Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
breast cancer; deep learning; histopathological images; mitosis cells; segmentation; YOLO-v5
Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors.
Journal of Advances in Information Technology, Vol-15, No. 10, pp. 1184-1192.
Lijo J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India, School of Computer Applications, Dayananda Sagar University, Karnataka, Bengaluru, India; Saleema J.S., Department of Statistics and Data Science, Christ University, Karnataka, Bengaluru, India
Efficient multipath model based cross layer routing techniques for Gauss Markov movable node management in MANET
This research unveils an innovative cross-layer routing methodology tailored for managing Gauss Markov mobile nodes within MANETs. The primary focus deceits cutting-edge inspiring network performance through the efficient utilization of resources and the steadfast maintenance of mobile node connectivity. Central to this model is the implementation of joint optimization, which takes into account both node mobility patterns and resource allocation dynamics to pinpoint the most favorable data transmission pathway. Incorporating multipath routing, the methodology enables the simultaneous exploration of multiple transmission routes, thereby fortifying the network against potential link failures and disruptions. By embracing a cross-layer approach, it seamlessly integrates functionalities across network, and steering layers, thereby amplifying the complete system efficacy. Comprehensive simulations conducted reveal the superior performance of this approach compared to existing techniques, particularly in terms of network throughput, latency reduction, and augmentation of packet delivery ratios. Such findings underscore the immense potential of this methodology across a spectrum of MANET applications that demand streamlined and dependable data transmission mechanisms. 2024 Author(s).
Musa A.I.A.; Sivakumar V.; Chandrasekharan N.; Priya T.M.; Kanna R.R.
AIP Conference Proceedings, Vol-3161, No. 1
https://doi.org/10.1063/5.0229502 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204024285&doi=10.1063%2f5.0229502&partnerID=40&md5=5da5eb5f70ea4a9594cc93c69ccadb45
Musa A.I.A., Department of Computer Science, College of Computer, Qassim University, Riyadh, Saudi Arabia; Sivakumar V., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Chandrasekharan N., School of Computing, Faculty of Computing Engineering and Technology, Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia; Priya T.M., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India; Kanna R.R., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
Efficient neighbour feedback based trusted multi authenticated node routing model for secure data transmission
Authenticated routing mode; MANET; Mobile ad hoc network; Neighbour feedback
The Mobile Ad Hoc Network (MANET) is a network that does not have a fixed infrastruc-ture. Migratory routes and related hosts that are connected via wireless networks self-configure it. Routers and hosts are free to wander, and nodes can change the topology fast and unexpectedly. In emergencies, such as natural/human disasters, armed conflicts, and emergencies, the lowest configuration will ensure ad hoc network applicability. Due to the rapidly rising cellular service requirements and deployment demands, mobile ad-hoc networks have been established in numerous places in recent decades. These applications include topics such as environmental surveillance and others. The underlying routing protocol in a given context has a significant impact on the ad hoc network deployment power. To satisfy the needs of the service level and efficiently meet the deployment requirements, developing a practical and secure MANET routing protocol is a critical task. However, owing to the intrinsic characteristics of ad hoc networks, such as frequent topology changes, open wireless media and limited resources, developing a safe routing protocol is difficult. Therefore, it is vital to develop stable and dependable routing protocols for MANET to provide a better packet delivery relationship, fewer delays, and lower overheads. Because the stability of nodes along this trail is variable, the route discovered cannot be trusted. This paper proposes an efficient Neighbour Feedback-based Trusted Multi Authenticated Node (NFbTMAN) Routing Model. The proposed model is compared to traditional models, and the findings reveal that the proposed model is superior in terms of data security. 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Bondada P.; Samanta D.; Chaudhry S.A.; Zikria Y.B.; Ishmanov F.
