A Potential Review on Self-healing Material Bacterial Concrete Methods and Its Benefits
subject: Bacterial concrete; Building construction; Self-healing
description: Building plays an important role for survival of human being in a safe place to live and store basic requirements. The building can be constructed for any purpose and the architecture of each building (official, residential) differs according to the plan. Beyond the plan for a building, it is also significant in designing plans for the construction of bridges, dams, canals, etc. In all the construction, the key goal is the strength of a building which completely depends on the materials that are chosen for each work. Hence, it is essential to prefer high quality materials for the construction of a building and the major materials are such as cement, concrete, steel, bricks, and sand. Among these materials, the concrete is often used for construction which enables to harden the building by combining cement, sand, and water. The concrete looks like a paste that reinforce to prolong life of the building. In this paper, we discuss a review on the use of bacteria in concrete that has the ability of self-healing cracks in the building. The procedural process behind the activation and reaction of bacteria into concrete is studied with the benefits of this process. This bacterial concrete usage assures to enhance the property of durability and but still it is yet to be introduced in the industries. Hereby, we review the recent research works undergone in concrete using bacteria. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
creator: Likhit M.L.; Alex J.
source: Lecture Notes in Civil Engineering, Vol-284, pp. 1087-1110.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23662557; ISBN: 978-303112010-7
format: Online
language: English
type: Conference paper
coverage: Likhit M.L., Department of Civil Engineering, Christ (Deemed to Be University), Bengaluru, India; Alex J., Department of Civil Engineering, Christ (Deemed to Be University), Bengaluru, India

Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
subject: AI Healthcare chatbots; Conceptual model; Indian Context; Intention to use
description: Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
creator: Nagarathinam A.; Chellasamy A.; Elangovan N.; Rengasamy S.
source: Lecture Notes in Networks and Systems, Vol-573 LNNS, pp. 657-666.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303120428-9
format: Online
language: English
type: Conference paper
coverage: Nagarathinam A., School of Business and Management, Christ University, Bengaluru, India; Chellasamy A., School of Business and Management, Christ University, Bengaluru, India; Elangovan N., School of Business and Management, Christ University, Bengaluru, India; Rengasamy S., School of Business and Management, Christ University, Bengaluru, India

Evaluation of Maximum Bending Stiffness of Stranded Cables with Refined Kinematic Relations
subject: Effective stiffness; Helical cable assembly; Kinematic relations
description: The mechanical response of a helically stranded cable depends on the effective stiffness offered by the collective assembly of its constituent wires. This can vary between two extreme conditions, namely a monolithic state, also known as the stickslip state, wherein all the wires in the cable behave as a single unit with no relative movements among themselves, offering the maximum stiffness for the cable. In the other extreme condition, all the wires are free to move among themselves, with no frictional holding among them, thus offering the minimum stiffness. This paper reviews the various mathematical models that are available for the estimation of maximum bending stiffness and brings out the need for considering a vital parameter known as the wire stretch effect that has been neglected by many authors till date. The consequent fundamental changes that occur in the basic kinematic relations are brought out and refined expressions for the internal wire forces and moments are established for the first time in the coupled axial-bending analysis. Further, the shear displacement of the wire due to the stretch has also been included in the wire normal and binormal shear forces. A single-layered cable with core-wire contact has been considered for analysis and the numerical results are evaluated with these new inclusions and are compared with the published results. It is hoped that the refined model suggested in this paper for the accurate estimation of the maximum stiffness, will pave way for more reasonable cable analysis in the subsequent slip stages. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Dulabhai H.P.; Parthasarthy N.S.; Hebbar G.S.
source: Lecture Notes in Mechanical Engineering, pp. 353-366.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 21954356; ISBN: 978-981194207-5
format: Online
language: English
type: Conference paper
coverage: Dulabhai H.P., CHRIST (Deemed to be University), Bangalore, 560074, India; Parthasarthy N.S., CHRIST (Deemed to be University), Bangalore, 560074, India; Hebbar G.S., CHRIST (Deemed to be University), Bangalore, 560074, India

