanta D., Department of Computer Science, CHRIST University, Bangalore, India; Khan M.Z., Department of Computer Science and Information, Taibah University, Madinah, Saudi Arabia; Alahmadi A.H., Department of Computer Science and Information, Taibah University, Madinah, Saudi Arabia
The realist approach for evaluation of computational intelligence in software engineering
Computational intelligence; Data science; Software engineering; Software requirements management; Threat modeling
Secured software development must employ a security mindset across software engineering practices. Software security must be considered during the requirements phase so that it is included throughout the development phase. Do the requirements gathering team get the proper input from the technical team? This paper unearths some of the data sources buried within software development phases and describes the potential approaches to understand them. Concepts such as machine learning and deep learning are explored to understand the data sources and explore how these learnings can be provided to the requirements gathering team. This knowledge system will help bring objectivity in the conversations between the requirements gathering team and the customer's business team. A literature review is also done to secure requirements management and identify the possible gaps in providing future research direction to enhance our understanding. Feature engineering in the landscape of software development is explored to understand the data sources. Experts offer their insight on the root cause of the lack of security focus in requirements gathering practices. The core theme is statistical modeling of all the software artifacts that hold information related to the software development life cycle. Strengthening of some traditional methods like threat modeling is also a key area explored. Subjectivity involved in these approaches can be made more objective. 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
Innovations in Systems and Software Engineering, Vol-17, No. 1, pp. 17-27.
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/s11334-020-00383-2 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099971163&doi=10.1007%2fs11334-020-00383-2&partnerID=40&md5=dbde10a4b8d25faa1a3ed1efb4cba619
Althar R.R., Data Science Department, CHRIST (Deemed To Be University) and Specialist-QMS, American India Private Ltd, Bangalore, First, India; Samanta D., Computer Science Department, CHRIST (Deemed To Be University), Bangalore, India
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective
Bidirectional encoder transformers; Knowledge graphs; Requirements management; Software development; Transfer learning
Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes on Data Engineering and Communications Technologies, Vol-132, pp. 427-440.
Althar R.R., Data Science Department, CHRIST (Deemed to be University), Karnataka, Bangalore, India, Specialist-QMS, First American India Private Ltd., Karnataka, Bangalore, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be) University, Karnataka, Bangalore, India
Application of machine intelligence-based knowledge graphs for software engineering
This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations. 2021, IGI Global.
Methodologies and Applications of Computational Statistics for Machine Intelligence, pp. 186-202.
Althar R.R., Christ (Deemed to be University), QMS, First American India, Bangalore, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Computational statistics of data science for secured software engineering
The chapter focuses on exploring the work done for applying data science for software engineering, focusing on secured software systems development. With requirements management being the first stage of the life cycle, all the approaches that can help security mindset right at the beginning are explored. By exploring the work done in this area, various key themes of security and its data sources are explored, which will mark the setup of base for advanced exploration of the better approaches to make software systems mature. Based on the assessments of some of the work done in this area, possible prospects are explored. This exploration also helps to emphasize the key challenges that are causing trouble for the software development community. The work also explores the possible collaboration across machine learning, deep learning, and natural language processing approaches. The work helps to throw light on critical dimensions of software development where security plays a key role. 2021, IGI Global.
Methodologies and Applications of Computational Statistics for Machine Intelligence, pp. 81-96.
Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP
Security of the software system is a prime focus area for software development teams. This paper explores some data science methods to build a knowledge management system that can assist the software development team to ensure a secure software system is being developed. Various approaches in this context are explored using data of insurance domain-based software development. These approaches will facilitate an easy understanding of the practical challenges associated with actual-world implementation. This paper also discusses the capabilities of language modeling and its role in the knowledge system. The source code is modeled to build a deep software security analysis model. The proposed model can help software engineers build secure software by assessing the software security during software development time. Extensive experiments show that the proposed models can efficiently explore the software language modeling capabilities to classify software systems' security vulnerabilities. 2021 Raghavendra Rao Althar et al.
Althar R.R.; Samanta D.; Kaur M.; Alnuaim A.A.; Aljaffan N.; Aman Ullah M.
