Structural Health Monitoring Using Machine Learning Techniques
Activation function; Artificial Neural Networks; Decision Tree; K-Nearest Neighbor; Logistic Regression; Machine learning algorithms; Random Forest
Environmental factors, particularly vibrations and temperature can damage the structural health of the building. To avoid heavy damage to the building and to maintain the building's structural health this paper suggests monitoring of building using machine learning algorithms. Machine learning algorithms are used to predict temperature and vibration damages in buildings. Temperature and vibration values are obtained through the grove vibration sensor and NTC thermistor attached to Raspberry Pi 3B plus. In the Raspberry pi, Machine learning algorithms are executed. The activation functions used are Relu, Sigmoid, and Tanh. The experimental results reveal that the Sigmoid activation function gives the best results in terms of metrics with accuracy 94.25, Precision 0.951, Recall 0.912, and F1 score 0.388. The sigmoid function is used in machine learning algorithms for predicting temperature and vibrations. Predicted temperature and vibrations damages are sent to the server and viewed through the user mobile. K- Nearest Neighbor algorithm produced best results with an accuracy rate of 85.50, Precision of 0.922, Sensitivity of 0.830, Specificity of 0.840 and F1 score of 0.873. 2023 IEEE.
Monisha P.; Arunraja A.; Judeson Antony Kovilpillai J.
Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, pp. 275-282.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICPCSN58827.2023.00052 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175235164&doi=10.1109%2fICPCSN58827.2023.00052&partnerID=40&md5=280b3f33e5dbc6c6fecaee370cc44dcc
Monisha P., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India; Arunraja A., Christ University (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Judeson Antony Kovilpillai J., Sri Ramakrishna Engineering College, Department of Electronics and Communication Engineering, Coimbatore, India
Analysis of Fraud Prediction and Detection Through Machine Learning
Genetic Algorithm (GA); Insurance fraud; K-means; Machine Learning
In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE.
Upreti K.; Sharma A.; Khatri V.; Hundekari S.; Gautam V.; Kapoor A.
2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Ghaziabad, India; Sharma A., Dr. Akhilesh das Gupta Institute of Technology and Management, Department of Cse, Delhi, India; Khatri V., Bpit, Department of Cse, Delhi, India; Hundekari S., Mit Adt University, Pune, India; Gautam V., University of Warwick, United Kingdom; Kapoor A., Department of Cse, Maharaja Surajmal Institute of Technology, Delhi, India
Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
Deep learning; Inception-V3; Malware; ResNet-50; Transfer learning; VGG-16
The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Jeyalakshmi V.S.; Krishnan N.; Jayapriya J.
Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 39-51.
Springer Science and Business Media Deutschland GmbH
Jeyalakshmi V.S., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Krishnan N., Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India; Jayapriya J., Department of Computer Science (YPR Campus), Christ University, Bangalore, India
Development and Evaluation of an Artificial Intelligence-Based System for Pancreatic Cancer Detection and Diagnosis
Artificial intelligence; Deep learning; Machine learning; Pancreatic cancer; Validation studies
Due to its aggressive nature and late-stage manifestation, pancreatic cancer is a difficult illness to find and diagnose. The creation of a pancreatic cancer detection and diagnosis system based on artificial intelligence (AI) has the potential to increase early detection and improve treatment results. We have described the creation and assessment of an AI-based system in this paper that is intended for the identification of pancreatic cancer. A large dataset including a variety of medical pictures, including CT scans, MRI scans, and PET scans, as well as the related clinical information, was gathered for the study. With the help of the annotated dataset, a deep learning model built on convolutional neural networks was created. The proposed AI-based solution was then assessed using a separate test dataset made up of control cases and known pancreatic cancer patients. A significant effectiveness for the early diagnosis of the disease was shown by the systems excellent precision as well as sensitivity in identifying pancreatic tumors. The outcomes of this investigation demonstrate the promise of AI-based systems for pancreatic cancer detection and diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Upreti K.; Mittal S.; Vats P.; Haque M.; Pawar V.; Haque M.