Stability Analysis ofSalt Fingers forDifferent Non-uniform Temperature Profiles inaMicropolar Liquid
subject: Double diffusive convection; Micropolar fluid; Non-uniform temperature gradients; Salt finger
description: This paper describes the linear stability analysis of salt finger convection for different non-uniform temperature profiles by keeping the solutal concentration uniform throughout the system. The system consists of two parallel plates separated by a thin layer of micropolar liquid with infinite length, in which the system is heated and soluted from above the plate. Normal mode techniques are used to convert the system of partial differential equations into ordinary differential equations; further, Galerkian method is introduced to get the eigenvalue for isothermal, permeable with no-spin boundary conditions. The study also explains the effect of different micropolar parameters on the onset of convection. The phase of temperature flow for different boundary conditions explains the graphical solution of the energy equation and its gradients. It is shown that non-uniform temperature profiles, diffusivity ratio, coupling parameter, and solutal Rayleigh number influence the stability of the system. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Mary Daniel N.; Lingamneni V.; Tom T.; Arun Kumar N.
source: Lecture Notes in Mechanical Engineering, pp. 371-379.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 21954356; ISBN: 978-981191928-2
format: Online
language: English
type: Conference paper
coverage: Mary Daniel N., Research Scholar, Department of Mathematics, CHRIST(Deemed to be University), Bengaluru, India; Lingamneni V., Department of Mathematics, Sir MV PU College, Karnataka, India; Tom T., Department of Commerce and Management, CHRIST(Deemed to be University), Lavasa, India; Arun Kumar N., School of Engineering and Technology, CHRIST(Deemed to be University), Bengaluru, India

Applicability of Search Engine Optimization for WordPress (WP) Website
subject: AdWords; Information retrieval; P-Purchase; Quality Score; SERP
description: 91 percent of online experiences begin with a search, according to the Content Marketing Institute. That is the hunt for an explanation. As a result, search marketing is a critical practice for any businesses looking to grow and improve. Marketers and clients that paid for adverts began researching SEO and SEM at that time. This pursuit plans to give knowledge into the paid and unpaid procedures of search engine marketing (SEM) and what falls under its umbrella including search engine optimization (SEO) and pay per click (PPC). So in this exploration work, we feel the most ideal approach to utilize a web search tool SEM, is such a method of Internet showcasing that incorporates the utilization of web crawler result pages to advance business sites. SEM was earlier used as a protective gadget for anything to be done with the online search marketing field and it was girdled along with SEO. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Singh D.; Shivamurthaiah M.; Samanta D.; Bhattacharya A.; Dutta S.
source: Smart Innovation, Systems and Technologies, Vol-316, pp. 227-237.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981195402-3
format: Online
language: English
type: Conference paper
coverage: Singh D., Deptartment of CA, ITER, SOA Deemed to be University, Odisha, Bhubaneswar, India; Shivamurthaiah M., Department of Computer Science and Engineering, Brindavan College of Engineering, Bengaluru, India; Samanta D., CHRIST (Deemed to Be University), Karnataka, Bengaluru, India; Bhattacharya A., Institute of Engineering Management, Kolkata, India; Dutta S., Institute of Engineering Management, Kolkata, India

Parametric Study on Compaction Characteristics of Clay Sand Mixtures
subject: Artificial soils; Atterberg limits; Compaction characteristics; Natural soils; Percentage fines
description: The behaviour of fine-grained soils can be attributed to their mineral composition and the amount of fines present in them. The present study aims to determine the effect of mineral composition and quantity of fines on the Atterberg limits and compaction characteristics and to determine the correlation between them. Two types of fine-grained artificial soil mixtures were prepared in the laboratory representing kaolinitic and montmorillonitic mineral compositions.The amount of fines was varied at 10% intervals, from 50 to 100%. The Atterberg limits like liquid limit, plastic limit, shrinkage limit, and compaction characteristics like maximum dry density (MDD) and optimum moisture content (OMC) for two compaction energy levels, i.e. standard proctor (SP) and modified proctor (MP) tests, were determined. The correlations were developed between percentage fines and Atterberg limits and similarly between percentage fines, Atterberg limits, and compaction characteristics for artificial mix proportions. The developed correlations were used to predict the properties of natural soil samples, and the predicted and actual values are compared. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Yogeshraj Urs C.; Prasanna H.S.
source: Lecture Notes in Civil Engineering, Vol-296, pp. 141-152.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23662557; ISBN: 978-981196512-8
format: Online
language: English
type: Conference paper
coverage: Yogeshraj Urs C., Department of Civil Engineering, The National Institute of Engineering, Mysuru, 570008, India, Department of Civil Engineering, School of Engineering and Technology, Christ (Deemed to be University), Bangalore, 560074, India; Prasanna H.S., Department of Civil Engineering, The National Institute of Engineering, Mysuru, 570008, India