Computational Intelligence and Neuroscience, Vol-2021
All Open Access; Green Open Access; Hybrid Gold Open Access
https://doi.org/10.1155/2021/8522839 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122898212&doi=10.1155%2f2021%2f8522839&partnerID=40&md5=f0bb3c564264a93cba7fbfbf11b611d0
Althar R.R., Data Science Department, CHRIST Deemed to Be University, Karnataka, Bangalore, India, QMS, First American India Private Ltd., Karnataka, Bangalore, India; Samanta D., Department of Computer Science, CHRIST Deemed to Be University, Karnataka, Bangalore, India; Kaur M., School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea; Alnuaim A.A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh, 11495, Saudi Arabia; Aljaffan N., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh, 11495, Saudi Arabia; Aman Ullah M., Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, Bangladesh
Automated Risk Management Based Software Security Vulnerabilities Management
integrated security management system; machine learning; quantitative risk assessment; Quantitative threat modeling; software security
An automated risk assessment approach is explored in this work. The focus is to optimize the conventional threat modeling approach to explore software system vulnerabilities. Data produced in the software development processes are better leveraged using Machine Learning approaches. A large amount of industry knowledge around security vulnerabilities can be leveraged to enhance current threat modeling approaches. Work done here is in the ecosystem of software development processes that use Agile methodology. Insurance business domain data are explored as a target for this study. The focus is to enhance the traditional threat modeling approach with a better quantitative approach and reduce the biases introduced by the people who are part of software development processes. This effort will help bridge multiple data sources prevalent across the software development ecosystem. Bringing these various data sources together will assist in understanding patterns associated with security aspects of the software systems. This perspective further helps to understand and devise better controls. Approaches explored so far have considered individual areas of software development and their influence on improving security. There is a need to build an integrated approach for a total security solution for the software systems. A wide variety of machine learning approaches and ensemble approaches will be explored. The insurance business domain is considered for the research here. CWE (Common Weaknesses Enumeration) mapping from industry knowledge are leveraged to validate the security needs from the industry perspective. This combination of industry and company data will help get a holistic picture of the software system's security. Combining the industry and company data helps lay down the path for an integrated security management system in software development. The risk management framework with the quantitative threat modeling process is the work's uniqueness. This work contributes toward making the software systems secure and robust with time. 2013 IEEE.
Althar R.R.; Samanta D.; Kaur M.; Singh D.; Lee H.-N.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ACCESS.2022.3185069 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133705570&doi=10.1109%2fACCESS.2022.3185069&partnerID=40&md5=34390481d0a1b2825811ff60629c9d14
Althar R.R., Christ University, Data Science Department, Karnataka, Bangalore, 560029, India, First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Samanta D., Christ University, Department of Computer Science, Karnataka, Bangalore, 560029, India; Kaur M., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Singh D., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India; Lee H.-N., First American India Private Ltd., Qms, Karnataka, Bangalore, 560038, India
Statistical modelling of software source code
Data mining; Softwareentwicklung; Statistik
This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues. Statistic tool for the software engineer. 2021 Walter de Gruyter GmbH, Berlin/Boston.
Althar R.R.; Samanta D.; Konar D.; Bhattacharyya S.
Statistical Modelling of Software Source Code, pp. 1-342.
Althar R.R., First American India Private Ltd, Bangalore, India; Samanta D., CHRIST (Deemed to be University), Department of Computer Science, Hosur Road, Bhavani Nagar, 566003, India; Konar D., Sikkim Manipal Institute of Technology, Department of Computer Science and Engineering, East Sikkim, Majitar, Sikkim, 737136, India; Bhattacharyya S., Rajnagar Mahavidyala, Birbhum, India
Design and Development of Artificial Intelligence Knowledge Processing System for Optimizing Security of Software System
Bi-directional encoder representation for transformers; Knowledge processing; Software development; Software security; Software vulnerability
Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities. 2023, Crown.
Althar R.R.; Samanta D.; Purushotham S.; Sengar S.S.; Hewage C.
Althar R.R., CHRIST (Deemed to be) University, Karnataka, Bangalore, India, First American India Private Limited, Karnataka, Bangalore, India; Samanta D., CHRIST (Deemed to be) University, Karnataka, Bangalore, India, Department of Computational Information Technology, Rochester Institute of Technology-RIT Global, Germia Campus, Prishtina, Kosovo, 10000; Purushotham S., Department of Information Science, BMS Institute of Technology and Management, Karnataka, Bangalore, India; Sengar S.S., Department of Computer Science, Cardiff Metropolitan University, Cardiff, CF5 2YB, United Kingdom; Hewage C., Department of Computer Science, Cardiff Metropolitan University, Cardiff, CF5 2YB, United Kingdom
Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
forest fire; long short term memory; modified firefly algorithm; variational autoencoder; waterwheel plant optimization
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license.