Communications in Computer and Information Science, Vol-1920, pp. 26-38.
Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Mittal S., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Vats P., SCSE, Manipal University Jaipur, Rajasthan, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Pawar V., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Patil Vidyapeeth (Deemed to be University), Pune, India; Haque M., National Medical College and Teaching Hospital, Birganj, Nepal
An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
CNN; Deep Learning; Diabetes mellitus (DM); ENCODE; epigenomic; FACS; GWAS
Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE.
https://doi.org/10.1109/NMITCON58196.2023.10276014 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175398757&doi=10.1109%2fNMITCON58196.2023.10276014&partnerID=40&md5=216f1165d6765a6531806be3dceebc8c
Padmaja K., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Mukhopadhyay D., Christ (Deemed to Be University), Department of Cse, Bangalore, India
Machine Learning Observation on the Prediction of Diabetes Mellitus Disease
Diabetes; Machine Learning; Prediction; Training Model
Diabetes disease has become as one of the common syndromes in many of the age groups. Diabetes can result in high blood sugar levels, a heart attack, or heart disease. This is one of the fastest developing illnesses, and it requires regular care. After seeing the doctor and being diagnosed, the patient is typically compelled to obtain their reports. Because this procedure is time-consuming and costly, we have the option of using ML approaches to solve this problem. Our research aims to foster a framework prepared to do all the more precisely foreseeing a patient's diabetes risk level. To develop models, classification methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Random Forest Classifier are employed. The results indicate that the techniques are quite accurate. The result showed that the prediction with the Logistic Regression model acquired the highest accuracy. 2023 IEEE.
Kumar P.D.; Priya S.L.; Kumar K.P.; Rego A.M.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Kumar P.D., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Priya S.L., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kumar K.P., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Rego A.M., CHRIST (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
Character Recognition; Computer Vision; Deep Learning; Pattern Recognition
Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE.
Sharma A., CHRIST(Deemed to Be University), Department of Computer Science, and Engineering, Bangalore, India; Mithun B.N., CHRIST(Deemed to Be University), Department of Computer Science, and Engineering, Bangalore, India
Node Overlapping Detection for Draggable Node-Based Applications
Node-based interfaces are user interfaces that are based on the concept of nodes, which represent individual units of functionality, and edges, which represent the connections between nodes. In a node-based interface, nodes are connected by edges to form a graph, which represents the data flow and relationships between different parts of the system. The Node overlapping detection technique is only for react flow version 11 and higher. Users having previous versions are not able to use that functionality. To detect the overlapping, based on the output of this library, several user-defined functions can be used to resolve to overlap. It will see the single-pixel overlap. Using this library, users can avoid Node and edge overlapping by creating custom edges. It is a simple JavaScript function currently used for reactjs. In the future, if any other script develops a draggable node-based flowsheet-creating feature, the user can use this library accordingly. 2023 IEEE.
Pobaru M., Computer Science and Engineering CHRIST (Deemed to Be University), Bangalore, India; Gokulapriya R., Computer Science and Engineering CHRIST (Deemed to Be University), Bangalore, India
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
Artificial intelligence; blockchain technologies; E Healthcare; Internet of Healthcare Things (IoHT); Ma-chine learning; Random Forest; Support vector machines component
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE.
Upreti K.; Vats P.; Borkhade G.; Raut R.D.; Hundekari S.; Parashar J.
2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023 - Proceedings, pp. 196-201.