Sustainability (Switzerland), Vol-13, No. 23
https://doi.org/10.3390/su132313296 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120906906&doi=10.3390%2fsu132313296&partnerID=40&md5=8d82a0fc52d78d6668857de26c9c3c38
Bondada P., Dayananda Sagar Research Foundation, University of Mysore (UoM), Mysuru, 570005, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Mysuru, 570005, India, Department of Computer Science, CHRIST (Deemed to be) University, Bangalore, 560029, India; Chaudhry S.A., Department of Computer Engineering, Faculty of Engineering and Architecture, Istanbul Gelisim University, Istanbul, 34310, Turkey; Zikria Y.B., Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea; Ishmanov F., Department of Electronics and Communication Engineering, Kwangwoon University, Seoul, 01897, South Korea
EFFICIENT NON-DEGRADABLE WASTE PROCESSING TECHNOLOGIES INTEGRATED WITH MANETS FOR SUSTAINABLE WASTE MANAGEMENT MODELS
circular economy; integrated technologies; MANETs (Mobile Ad-hoc Networks); non-degradable waste; real-time monitoring; sustainable waste management
In order to handle the growing amount of non-biodegradable trash, creative and sustainable solutions are becoming more and more necessary as the global waste management challenge grows. To create a complete and sustainable waste management model, this investigation suggests a revolutionary approach that combines Mobile Ad-hoc Networks (MANETs) with effective non-degradable waste processing technology. Utilising cutting-edge waste processing technology that can efficiently handle non-biodegradable materials including plastic, e-waste, and other persistent pollutants is the main goal of this. With the goal of reducing their negative effects on the environment and advancing the concepts of circular economy, these technologies include sophisticated sorting systems, chemical treatments, and recycling procedures. Furthermore, the efficiency and real-time monitoring of waste processing processes are improved by the incorporation of MANETs into the waste management paradigm. MANETs enable smooth data transmission and communication between the central control centres, waste processing units, and monitoring sensors that make up the waste management system. Because of this connectedness, waste processing activities can be dynamically optimised, facilitating prompt resource allocation and decision-making. In addition to addressing the environmental issues raised by non-biodegradable garbage, the suggested paradigm advances the creation of intelligent and networked waste management systems. Because MANETs are used, the system is scalable and adaptable, making it appropriate for a variety of urban and rural areas. The model incorporates the Ant Colony Optimisation (ACO) algorithm for resource allocation. The integration of ACO optimises resource allocation, contributing to the reduction of environmental footprints associated with waste processing. The interconnectedness facilitated by MANETs, in conjunction with ACO, enables dynamic optimisation of waste processing operations, ensuring prompt resource allocation and decision-making. This investigation envisions a sustainable waste management model that minimises pollution, promotes resource recovery, and establishes a robust framework for addressing the growing challenges of non-degradable waste on a global scale by combining cutting-edge waste processing technologies with a strong communication infrastructure. The results of the investigation have a significant impact on waste management procedures by encouraging a more ecologically friendly and sustainable way to deal with non-biodegradable garbage. 2024, Scibulcom Ltd. All rights reserved.
Ganesh V.N.; Prathapa Reddy S.L.; Banumathi S.; Acharjee P.B.; Basha H.A.; Kumar S.S.; Rajaram A.
Journal of Environmental Protection and Ecology, Vol-25, No. 2, pp. 440-448.