Aluri Y.K.; Devi V.S.A.; Chinthakunta M.; Manur M.; Jayapal P.K.; Balan G.; Lella K.K.
International Journal of Safety and Security Engineering, Vol-14, No. 2, pp. 329-340.
International Information and Engineering Technology Association
https://doi.org/10.18280/ijsse.140202 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191979067&doi=10.18280%2fijsse.140202&partnerID=40&md5=da5ab8e8345813967a68be7e906ffac2
Aluri Y.K., Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, 520007, India; Devi V.S.A., Department of Chemistry, New Horizon College of Engineering, Bengaluru, 560103, India; Chinthakunta M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Jayapal P.K., DiSTAP, Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore; Balan G., Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru, 560103, India; Lella K.K., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India
Exploring the Use of the Therapists Self in Therapy: A Systematic Review
person of the therapist; Psychotherapy research; therapist factors; therapist self-disclosure; use of self in therapy
Purpose: This systematic qualitative review explored how psychotherapists use their self in therapy within the psychotherapy literature. It sought to examine the key documented ways through which the therapists self is intentionally used in therapy and the process of using the therapists self. Methods: Following PRISMA guidelines, databases including PubMed, ProQuest, APA PsycArticles, and APA PsycINFO were searched. The review question How do therapists use their self in therapy? guided the search using derivative keywords. Of the 149 screened articles, 20 underwent full-text review, and only four studies met inclusion criteria. Findings: All studies that met the inclusion criteria were from the West. Therapeutic self-disclosure (TSD) emerged as the primary way through which therapists used their self in therapynotably, the only way documented in the studies reviewed. Studies discussed the nature, rationale, influencing factors, and effectiveness of TSD. This article elaborates upon the themes from the reviewed studies. It critically examines existing literature, lists avenues for future research, and discusses implications for psychotherapy practice. Conclusions: The review underscores a significant gap in empirical qualitative research regarding therapists use of their self beyond TSD in therapy. There is an urgent need for further exploration in this domain. 2024 The Author(s).
Indian Journal of Psychological Medicine
Alva M.H., Dept. of Psychology, Christ University, Karnataka, Bangalore, India; Antony S.P., Dept. of Psychology, Christ University, Karnataka, Bangalore, India; Kataria K., School of Human Ecology, Tata Institute of Social Sciences, Maharashtra, Mumbai, India
A comprehensive survey on features and methods for speech emotion detection
classification methods; Emotion recognition; speech corpus; speech features
Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition. 2015 IEEE.
Proceedings of 2015 IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2015
https://doi.org/10.1109/ICECCT.2015.7226047 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954113948&doi=10.1109%2fICECCT.2015.7226047&partnerID=40&md5=79c9b7a62b66eec6d27063b52ea139b4
Alva M.Y., Department of Computer Science, Christ University, Bangalore, India; Nachamai M., Department of Computer Science, Christ University, Bangalore, India; Paulose J., Department of Computer Science, Christ University, Bangalore, India
Hybrid nanofluid flow over a vertical rotating plate in the presence of hall current, nonlinear convection and heat absorption
Hall current; Hybrid nanofluids; Laplace transforms method; Nonlinear convection; Unsteady flow
An exact analysis has been carried out to study a problem of the nonlinear convective flow of hybrid nanoliquids over a vertical rotating plate with Hall current and heat absorption. Three different fluids namely CuAl2O3H2Ohybrid nanofluid, Al2O3H2O nanofluid and H2O basefluids are considered in the analysis. The simulation of the flow was carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e., sphere, hexahedron, tetrahedron, column and lamina). The governing PDEs with the corresponding boundary conditions are non-dimensionalised with the appropriate dimensionless variables and solved analytically by using LTM (Laplace transform technique). This investigation discusses the effects of governing parameters on velocity and temperature fields in addition to the rate of heat transfer. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for twelve different hybrid nanofluids at 300 K is presented. The temperature profile of hybrid nanoliquid is larger than nanoliquid for same volume fraction of nanoparticles. Also, the glycerin-based nanoliquid has a high rate of heat transfer than engine oil, ethylene glycol and water-based nanoliquids in order. 2018 by American Scientific Publishers All rights reserved.