Upreti K., CHRIST(Deemed to Be University), Dept. of Computer Science, Delhi NCR, Ghaziabad, India; Vats P., Scse Manipal University Jaipur, Dept. of Cse, Rajasthan, Jaipur, India; Borkhade G., Bharati Vidyapeeth College of Engineering, Dept. of Electronics & Telecommunication Engg, Maharashtra, Navi Mumbai, India; Raut R.D., Sant Gadge Baba Amravati University, Pgtd Applied Electronics, Maharashtra, Amravati, India; Hundekari S., Mit Adt University Loni Kalbhor, Pune, Pune, India; Parashar J., Adgitm, Dept. of Computer Science & Engg., New Delhi, India
Impact of AI in Financial Technology- A Comprehensive Study and Analysis
5G/6G; Artificial Intelligence; Digital twin; Fintech; IoT; Metaverse
Presently across the world, financial institutions strive tremendously hard to make financial services smarter to benefit from the advantages of digitization. To enhance client services, financial technology (Fintech) uses a variety of modern breakthrough technologies, including Artificial Intelligence (AI), 5G/6G, Blockchain, Metaverse, IoT, and others, in the financial sector. Many important financial services and procedures, including loans, authentication, fraud detection, quality control, creditworthiness, and several more, would be streamlined and improved by the adoption of technology. However, a need exists for the development of innovative financial products as well as the corresponding technological ecosystem. To launch Information and Communication Technology (ICT) alternatives, various major tech companies have placed their emphasis on Fintech. In this paper, we first explore the latest opportunities in Fintech. Furthermore, we also attempt to present a foundation of the Fintech accelerators, such as IoT, 5G, Digital twins, and Metaverse. Additionally, we also outline recommendations for future research directions in Fintech while looking forwards to potential difficulties. 2023 IEEE.
Ajmani P.; Sharma V.; Sharma S.; Alkhayyat A.; Seetharaman T.; Boulouard Z.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 985-991.
Ajmani P., Vivekananda Institute of Professional Studies- TC, New Delhi, India; Sharma V., CHRIST (Deemed to Be University) Delhi-NCR, India; Sharma S., Vivekananda Institute of Professional Studies- TC, New Delhi, India; Alkhayyat A., College of Technical Engineering the Islamic University, Najaf, Iraq; Seetharaman T., Acharya Institute of Technology, Department of AIML, Bangalore, India; Boulouard Z., University of Cassblanca, Morocco
Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization
Logistics transportation; Multimodal transport model; Particle swarm optimization
With the rapid improvement of market economy and modern logistics technique, the logistics distribution link is receiving more and more attention, and the logistics distribution path question in distribution has become the core question in logistics distribution. Study the optimization of logistics distribution path. Logistics distribution path optimization needs to find an optimal distribution route with less distribution vehicles and the shortest total length of the path, and has the rapidity of distribution. The traditional algorithm takes a long time to search the optimal route, which makes it difficult to find the optimal distribution route, resulting in high logistics distribution costs. In order to quickly find the optimal distribution route and improve the quality of logistics service, a logistics model based on particle swarm optimization algorithm is proposed. The group is composed of several non-intelligent individuals or groups of individuals. Each individual's behavior follows certain simple rules and has no intelligence; Individuals or groups of individuals can cooperate to solve questions through certain principles of message exchange, thus showing the behavioral characteristics of collective intelligence. After research, the algorithm in this paper is effective and suitable for wide application in practice. 2023 IEEE.
Babu K.; Alawady A.H.; Kaur I.; Subramani K.; Ahila R.; Gunapriya B.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 814-818.