Dynamic response of parabolic reflector antenna subjected to shock load and base excitation considering soil-structure interaction
subject: Base excitation; Dynamic response; Response Spectrum; Soil Structure Interaction; Transient Response
description: Parabolic reflector antenna structures are subjected to dynamic loads along with normal loads. Determining the dynamic response of the antenna structure subjected to short-duration loads such as earthquake loads and shock loads considering soil-structure interaction is very important to ensure the safety and functionality of the antenna system resting on soft soil. A 7.2m diameter parabolic reflector antenna with a 90-degree elevation orientation is considered for the study. A triangular pulse of shock load is applied to the antenna at different locations and responses are estimated to understand the coupling effect of soil and structure on frequencies, damping, and response. Transient response analysis is carried out. Earthquake analysis is also carried out as per IS 1893 part 4:2016 considering Zone V site location. The foundation soil below the antenna is considered homogeneous with shear wave velocity (Vs) of 100m/sec. A direct method of analysis considering soil-structure interaction as per ASCE 4-16 is performed. FEM software MSC NASTRAN is used for analysis. The absorbing boundary conditions are used to reflect radiation damping. The depth-wise stress variation in foundation soil is evaluated. The results of free vibration analysis, transient response analysis with fixed base and SSI are compared. 2022 the Author(s).
creator: Gullapalli V.; Raghunandan K.R.; Reddy G.R.
source: Current Perspectives and New Directions in Mechanics, Modelling and Design of Structural Systems - Proceedings of the 8th International Conference on Structural Engineering, Mechanics and Computation, 2022, pp. 132-137.
publisher: CRC Press/Balkema
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-100334844-3
format: Online
language: English
type: Conference paper
coverage: Gullapalli V., Department of Civil Engineering, Christ University, Bangalore, India, ECIL, Hyderabad, India; Raghunandan K.R., Department of Civil Engineering, Christ University, Bangalore, India; Reddy G.R., BARC, HBNI, Mumbai, India

Machine Learning based Plant Disease Identification by using Hybrid Nae Bayes with Decision Tree Algorithm
subject: Artificial Intelligence; Convoluted Neural Networks; Decision Tree; Machine Learning; Nae Bayes
description: Artificial intelligence or machine learning as a domain started as a distinct domain marketplace for enthusiasts. Over an extended period of time, this has evolved into an industry with boundless potential. This is the focal point of a plethora of technologies like real-time analytics, deep learning in computer science. It's inherent to various customer needs such as fault detection, home automation, health monitoring devices as well as appliances, and multiple RPM devices Artificial intelligence which has been tested and trained to recognize and determine a plethora of flaws and inaccuracies. This could be intriguing procedures in day-to-day applications. An unimaginable number of prediction models, packages, libraries as well as sensors are utilized to sieve through flaws with the aid of mobile app development and other multispectral sensors. These trendy devices have become ever present and a part of our extensive routine. The demand for dependable and efficient algorithms is satisfied while implementing these devices. The objective primarily dictates emphasis on the prediction of plant diseases in the agricultural arena in reality by providing aid in the field of agriculture, and industry. In this case, the device incorporates a database which stores and keeps track of previously detected flaws or defects. In addition, the history of detected plant infections is maintained in an online repository. This can help with the forecast of the defects within the gadgets that are to be enhanced. Furthermore, the suggested approach of this text inculcates the invigilation of every leaf in the plant via machine learning model. Hence, this approach of implementation limits interaction of humans with the interface and it detects disease ridden plants efficiently with accuracy. The plant disease identification problem is to solve the proposed hybrid Nae Bayes with Decision Tree algorithm. The proposed model provides higher accuracy level compare to the regular model. 2023 IEE…
creator: Yetukuri N.K.; Maddali K.; Jayapandian N.
source: Proceedings - 5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023, pp. 852-858.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-166547467-2
format: Online
language: English
type: Conference paper
coverage: Yetukuri N.K., Christ (Deemed to Be University), Department of Cse, India; Maddali K., Christ (Deemed to Be University), Department of Cse, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, India