Journal of Nanofluids, Vol-7, No. 6, pp. 1138-1148.
https://doi.org/10.1166/jon.2018.1550 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047875321&doi=10.1166%2fjon.2018.1550&partnerID=40&md5=338b35bb41715d3a6cbd8d030c128194
Amala S., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Mahanthesh B., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India
Championing inclusion: Understanding lgbt diversity and social support in the workplace
acceptance; co-workers; diversity management; inclusion; LGBT; social support; supervisors
Purpose : This study investigated the impact of LGBT diversity management practices on the acceptance of LGBT employees by non-LGBT peers in Indian organizations. Based on classical social support theory and signaling theory, the study focused on how social support from co-workers and supervisors influenced this relationship. Methodology : Data were collected by surveying 546 employees across nine tech parks in the Indian IT/ITES sector. Partial Least Square (PLS) predictions and structural equation modeling (SEM) were conducted using Smart PLS version 4. Mediation and moderation analyses were also performed. Findings : The results exhibited that LGBT diversity management positively affected the acceptance of LGBT peers in the workplace (? = 0.298; t = 6.314; p = 0.00). Supervisor support was a complementary mediator (VAF = 0.33), while co-worker support moderated the association (? = 0.514; t = 15.916; p = 0.00). Practical Implications : The study presented managerial acumen regarding how social support from supervisors and co-workers enriched the efficacy of diversity management approaches. These outcomes were predominantly pertinent for organizations considering adopting an all-encompassing place of work for LGBT employees. Originality : This investigation delivered a distinctive offering by inspecting the role of social support in LGBT diversity management among the Indian IT segment. While based in Bengaluru, the study encouraged further investigation into additional businesses and geographies. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved.
Amancherla S.; Balasundaram S.; Basavaraj S.
Prabandhan: Indian Journal of Management, Vol-17, No. 12, pp. 64-82.
Associated Management Consultants Pvt. Ltd.
https://doi.org/10.17010/pijom/2024/v17i12/174057 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215274745&doi=10.17010%2fpijom%2f2024%2fv17i12%2f174057&partnerID=40&md5=ff6d3d3ee8e65cc125801e861d021e31
Amancherla S., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, 560 029, India; Balasundaram S., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, 560 029, India; Basavaraj S., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, 560 029, India
Time allocation between paid and unpaid work among men and women: An empirical study of indian villages
Agricultural labour markets; Employment; Gender; India; Occupation; Paidunpaid work; Time allocation; Wage structure
The paper examines the time allocation between paid work (wage earning or self-em-ployed work generally termed as employment work) and unpaid (domestic chores/care work generally termed as non-employment work) along with wage rates, imputed earnings, and occupational structure among men and women and according to different social groups to establish the extent to which the rural labour market is discriminated by sex and social group. The major objective of the paper is to show the differential in wage income between men and women in farm and non-farm activities. The paper also shows the division of time between employment and non-employ-ment activities by men and women. The paper uses high-frequency data and applies econometric techniques to know the factors behind time allocation among different activities across gender. The study finds that males spend more hours on employment work and work at a higher wage rate than females. As a result, a vast monetary income gap between men and women is observed, even though women worked more hours if employment and non-employment activities are jointly taken into consideration. Time spent on employment work and non-employment (mainly domestic chores) has been found to vary significantly due to social identity, household wealth, land, income, educa-tion, and skill. The segregation of labour market by sex was evident in this study, with men shifting to non-farm occupations with greater monetary returns and continued dependence on womens farm activities. Enhancing the ownership of land and other assets, encouraging womens participation particularly among minorities, and improving health are some of the policy recommendations directed from this study to enhance participation in employment work and shifting towards higher wage income employment. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Amarender Reddy A.; Mittal S.; Roy N.S.; Kanjilal-Bhaduri S.
Sustainability (Switzerland), Vol-13, No. 5, pp. 1-17.
https://doi.org/10.3390/su13052671 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102498527&doi=10.3390%2fsu13052671&partnerID=40&md5=9dfbff281900c428b6f050d1367ca821
Amarender Reddy A., Indian Council of Agricultural Research-Central Research Institute for Dryland Agriculture, Hyderabad, 500059, India; Mittal S., Agricultural Economics Research Association (AERA), New Delhi, 110012, India; Roy N.S., Department of Economics, Christ University, Bengaluru, 560029, India; Kanjilal-Bhaduri S., Department of Economics, University of Algarve, Faro, 8005139, Portugal