https://doi.org/10.1109/SmartTechCon57526.2023.10391805 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184795373&doi=10.1109%2fSmartTechCon57526.2023.10391805&partnerID=40&md5=9880085ec19eade067740c00c5a0531d
Babu K., Department of Computational Intelligence, SRM Institute of Science & Technology, Tamilnadu, Kattankulathur, India; Alawady A.H., College of Technical Engineering, The Islamic University, Najaf, Iraq; Kaur I., GCET, Greater Noida, India; Subramani K., School of Business and Management, CHRIST (Deemed to be University) Bangalore, Yeshwanthpur Campus, Karnataka, Bangalore, India; Ahila R., New Prince Shri Bhavani College Of Engineering And Technology, Tamilnadu, Chennai, India; Gunapriya B., Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bangalore, India
Optimal allocation algorithm of marketing resources based on improved random forest
allocation algorithm; Improved random forest; marketing; resource optimization
Random Forest algorithm is an ensemble learning algorithm that classifies data by combining multiple decision trees. It has a wide range of applications and is not easy to overfit. It has a wide range of applications in medicine, bioinformatics, management and other fields. By studying the Cobb-Douglas sales function, it is found that it can only analyze the static allocation of marketing resources, but cannot describe the dynamic changes. Enterprise marketing resource management runs through the enterprise management from beginning to end. The research on marketing resource management is helpful for enterprises to grasp and control the whole process of marketing resource management from the overall and overall level, and has important theoretical value and reality for enterprise marketing management activities. significance. In the vast majority of enterprises in our country, the size of advertising promotion expenses and the number of salesmen is often determined based on the experience and subjective assumptions of decision makers, so it is difficult to say that they are optimized. This paper starts with determining the optimal advertising budget and the number of salespeople, and conducts applied research on the optimal allocation of marketing resources. 2023 IEEE.
Winster Praveenraj D.D.; Hussein L.; Tomar R.S.; Margarat G.S.; Vimala M.; Krishnaraj V.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 809-813.
https://doi.org/10.1109/SmartTechCon57526.2023.10391496 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184795494&doi=10.1109%2fSmartTechCon57526.2023.10391496&partnerID=40&md5=700778fe838e647cd6c374a5aadf4642
Winster Praveenraj D.D., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Hussein L., College of Technical Engineering, The Islamic University, Najaf, Iraq; Tomar R.S., Department of Computer Science & Engineering, IES College Of Technology, MP, Bhopal, 462044, India; Margarat G.S., New Prince Shri Bhavani College Of Engineering And Technology, India; Vimala M., Department of Electronics and Communication Engineering, P.S.R Engineering College, Tamilnadu, India; Krishnaraj V., Department of Mathematics, Sona College of Technology, Tamilnadu, Salem, India
Application of CNN and Recurrent Neural Network Method for Osteosarcoma Bone Cancer Detection
Cancer Stem Cell (CSC); Convolutional Neural Network (CNN); Recurrent Neural Network (RNN)
The outlook for people with metastatic osteosarcoma at an advanced stage is poor. Osteosarcoma is the most frequent form of bone cancer in children and young adults. There is an urgent need for both advances in treatment tactics and the identification of novel therapeutic targets for osteosarcoma since the disease typically develops resistance to existing treatments. Cancer stem cells, also known as tumor stem cells, have been linked to the development and spread of cancer at multiple points in the disease's progression. Cancer stem cells are linked to treatment resistance and carcinogenesis, and recent studies have demonstrated that osteosarcoma shares these properties. The proposed methodology rests on the three pillars of preprocessing, feature extraction, and model training. During preprocessing, that the proposed approach eliminated isolated highlights to help us zero in on the trustworthy region. They use the wavelet transform and the gray level co-occurrence matrix to extract features. A CNN-RNN technique is used to evaluate the models. In terms of output quality, the proposed technique is superior to both CNN and RNN. 2023 IEEE.
Nanthini S.; Sivabalan R.; Sivabalan S.; Bethapudi P.; Kukreti R.; Chauhan A.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 62-67.