Hunter Prey Optimization for Optimal Allocation of Photovoltaic Units in Radial Distribution System for Real Power Loss and Voltage Stability Optimization
subject: Active power loss minimization; Distribution generation; Hunter-prey optimization; Radial distribution system; Voltage stability
description: Renewable Energy (RE) based Distribution Generation (DG), is a widely accepted eco-friendly alternative to conventional energy production. On the basic note, a DG is used to provide a part of or all of a customers real power demand and/or as a standby supply, and of all various existing types of DG technologies, Photovoltaic (PV) type distribution generation is considered for the study. The location of distributed generation is defined as the installation and operation of electric power generation modules connected directly to the distribution network or the network on the customer side of the meter, hence signifying the optimal location and size of the DGs used. This paper proposes a new algorithm of Hunter-Prey Optimization (HPO) to determine the optimal allocation of PV integration in the radial distribution systems (RDS). HPO is a new population-based algorithm inspired by the hunting behavior of a carnivore. The optimal sizing and siting of the PVs are determined by the proposed algorithm of HPO and are tested in MATLABR2021b on standard IEEE-33 and 69 test bus systems. On the basic of comparative study with literature, HPO is performed efficiently for solving multi-variable complex optimization problem. Also, the performance of RDSs is significantly improved with optimal PV allocations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Lahari P.S.; Janamala V.
source: Lecture Notes in Networks and Systems, Vol-608, pp. 817-825.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981199224-7
format: Online
language: English
type: Conference paper
coverage: Lahari P.S., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India; Janamala V., Department of Electrical and Electronics Engineering, School of Engineering and Technology, Christ (Deemed to be University), Karnataka, Bangalore, 560074, India

An Efficient Machine Learning Approach: Analysis of Supervised Machine Learning Methods to Forecast the Diamond Price
subject: Diamonds; Estimated values; Machine learning; Regression Models and Cross validation
description: Diamond, a found natural process compound of carbon, is one of the hardest and most immensely expensive material known to men, especially more to women. Investments in expensive gems like diamonds are in significant demand. The rate of a diamond, nevertheless, is not as easily calculated as the value of either gold or platinum since so many factors must be taken into account. Because there is such a broad range of diamond dimensions and qualities; as a result, being able to make reliable price predictions is crucial for the diamond industry. Although, making accurate predictions is challenging. In this study, we implemented multiple machine learning techniques employed to the challenge of diamond price forecasting's such as Linear Regression, Random Forest, Decision Tree Random Forest, Cat-Boost Regressor and XGB Regressor. This article's goal is to develop an accurate model for estimating diamond prices based on its characteristics such as weighting factor, cut grade, and dimensions. We compared the sum of estimated values and test values of predicted values with overestimated, underestimated and exact estimations. We applied cross-validation to calculate how much the model deviates from the actual when faced with a difference between the training set and the test set. We predicted values side by side. We performed a comparative analysis of supervised machine learning models with other models to evaluate the model accuracy and performance metrics. The Study's experimental findings show that out of all the supervised machine learning models, Random Forest performs well with R2score and Low RMSE and MAE values and CV Score. 2023 IEEE.
creator: Basha M.S.A.; Oveis P.M.; Prabavathi C.; Lakshmi M.B.; Sucharitha M.M.
source: 2023 International Conference for Advancement in Technology, ICONAT 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-166547517-4
format: Online
language: English
type: Conference paper
coverage: Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Oveis P.M., GITAM School of Business, Gandhi Institute of Technology and Management, Deemed to Be University, Bengaluru, India; Prabavathi C., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Lakshmi M.B., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India

Experimental Investigations on Turbine-Generator Shaft Under Subsynchronous Resonance
subject: Damper; Finite element method; Modal analysis; Rotor dynamics; Subsynchronous resonance; Turbo-generator shaft
description: Energy exchange takes place between turbine and generator in the power system during subsynchronous resonance (SSR) which leads to torsional interaction between the shafts. Resonance in the power system is caused by the series capacitors connected to the transmission line. This paper aims to present an electromechanical approach to analyse and interpret subsynchronous resonance using the Finite element method. Subsynchronous resonance is introduced in two test rigs consisting of turbine, generator, shaft, and coupler with capacitors. Experiments and simulations (torque analysis and frequency response analysis) are conducted in test rigs and ANSYS workbench 16.0. Moreover, a spring damper is modelled to improve the stability of the shaft. From the results, it is clear that mechanical stress is increased when capacitors are connected to the test rig. A spring damper is installed at the point where the deformation is high. The damper reduced the stress and the vibration. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Manikandan P.; Sushanth G.; Haneesh K.M.
source: Lecture Notes in Electrical Engineering, Vol-977, pp. 693-703.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 18761100; ISBN: 978-981197752-7
format: Online
language: English
type: Conference paper
coverage: Manikandan P., Department of EEE, CHRIST (Deemed to be University), Bengaluru, India; Sushanth G., Department of ECE, CHRIST (Deemed to be University), Bengaluru, India; Haneesh K.M., Department of EEE, CHRIST (Deemed to be University), Bengaluru, India