Ganesh V.N., Department of Civil Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India; Prathapa Reddy S.L., Department of Electronics and Communication Engineering, KSRM College of Engineering, Andhra Pradesh, Kadapa, India; Banumathi S., Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, India; Acharjee P.B., CHRIST University Pune, Lavasa Campus, India; Basha H.A., Department of Computer Science, Saveeth College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Sciences, Chennai, India; Kumar S.S., Department of Information Technology, Sri Ramakrishna Mission Vidyalaya College of Arts and Science (Autonomous), Periyanaickenpalayam, Coimbatore, India; Rajaram A., Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Tamil Nadu, Nagapattinam, 611 002, India
Efficient one-pot green synthesis of carboxymethyl cellulose/folic acid embedded ultrafine CeO2 nanocomposite and its superior multi-drug resistant antibacterial activity and anticancer activity
Antibacterial activity; Biocompatibility; Carboxymethyl cellulose; CeO2; Folic acid
Due to the prevalence of drug-resistant bacteria and the ongoing shortage of novel antibiotics as well as the challenge of treating breast cancer, the therapeutic and clinical sectors are consistently seeking effective nanomedicines. The incorporation of metal oxide nanoparticles with biological macromolecules and an organic compound emerges as a promising strategy to enhance breast cancer treatment and antibacterial activity against drug-resistant bacteria in various biomedical applications. This study aims to synthesize a unique nanocomposite consisting of CeO2 embedded with folic acid and carboxymethyl cellulose (CFC NC) via a green precipitation method using Moringa oleifera. Various spectroscopic and microscopic analyses are utilized to decipher the physicochemical characteristics of CFC NC and active phytocompounds of Moringa oleifera. Antibacterial study against MRSA (Methicillin-resistant Staphylococcus aureus) demonstrated a higher activity (95.6%) for CFC NC compared to its counterparts. The impact is attributed to reactive oxygen species (ROS), which induces a strong photo-oxidative stress, leading to the destruction of bacteria. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CFC NC are determined as 600g/mL and 1000g/mL, respectively. The anticancer activity against breast cancer cell resulted in the IC50 concentration of 10.8?g/mL and 8.2?g/mL for CeO2 and CFC NC respectively.The biocompatibility test was conducted against fibroblast cells and found 85% of the cells viable, with less toxicity. Therefore, the newly synthesized CFC NC has potential applications in healthcare and industry, enhancing human health conditions. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Boopathi T.S.; Rajiv A.; Patel T.S.G.M.; Bareja L.; Salmen S.H.; Aljawdah H.M.; Arulselvan P.; Suriyaprakash J.; Thangavelu I.
Springer Science and Business Media Deutschland GmbH
ISSN: 16157591; PubMed ID: 39503917; CODEN: BBEIB
Boopathi T.S., Department of Chemistry, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India, Functional Materials Laboratory, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India; Rajiv A., Department of Physics, Faculty of Sciences, JAIN (Deemed-to-Be University), Karnataka, Bangalore, 562112, India; Patel T.S.G.M., Department of Community Medicine, Parul University, Gujarat, PO Limda, Tal. Waghodia, Vadodara, 391760, India; Bareja L., Centre of Research Impact and Outcome, Chitkara University, Punjab, Rajpura, 140417, India; Salmen S.H., Department of Botany and Microbiology, College of Science, King Saud University, PO Box-2455, Riyadh, 11451, Saudi Arabia; Aljawdah H.M., Department of Zoology, College of Science, King Saud University, PO Box-2455, Riyadh, 11451, Saudi Arabia; Arulselvan P., Department of Chemistry, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Tamil Nadu, Chennai, 602 105, India; Suriyaprakash J., Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510006, China; Thangavelu I., Department of Chemistry, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India
Efficient Power Conversion in Single-Phase Grid-Connected PV Systems through a Nine-Level Inverter
Efficiency; multilevel inverter; pulse width modulation; reduced switches; THD
In this paper, a novel nine-level inverter-based method for achieving efficient power conversion in single-phase grid-connected photovoltaic (PV) systems is proposed. The traditional two-level inverter has poor power quality and a high harmonic content. By using fewer power switches and adding more voltage levels, the proposed nine-level inverter gets around these restrictions, improving power conversion efficiency and lowering total harmonic distortion (THD). The effectiveness of the indicated technique for accomplishing better power quality and greater overall system efficiency is demonstrated by the simulation findings. A promising approach to improving the efficiency of single-phase grid-connected PV systems is the suggested nine-level inverter. 2023 IEEE.