https://doi.org/10.1109/ICSSAS57918.2023.10331794 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181060387&doi=10.1109%2fICSSAS57918.2023.10331794&partnerID=40&md5=eaae8115a1b545260ad3bf49bde43e39
Nanthini S., Saveetha School of Engineering, Department of Data Science, Chennai, Thandalam, India; Sivabalan R., Sri Ramakrishna College of Engineering, Department of Cse, Tamilnadu, Perambalur, India; Sivabalan S., Sri Ramakrishna College of Engineering, Department of Cse, Tamilnadu, Perambalur, India; Bethapudi P., Cse, Gitam School of Technology, Gitam Deemed to Be University, Visakhapatnam, India; Kukreti R., Graphic Era Deemed to Be University, Department of Hospitality Management, Uttarakhand, Dehradun, India; Chauhan A., School of Sciences, Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
AI Based Variable Step Size Block Least Mean Square Filter for Noise Cancellation System
Adaptive filter; Blocked Least Mean Square (BLMS); FIR filter; Normalized LMS
Most of the Active Noise Cancellation (ANC) systems working properly in low-frequency noises only. To make it suitable for isolating high-frequency noise, it needs an additional circuit which consumes more energy. This problem is mitigated in this study by designing a Variable Step size Block Least Mean Square (VSBLMS) filter which is suitable for an effective noise cancellation system. VSBLMS filter is designed with RCA to make a design area efficient and it is designed with a novel adder to achieve high speed as well as less energy consumption. The proposed filter is designed and simulated using Xilinx ISE 13.2. The simulation results shows that the proposed VSBLMS filter design mitigates the unwanted noises in various frequency bands. The proposed VSBLMS reduces the energy consumption by 9.32%, 27.63%, 13.53%, 11.80%, 10.71 %, 13.14% and 9.26% when compared with state of the art methods. 2023 IEEE.
International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 122-125.
Manikandan R., School of Business and Management Christ (Deemed to Be University), Department of Lean Operations and Systems, Karnataka, Bengaluru, India; Kumar K.S., Alliance University, Computer Science and Engineering, Alliance College of Engineering and Design, Karnataka, Bengaluru, India; Ramkumar S., School of Sciences, Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India
An Innovative Approach for Osteosarcoma Bone Cancer Detection based on Attention Embedded R-CNN Approach
Medical Image; Osteosarcoma; Residual Progressive Feature Extraction (RPFE)
The malignant bone tumor osteosarcoma. Any bone is at risk, but lengthy bones like the limbs are more vulnerable. Although the precise cause of this malignant growth is uncertain, experts concur that it is caused by changes to deoxyribonucleic acid (DNA) inside the bones. This can cause the breakdown of good tissue and the growth of aberrant, pathological bone. Osteosarcoma has a 76% cure rate if detected early and treated before it spreads to other parts of the body. An X-ray is the primary tool for detecting bone tumors. Bone X-rays and other imaging tests can help detect osteosarcoma. A biopsy should be performed for an accurate diagnosis. This is a time-consuming and tedious task that might be greatly reduced with the help of appropriate tools. Data preprocessing, segmentation, feature extraction, and model training are the four main pillars of the proposed approach. Unwanted noises can be filtered out with some preprocessing. Low-spatial-frequency and high-spatial-frequency components are separated using segmentation. The proposed approach employed Tumor Border Clarity, Joint Distance, Tumor Texture, and other features for feature extraction. Let's move on to A-Residual CNN model training. The success percentage of the proposed approach was 96.39 percent. 2023 IEEE.
Prasad B.M.G.; Bikash Acharjee P.; Guntakala S.; Sharma D.; Divya N.; Patil H.
International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings, pp. 1431-1436.
Prasad B.M.G., Ravindra College of Engineering for Women (RCEW, Autonomous), Department of Cse, Andhra Pradesh, Kurnool, 518452, India; Bikash Acharjee P., Christ University, Pune, India; Guntakala S., K.B.N College, Department of Mca, Andhra Pradesh, Vijayawada, India; Sharma D., Institute of Pharmacy, Assam Don Bosco University, Tapesi, Assam, India; Divya N., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Civil Engineering, Tamilnadu, Chennai, India; Patil H., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Maharashtra, Pune, India
Statistical Analysis of Ecological Mathematical Model Based on Data Warehouse
Data warehouse; ecological mathematical model; Ecosystem; statistical analysis
Persistence of ecosystems, existence and stability of periodic and almost periodic solutions, and global attractiveness are important research contents in ecological mathematical theory. This article takes the ocean as an example to illustrate. The marine ecological model management system integrates marine technology, Internet technology and database technology. The purpose is to collect, organize and analyze mathematical models related to marine ecosystems, integrate them according to certain classification principles, and store them in the form of text. In the database, the query of the database according to the important parameters in the mathematical model or the classification of the mathematical model is provided on the Internet, and the queried mathematical model is displayed on the screen through the browser. This paper adopts the method of data warehouse. How to effectively use resources is an important aspect of whether to take the initiative in competition. Data warehouse can play the characteristics of information processing and has broad application prospects in the face of competition in the field of telecommunications. 2023 IEEE.