A Review of Deep Learning Methods in Cervical Cancer Detection
subject: Cervical Cancer; Deep Learning; Medical Image Classification
description: Cervical cancer is one of the most widespread and lethal malignancy that affects women aged 25 to 55 across the globe. Early detection of cervical cancer reduces burden of living and mortality drastically. Cervical cancer is caused through human papillomavirus transmitted sexually. Since the hereditary aspect is absent in cervical cancer, it can be cured completely if diagnosed early. Cervix cell image analysis is gold standard for classifying cervical cancer. Also known as pap smear, this histopathological test can provide dependable, and accurate diagnostic support. The current study examines the most recent research breakthroughs in deep learning models to classify cervical cancer. Three benchmark datasets are comprehensively described. Selective key classification models were implemented and comparative analysis was conducted on their performance. The findings of this study will allow researchers, publishers, and professionals to examine developing research patterns. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
creator: Lalasa M.; Thomas J.
source: Lecture Notes in Networks and Systems, Vol-648 LNNS, pp. 624-633.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303127523-4
format: Online
language: English
type: Conference paper
coverage: Lalasa M., CHRIST (Deemed to be University), Kengeri, Bangalore, 560075, India; Thomas J., CHRIST (Deemed to be University), Kengeri, Bangalore, 560075, India

SemKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization
subject: News recommendation; Ontology; Personalization; Transformers
description: The availability of digital devices has increased throughout the world exponentially owing to which the average reader has shifted from offline media to online sources. There are a lot of online sources which aggregate and provide news from various outlets but due to the abundance of content there is an overload to the user. Personalization is therefore necessary to deliver interesting content to the user and alleviate excessive information. In this paper, we propose a novel semantically inclined knowledge driven approach for multi-source aggregation and recommendation of news with a focus on personalization to address the aforementioned issues. The proposed approach surpasses the existing work and yields an accuracy of 96.62% 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
creator: Chhatwal G.S.; Deepak G.; Sheeba Priyadarshini J.; Santhanavijayan A.
source: Communications in Computer and Information Science, Vol-1788 CCIS, pp. 263-274.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 18650929; ISBN: 978-303127608-8
format: Online
language: English
type: Conference paper
coverage: Chhatwal G.S., Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Panjab University, Chandigarh, India; Deepak G., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India; Sheeba Priyadarshini J., Department of Data Science, CHRIST (Deemed to be University), Bangalore, India; Santhanavijayan A., Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tiruchirappalli, India

Gender Identification of Silkworm Pupa and Automated Cocoon Cutting Machine for Benefiting the Sericulture Grainages in Karnataka
subject: Cocoon Cutting Machine; Convolution Neural Networks; Efficient network; Grainages; VGG 16
description: Sericulture is the backbone of a mediocre farmer family in India. Sericulture provides a major financial support to the farmers with minimum infrastructure and maintenance. Farmers collect the seed cocoons from the grainages also known as seed factories. Grainages produce high quality seeds by mating the male and female cocoons. There is a huge demand of labor in these seed factories to process cocoons. The process includes deflossing, removal of pupa from the cocoon, gender identification, mating, storage of seeds, dispersal of eggs to farmers. Removal of pupa from the cocoon requires the labor to cut open a small portion of the cocoon and remove the pupa from inside. Presently in India, most of the grainages induce female laborers to perform the above job. Pupa is removed from the cocoon by cutting the cocoon using a stainless-steel blade. Each labour is given certain amount of cocoons to cut in a day. This requirement would force the laborers to perform the job at a higher speed which poses a threat of getting wounded by the blade. Hence the process of removal of pupa from the cocoon and sex identification of pupa to be automated. Thereby it is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. Bivoltaine hybrid race of silkworm namely FC1 and FC2 are the varieties under consideration for the research. Theses silkworm varieties are majorly used in grainages for seed production and hence the proposed machine was introduced. This semi automated cocoon cutting machine identifies the gender of the cocoon and later cut the required amount of cocoons minimally. This process would help in maintaining the maximum reliability of silk thread. Thereby the silkworm gender identification has to be non destructive. Image classification were done using Convolution Neural Network (CNN), Visual Geometry Group 16 (VGG16) and Efficient net methods, among which the latter produced highest accuracy. The Effic…
creator: Thomas J.; Reddy V.; Kumai K.; Aswathy S.U.
source: Lecture Notes in Networks and Systems, Vol-648 LNNS, pp. 372-381.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303127523-4
format: Online
language: English
type: Conference paper
coverage: Thomas J., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Reddy V., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Kumai K., Department of CSE, CHRIST (Deemed to be University), Bangalore, India; Aswathy S.U., Marian Engineering College, Kerala, Kazhakkoottam, India