Parimalasundar E.; Cheepati K.R.; Kamalkumar V.; Suresh K.; Devaraju T.; Kumar B.H.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 1-6.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/TEECCON59234.2023.10335776 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181532747&doi=10.1109%2fTEECCON59234.2023.10335776&partnerID=40&md5=4fdfea49f1ac8e13cc3e67f88db17c2c
Parimalasundar E., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Cheepati K.R., Kandula Sreenivasa Reddy Memorial College of Engineering, Electrical and Electronics Engineering, Kadapa, India; Kamalkumar V., KPR Institute of Engineering and Technology Coimbatore, Electrical and Electronics Engineering, Coimbatore, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India; Devaraju T., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Kumar B.H., Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India
Efficient Routing Strategies for Energy Management in Wireless Sensor Network
Energy efficiency; Routing protocols; Wireless sensor networks
Wireless Sensor Network (WSN) refers to a group of distributed sensors that are used to examine and record the physical circumstances of the environment and coordinate the collected data at the centre of the location. This WSN plays a significant role in providing the needs of routing protocols. One of the important aspects of routing protocol in accordance with Wireless Sensor Network is that they should be efficient in the consumption of energy and have a prolonged life for the network. In modern times, routing protocol, which is efficient in energy consumption, is used for Wireless Sensor Network. The routing protocol that is efficient in energy consumption is categorized into four main steps: CM Communication Model, Reliable Routing, Topology-Based Routing and NS Network Structure. The network structure can be further classified as flat/hierarchical. The communication model can be further classified as query, coherent/non-coherent, negotiation-based routing protocol system. The topology-based protocol can be further classified as mobile or location-based. Reliable routing can be further classified as QoS (Quality of service) or multiple-path based. A survey on routing protocol that is energy-efficient on Wireless Sensor Network has also been provided in this research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
Kocherla R.; Chandra Sekhar M.; Vatambeti R.
EAI/Springer Innovations in Communication and Computing, pp. 79-99.
Kocherla R., Department of CSE, Presidency University, Bangalore, India; Chandra Sekhar M., Department of CSE, Presidency University, Bangalore, India; Vatambeti R., Department of CSE, CHRIST (Deemed to be University), Bangalore, India
Efficient Ultra Wideband Radar Based Non Invasive Early Breast Cancer Detection
Breast phantom; Cole-Cole model; dielectric properties; early breast cancer detection; federal communication commission mask; scattering parameter; specific absorption rate; ultra wideband pulse shaper; ultra wideband radar
Ultra Wideband radar systems have emerged as a good alternative for non-invasive and harmless breast cancer detection. In this paper, bistatic and monostatic radar systems are proposed, which detects the deep-rooted and smallest formation of the tumor in the breast. The source signal for transmission through the breast is a seventh derivative Gaussian Ultra Wideband pulse. This pulse is shaped using the proposed sharp transition bandpass Finite Impulse Response filter. The pulse shaper filter design has a sharp transition, hence efficient for shaping very short-duration pulses, achieving higher data rate and less interference issues. Also, the pulse tightly fits the Federal Communication Commission spectral mask, thus achieving higher spectral utilization efficiency and meeting the signal safety standards for transmission through the breast. The shaped pulse fed to the antenna of the radar system provides higher antenna radiation efficiency and radiating power due to the concentration of power in the main lobe, sidelobe suppression, and less channel loss. Tumor detection is based on the time and frequency domain analysis of the backscattered signals from the tumor. These signals have higher amplitude, higher electric field intensity variations, and an increase in the scattering parameter values due to the presence of tumor. Simulation results show significant changes in the electric field intensity for normal and malignant breast tissue for tumor sizes ranging from 4 mm to 0.5 mm. To accurately detect the location of tumor inside the breast, Specific Absorption Rate (SAR) analysis is carried out. It is observed that the energy absorption in the cancerous breast is higher than that of the normal breast, thereby aids to detect the location of the tumor accurately by identifying the coordinates of the maximum value of SAR. The results obtained with an experimental setup consisting of fabricated heterogeneous breast phantom with tumor and monostatic radar closely confirms with the simulation results. 2013 IEEE.