Subramani K.; Abbas A.H.R.; Ansari N.; Jasmin M.; Vimala M.; Anandhi R.J.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1189-1193.
https://doi.org/10.1109/SmartTechCon57526.2023.10391642 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184817961&doi=10.1109%2fSmartTechCon57526.2023.10391642&partnerID=40&md5=9b59b5f6f72f7a3156987c05c134776c
Subramani K., School of Business and Management, CHRIST (Deemed to be University), Bangalore, Yeshwanthpur Campus, Karnataka, Bangalore, India; Abbas A.H.R., College of technical engineering, Islamic university, Najaf, Iraq; Ansari N., Department of Electronics and Communication, IES College Of Technology, MP, Bhopal, India; Jasmin M., New Prince Shri Bhavani College Of Engineering And Technology, Chennai, India; Vimala M., Department of Electronics and Communication Engineering, P.S.R Engineering College, India; Anandhi R.J., Department of Information Science and Engineering, New Horizon College of Engineering, Ring Road, Bellandur Post, Bengaluru, 560103, India
Unraveling Women's Involvement in the Digital Realm: An Empirical Investigation
cyber security; digital awareness; digital world; women security; women victims
A virtual world in which communication is done through the electronic medium using the computer. This world allows the user to gain knowledge in the form of information. Even though it has a lot of advantages, there are enormous issues when an individual exists in cyberspace. At hand are several challenges to be overcome by individuals to protectively survive cyberspace. Such as various attacks, financial risks, online crimes, and more. In cyberspace, the targeted audience is womanhood of all eons. Educating and promoting awareness about the risk in cyberspace for women in society is the need of the hour. Each individual is facing risk while they are in a digital world. Stakeholders are not given alertness of the threat and its consequences. The paper analyzes the risk and consequences of women's society, as most victims are from that environment. In this, different risks faced and the consequences affected by women's civilization, are discussed. Also remedial measures are taken and should be taken are also deliberated. Supporting this, an online survey is taken from various groups of common people to know the status of women's civilization in the current era. 2023 IEEE.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
https://doi.org/10.1109/ICCCNT56998.2023.10306918 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179846403&doi=10.1109%2fICCCNT56998.2023.10306918&partnerID=40&md5=9ff452a4ab0cef14770eb7960fef5cb7
Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India
Simulation of IoT-based Smart City of Darwin: Leading Cyber Attacks and Prevention Techniques
Cyber Attacks; Cybersecurity; Internet of Things; Network Simulation; Smart City; Vulnerabilities
The Rise of the Internet of Things (IoT) technology made the world smarter as it has embedded deeply in several application areas such as manufacturing, homes, cities, and health etc. In the developed cities, millions of IoT devices are deployed to enhance the lifestyle of citizens. IoT devices increases the efficiency and productivity with time and cost efficiency in smart cities, on the other hand, also set an attractive often easy targets for cybercriminals by exposing a wide variety of vulnerabilities. Cybersecurity risks, if ignored can results as very high cost to the citizens and management as well. In this research, simulated IoT network of Darwin CBD has been used with different IoT simulation tools. The treacherous effects of vulnerable IoT environment are demonstrated in this research followed by implementation of security measures to avoid the illustrated threats. 2023 IEEE.
Khan S.A.; Sundaram J.; Palendeng M.; Azam S.; Shanmugam B.