Optimal Shortest Path Routing over Wireless Sensor Networks Using Constrained Genetic Firefly Optimization Algorithm
subject: Dynamic source routing (DSR); Firefly algorithm; Genetic algorithm; NS-2 simulator; Particle swarm optimization (PSO); Shortest path routing
description: In Wireless Sensor Networks (WSNs), a large number of sensor nodes are placed over a specific area in any real-life application. The sensor node is small, with limited battery life, memory, and computing capacity. Due to the limited power of the battery, WSNs must expand the system life by minimizing the energy usage. In the existing system, the methods have limitations related to optimal shortest routing path, node energy consumption, network reconfiguration, and so on. In order to overcome these issues, aConstrained Genetic FireFly Optimization Algorithm (CGFFOA) is proposed. The CGFFOA algorithm is designed to select the best shortest path routing through the selection of Cluster Head (CH) nodes based on the better energy utilization, delay, and high throughput sensor nodes. It is used to optimize the routing path based on the energy, hop count, inter and intra cluster delay, and lifetime. The simulation findings therefore conclude that, with regard to reduced energy consumption, higher throughput, and lower end-to-end delay, the proposed CGFFOA algorithm is preferable to existing methods such as Particle Swarm Optimization (PSO) and Dynamic Source Routing (DSR). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Arun Kokatnoor S.; Reddy V.; Krishnan B.
source: Lecture Notes in Networks and Systems, Vol-608, pp. 643-654.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981199224-7
format: Online
language: English
type: Conference paper
coverage: Arun Kokatnoor S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Reddy V., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Krishnan B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, India

An Effective Deep Learning Classification of Diabetes Based Eye Disease Grades: An Retinal Analysis Approach
subject: Classification; Deep learning; Densenet; Diabetes retinopathy; Feature extraction; Feature selection; Particle Swarm Optimization
description: Diabetic Retinopathy (DR) is a common misdiagnosis of diabetes mellitus, which damages the retina and impairs eyesight. It can lead to vision impairment if it is not caught early. Tragically, DR is an unbreakable cycle, and treatment only serves to reinforce the perception. Early detection of DR and effective treatment can significantly lower the risk of visual loss. In comparison to PC-aided conclusion frameworks, the manual analysis process used by ophthalmologists to diagnose DR retina fundus images takes a lot of time, effort, and money and is prone to error. As of late, profound learning has become quite possibly the most well-known procedure that has accomplished better execution in numerous areas, particularly in clinical picture examination and classification. Thereby, this paper brings an effective deep learning-based diabetes-based retinography in which the following are the stages: a) Data collection from MESSIDOR which contains 1200 images classified into 4 levels and graded from 03 followed by b) Preprocessing using grayscale normalized data. Then followed by c) feature extraction using Discrete Wavelet Transform (DWT), d) feature selection using Particle Swarm Optimization (PSO) and finally given for e) classification using Densenet 169. Experimental states that the proposed model outperforms and effectively classified grades compared to other state-of-art models (accuracy:0.95, sensitivity:0.96, specificity;0.97). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
creator: Ajesh F.; Jims A.; Alapatt B.P.; Philip F.M.
source: Lecture Notes in Networks and Systems, Vol-649 LNNS, pp. 234-244.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303127498-5
format: Online
language: English
type: Conference paper
coverage: Ajesh F., Department of Computer Science and Engineering, Sree Buddha College of Engineering, Kerala, Alappuzha, India; Jims A., JAIN (Deemed-to-be University), Bangalore, India; Alapatt B.P., CHRIST (Deemed to be University), Delhi-NCR, India; Philip F.M., JAIN (Deemed-to-be University), Bangalore, India