https://doi.org/10.1109/ACCESS.2023.3303333 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167841317&doi=10.1109%2fACCESS.2023.3303333&partnerID=40&md5=29613c05fb5d0c04323a026b2e5bedb6
Devika Menon M., Christ (Deemed to Be University), Department of Electrical and Electronics Engineering, Bengaluru, 560074, India; Rodrigues J., Christ (Deemed to Be University), Department of Electrical and Electronics Engineering, Bengaluru, 560074, India
Effortless and beneficial processing of natural languages using transformers
68T05; 68T50; ALBERTA; Attention model; BERT; GPT3; NLP; RoBERTa; Sequential model encoder-decoder
Natural Language Processing plays a vital role in our day-to-day life. Deep learning models for NLP help make human life easier as computers can think, talk, and interact like humans. Applications of the NLP models can be seen in many domains, especially in machine translation and psychology. This paper briefly reviews the different transformer models and the advantages of using an Encoder-Decoder language translator model. The article focuses on the need for sequence-to-sequence language-translation models like BERT, RoBERTa, and XLNet, along with their components. 2022 Taru Publications.
Journal of Discrete Mathematical Sciences and Cryptography, Vol-25, No. 7, pp. 1987-2005.
Amrutha K., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN
And Double CNN; Deep Learning; Face Detection; Mask Detection; Real-time Monitoring
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemics impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who arent wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This studys proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Thamarai Selvi R.; Arulkumar N.; Ramasamy G.
Communications in Computer and Information Science, Vol-1973 CCIS, pp. 427-437.
Thamarai Selvi R., Master of Computer Applications, Bishop Heber College, Tiruchirappalli, India; Arulkumar N., Christ University, Bangalore, India; Ramasamy G., Christ University, Bangalore, India
Eggshells biowaste for hydroxyapatite green synthesis using extract piper betel leaf - Evaluation of antibacterial and antibiofilm activity
Antibacterial property; Antibiofilm activity; HAp composites; Hydroxyapatite; Piper betel leaf
The present research work reports the biosynthesis of hydroxyapatite (HAp) from eggshells and green synthesis of HAp from eggshells with incorporation of Piper betel leaf extract (PBL-HAp) using microwave conversion method. Although there are several works on synthesis of HAp from eggshells and other calcium and phosphorus rich substrates, the incorporation of herbal extract with HAp to promote antimicrobial and antibiofilm activity is less explored and reported. This research work highlights a simple and cost-effective method for development of antimicrobial biomaterials by combining the concepts of waste management, biomaterial science, and herbal medicine. In the present study, characterization of synthesized HAp was applied by X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy, and morphological analysis using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The characterization results indicated that the prepared HAp and PBL-HAp were pure b-type carbonated HAp. The PBL-HAp was checked for its antibacterial activity using the well diffusion method and biofilm inhibitory activity by crystal violet assay against some common pathogens. The antibacterial activities against Staphylococcus aureus and biofilm inhibitory activities against Escherichia coli, Vibrio harveyi, Pseudomonas aeruginosa, and Staphylococcus aureus of Piper betel leaf extract coated HAp (PBL-HAp) were showed to be significant and offered a promising role for the development of potent dental biomaterials. 2021 Elsevier Inc.
Umesh M.; Choudhury D.D.; Shanmugam S.; Ganesan S.; Alsehli M.; Elfasakhany A.; Pugazhendhi A.