Khan S.A., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Sundaram J., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, India; Palendeng M., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Azam S., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia; Shanmugam B., Charles Darwin University, Energy and Resources Institute, Faculty of Science and Technology, Darwin, Australia
VLSI Implementation of Area-Error Optimized Compressor-Based Modified Wallace Tree Multiplier
Approximate Multiplier; Mean Error; Wallace Tree
Approximate multiplier designs can improve their energy efficiency and performance with only a slight loss in accuracy by using approximate arithmetic circuits. This method is appropriate for applications where an approximative answer is acceptable because it uses a range of calculation approaches to those priorities, returning a potentially erroneous result above one that is assured to be exact. The basic idea underlying approximate computing is that, while accurate calculation may require a lot of resources, bounded approximation can result in considerable speed and energy efficiency advantages without sacrificing accuracy. The approximate 4:2 compressor and exact compressors, as well as half adders and full adders, make up the proposed approximate multiplier. The steps of the multiplier architecture are optimised using the recently suggested modified Wallace Tree Multiplier Architecture. When compared to previous designs, the proposed multiplier architecture can generate outcomes with the least amount of inaccuracy. The multiplier architecture is also finished in just two steps. The Modified Wallace Tree Architecture used in the suggested approximate multiplier excels by providing an error rate of 71.80% and a mean error of 173.82. As a result, the mean ? error Product improved by 10%, the error rate improved by 23.3%, and the mean error increased by 31.04%. This is accomplished by the proposed approximate multiplier with a small increase of 22.36% in total power consumption. 2023 IEEE.
Balamanikandan A.; Suresh K.; Gnanaprakasam D.; Saranya K.
2023 2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023, pp. 353-358.
Balamanikandan A., Sree Vidyanikethan Engineering College, Department of ECE, Tirupati, India; Suresh K., Christ Deemed to Be University, Department of EEE, Bangalore, India; Gnanaprakasam D., Dr.Mahalingam College of Technology, Department of EEE, Pollachi, India; Saranya K., Dr.Mahalingam College of Technology, Department of EEE, Pollachi, India
Construction of Virtual Simulation Practice Teaching Platform for Business Majors Based on Fuzzy Control Algorithm
Fuzzy control; Teaching platform; Virtual simulation
Simulation plays an important role in control research. Information technology and various related technologies, the research of simulation technology is also deepening. At present, there is no unified platform for the design and simulation of adaptive fuzzy controller, and the simulation algorithms of various controllers are different. With the strong advocacy of national education departments, virtual simulation technology has been widely used in academic education, and has gradually become an important means to improve traditional teaching. Cross-professional comprehensive training of business has almost become the preferred course of combining theory with practice in general colleges and universities. It requires students from different majors to participate together, cooperate and communicate deeply in teams, and compete and confront each other among groups, which helps to improve graduates' innovative and entrepreneurial ability. Through teaching practice, the design of teaching system, the joint training between schools and enterprises, and the consideration of virtual and actual combat are further improved. Explain the teaching application of virtual simulation experiment teaching platform. The virtual simulation experiment teaching platform is convenient for students to complete intelligent control experiments, and carry out secondary development and innovative experiments. 2023 IEEE.
David Winster Praveenraj D.; Stanley Vincent G.; Jasim L.H.; Sankar Ganesh S.; Kavitha P.; Sridharan M.
2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 1433-1438.
https://doi.org/10.1109/SmartTechCon57526.2023.10391365 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184823564&doi=10.1109%2fSmartTechCon57526.2023.10391365&partnerID=40&md5=044d550888552c6e703a43d51bcb3de2
David Winster Praveenraj D., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Stanley Vincent G., School of Management Studies, Mount Carmel College Autonomous, Bengaluru, India; Jasim L.H., College of technical engineering, The Islamic university, Najaf, Iraq; Sankar Ganesh S., Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Ghanpur Village, Ghatesar(M), Malkajgiri District,Telangana State, Medchal, 501301, India; Kavitha P., New Prince Shri Bhavani College Of Engineering And Technology, Chennai, India; Sridharan M., Department of Mathematics, N.P.R College of Engineering and Technology, Dindigul Dist, Natham, India