Artificial Intelligence in Disaster Management: A Survey
subject: Disaster management; Disaster mitigation; Disaster preparedness; Disaster recovery; Disaster response; Machine learning
description: This paper provides a literature review of cutting-edge artificial intelligence-based methods for disaster management. Most governments are worried about disasters, which, in general, are unbelievable events. Researchers tried to deploy numerous artificial intelligence (AI)-based approaches to eliminate disaster management at different stages. Machine learning (ML) and deep learning (DL) algorithms can manage large and complex datasets emerging intrinsically in disaster management circumstances and are incredibly well suited for crucial tasks such as identifying essential features and classification. The study of existing literature in this paper is related to disaster management, and further, it collects recent development in nature-inspired algorithms (NIA) and their applications in disaster management. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Arora S.; Kumar S.; Kumar S.
source: Lecture Notes in Networks and Systems, Vol-552, pp. 793-805.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981196633-0
format: Online
language: English
type: Conference paper
coverage: Arora S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Rajasthan, Jaipur, 303002, India; Kumar S., Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Rajasthan, Jaipur, 303002, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India

An Efficient Machine Learning Classification model for Credit Approval
subject: Bank Loan; Credit Approval; Logistic Regression.; Machine Learning; Random Forest
description: Credit authorization is a critical step for banks as well as every bank's main source of revenue is its line of credit. Thus, banks can profit from the loan interest they approve. Profitability or lost opportunity of a bank is highly dependent on loans that are whether consumers repay the debt or refuse. Loan collection is a significant factor in a bank's economic results. Forecasting the customer's ability to repay the loan in order to determine whether it should authorize or deny loan documents is a significant undertaking and a critical method in data analytics is being utilized to investigate the problem of loan default prediction: On the premise of assessment, the Logistic-Regression Classification Model, Random-Forest Classifier and Decision Tree Classification Models are compared. The mentioned classification algorithms were created as well as subsequently various evaluation metrics were obtained. By utilizing a suitable strategy, the appropriate clients for loan providing may be simply identified by assessing their probability of non-performing loans. This indicates that a bank really shouldn't simply prioritize wealthy consumers when giving loans, but it should also consider a client's other characteristics. This approach is critical in making credit judgments and forecasting default risk. 2023 IEEE.
creator: Dr.D.M.V. R.; Lakshmanarao A.; Gupta C.
source: Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023, pp. 499-503.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 978-166546216-7
format: Online
language: English
type: Conference paper
coverage: Dr.D.M.V. R., Aditya Engineering College, Department of It, Surampalem, India; Lakshmanarao A., Aditya Engineering College, Department of It, Surampalem, India; Gupta C., Christ University, School of Commerce, Finance and Accountancy, India

Soft Computing Approach for Student Dropouts in Education System
subject: Education; Educational technology; Soft computing
description: The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Sybol S.S.; Srivastava S.; Sharma H.
source: Lecture Notes in Networks and Systems, Vol-547, pp. 325-335.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981196524-1
format: Online
language: English
type: Conference paper
coverage: Sybol S.S., CHRIST (Deemed to be University), NCR, Delhi, India; Srivastava S., CHRIST (Deemed to be University), NCR, Delhi, India; Sharma H., Sheffield Hallam University, Sheffield, United Kingdom

Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
subject: GLCM; HOG; KNN; LBGLCM; LBHOG; LBP; SVM
description: In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Mallappa S.; Dhandra B.V.; Mukarambi G.
source: Lecture Notes in Electrical Engineering, Vol-967, pp. 113-124.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 18761100; ISBN: 978-981197168-6
format: Online
language: English
type: Conference paper
coverage: Mallappa S., Department of P.G.Studies and Research in Computer Science, Gulbarga University, Gulbarga, India; Dhandra B.V., Department of Statistics, Christ (Deemed to Be University), Bengaluru, India; Mukarambi G., School of Computer Science, Department of Computer Science, Central University of Karnataka, Gulbarga, India