ISSN: 139351; PubMed ID: 34129868; CODEN: ENVRA
https://doi.org/10.1016/j.envres.2021.111493 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107980241&doi=10.1016%2fj.envres.2021.111493&partnerID=40&md5=04efa0256aacc537bc5a4ecf6b4d45f5
Umesh M., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Choudhury D.D., Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Shanmugam S., Bioprocess Laboratory, Department of Microbial Biotechnology, Bharathiar University, Coimbatore, 641046, India; Ganesan S., Department of Safety, Health and Environmental Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City, 807, Taiwan; Alsehli M., Mechanical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Elfasakhany A., Mechanical Engineering Department, College of Engineering, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia; Pugazhendhi A., School of Renewable Energy, Maejo University, Chiang Mai, 50290, Thailand, College of Medical and Health Science, Asia University, Taichung, Taiwan
EGMM: removal of specular reflection with cervical region segmentation using enhanced Gaussian mixture model in cervix images
Cervical region; Colposcopy; Enhanced Gaussian mixture model; Mexican axolotl optimization; Specular reflection
Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance.Before segmenting the cervical region, specular reflection removal is an efficient one. Because, cervical cancer can be found using a visual check with acetic acid, which turns precancerous and cancerous areas whiteand these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-whiteareas and should therefore be removed. So, in this paper, specular reflection removal with segmentingthe cervix region ina colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican Axolotl Optimization (AMAO) algorithm. The performance of the proposed approach is analyzed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
https://doi.org/10.1007/s11042-024-19035-9 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190708576&doi=10.1007%2fs11042-024-19035-9&partnerID=40&md5=ecfbc67782ec0e407bb5d6de20ccc995
Mukku L., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India; Thomas J., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bangalore, India
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
higher education institution; Holistic development; holistic education; Mental Health; Quality Education aligned with human values; SDG; society; sustainable development goals
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society
ECS Transactions, Vol-107, No. 1, pp. 15337-15347.
https://doi.org/10.1149/10701.15337ecst https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130539036&doi=10.1149%2f10701.15337ecst&partnerID=40&md5=7b60173a3e0d78ed9e3d2a12d153ffd5
Goswami M., CHRIST (Deemed to be University), Bangalore, India; Balachandran K., CHRIST (Deemed to be University), Bangalore, India
Elastic circuit de-constructor: a pattern to enhance resiliency in microservices
circuit breaker; Cloud computing; cloud nativearchitecture; design patterns; microservices; resilience
Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel De-constructed state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging Availability, Latency and Error rate as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting. 2024 Informa UK Limited, trading as Taylor & Francis Group.
International Journal of Computers and Applications, Vol-46, No. 10, pp. 921-932.
Norton Stanley S.A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Sebastian S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
ELCCFD: An Efficient and Enhanced Credit Card Fraud Detection using Enhanced Deep Learning Principle
Credit Card Fraud Detection; Deep Learning AlexNet; Enhanced Learning for Credit Card Fraud Detection (ELCCFD); GBM
Credit card fraud poses a serious threat to financial institutions and their customers; hence, stringent detection protocols are necessary. This study introduces an approach known as Enhanced Learning for Credit Card Fraud Detection (ELCCFD) to enhance the accuracy of credit card fraud detection. To improve the fraud detection process, the proposed method combines the strengths of Convolutional Neural Networks (CNNs), AlexNet architecture, and Gradient Boosting Machines (GBM). The proposed approach begins with cleaning up the credit card data to get useful features, then trains a Convolutional Neural Network (CNN) using AlexNet to figure out complex patterns and representations on its own. This study generates a complete set of features by merging the CNN's output with features generated using GBM. The final model is trained by using a combination of deep learning and other conventional machine learning techniques to achieve the best results. Experimental findings on benchmark datasets demonstrate the effectiveness of the ELCCFD methodology, achieving an accuracy rate of 98%. This study combines AlexNet with GBM to get a model to capture the complex patterns and is easier to understand with the feature importance analysis. With its strong accuracy and reliability, the proposed methodology offers a strong option to fight credit card fraud, and it shows the potential for actual use in financial systems. 2024 IEEE.
Potluri P.; Suneel S.; Chandra B.; Muthukumaran D.; Upadhyaya M.; Rajesh Kanna R.
Proceedings - 2024 5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024, pp. 337-343.
https://doi.org/10.1109/ICIPCN63822.2024.00062 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204777453&doi=10.1109%2fICIPCN63822.2024.00062&partnerID=40&md5=91128030d116ea138ba95c0ce43de70e
Potluri P., Sitarampuram Narsapuram West Godavari district, department of CSE Swarnandhra college of engineering and technology, 534275, India; Suneel S., Institute of Aeronautical Engineering, Department of CSE(Data Science), Telangana, Hyderabad, India; Chandra B., Vignan's Institute of Information Technology(A), Department of Management Studies, AP, India; Muthukumaran D., Vel Tech Rangarajan Dr. Sagunthala, R&D Institute of Science and Technology, India; Upadhyaya M., University of Bahrain, College of Business Administration, Dept. of Management & Marketing, Bahrain; Rajesh Kanna R., CHRIST (Deemed to be University), Department of Computer Science, India
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
BJP; Election Prediction; Ensemble Learning; INC; JDS; Karnataka Election; Machine Learning; Political Analysis; Predictive Modelling; Sentiment Analysis
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE.
Gohil R.; Deepa S.; Vinay M.; Jayapriya J.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
https://doi.org/10.1109/AIKIIE60097.2023.10390333 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187269175&doi=10.1109%2fAIKIIE60097.2023.10390333&partnerID=40&md5=248596f3cf47caf9716119faaf24a5bb
Gohil R., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Deepa S., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India
Electric Vehicle Control and Driving Safety Systems: A Review
Control strategies; Electric vehicle; Electronic control unit; Optimisation techniques; Safety systems; Vehicle control unit; Vehicle safety
The relevance of Electric Vehicles (EVs) and the overall market demands of the respective control units is in a never before leap all around the globe as seen from the news, business studies, research trends and technological innovations today. Compared to earlier years, the relevance of driving safety in EVs also gains special attention due to the unforeseen surge in promoting EVs by National, State and City administrations for better environment and societal changes in future. For EV, the scenario broadens to a wider landscape beyond the earlier passive safety design features, to a highly comfortable and safer possible road travel. Safety enhancements can be experimented and implemented on EVs in a reliable way with higher end control of the dynamics, stability and optimised utilisation of individual vehicle characteristics and driver behaviours. In this paper, an attempt is made to scrutinise different control design approaches and possible solution paths experimented upon in the past and currently for EV as seen in the published literature. The quest is also to explore optimisation strategies in an organised way to ensure best possible driving safety along with passenger safety in EVs. 2023 IETE.
IETE Journal of Research, Vol-69, No. 1, pp. 482-498.
Indu K., Electronics and Communication Engineering, CHRIST (Deemed to be University), Bangalore, India; Aswatha Kumar M., Electronics and Communication Engineering, CHRIST (Deemed to be University), Bangalore, India
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE.
2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024
https://doi.org/10.1109/SEFET61574.2024.10718251 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208955425&doi=10.1109%2fSEFET61574.2024.10718251&partnerID=40&md5=1ef4507d29a496d2093ef17be0f3bdbc
Roy R., Christ University, Department of Electrical and Electronics Engineering, Bengaluru, India; Parag Jose C., Christ University, Department of Electrical and Electronics Engineering, Bengaluru, India
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799
Reshmi P.M., Kunjomana A.G., and Chandrasekharan K.A.
International Journal of Minerals, Metallurgy and Materials
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
electrical conductivity; gallium monotelluride; microhardness; physical vapour deposition; spherulites; whiskers
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg.
Reshmi P.M.; Kunjomana A.G.; Chandrasekharan K.A.
International Journal of Minerals, Metallurgy and Materials, Vol-20, No. 10, pp. 967-971.
University of Science and Technology Beijing
https://doi.org/10.1007/s12613-013-0822-4 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887922871&doi=10.1007%2fs12613-013-0822-4&partnerID=40&md5=e2b386ce2f8aa24b0c70aca4970cb23b
Reshmi P.M., Crystal Research Centre, Department of Physics, Christ University, Bangalore 560 029, India; Kunjomana A.G., Crystal Research Centre, Department of Physics, Christ University, Bangalore 560 029, India; Chandrasekharan K.A., Crystal Research Centre, Department of Physics, Christ University, Bangalore 560 029, India