Model and Algorithm of Multimodal Transportation in Logistics Transportation Based on Particle Swarm Optimization
subject: Logistics transportation; Multimodal transport model; Particle swarm optimization
description: 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.
creator: Babu K.; Alawady A.H.; Kaur I.; Subramani K.; Ahila R.; Gunapriya B.
source: 2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023, pp. 814-818.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030541-8
format: Online
language: English
type: Conference paper
coverage: 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

Impact of AI in Financial Technology- A Comprehensive Study and Analysis
subject: 5G/6G; Artificial Intelligence; Digital twin; Fintech; IoT; Metaverse
description: 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.
creator: Ajmani P.; Sharma V.; Sharma S.; Alkhayyat A.; Seetharaman T.; Boulouard Z.
source: Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 985-991.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030448-0
format: Online
language: English
type: Conference paper
coverage: 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

An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
subject: Artificial intelligence; blockchain technologies; E Healthcare; Internet of Healthcare Things (IoHT); Ma-chine learning; Random Forest; Support vector machines component
description: 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.
creator: Upreti K.; Vats P.; Borkhade G.; Raut R.D.; Hundekari S.; Parashar J.
source: 2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023 - Proceedings, pp. 196-201.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835031487-8
format: Online
language: English
type: Conference paper
coverage: 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

Node Overlapping Detection for Draggable Node-Based Applications
subject: Node overlapping; react flow; Reactjs
description: 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.
creator: Pobaru M.; Gokulapriya R.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: 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

Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
subject: Character Recognition; Computer Vision; Deep Learning; Pattern Recognition
description: 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.
creator: Sharma A.; Mithun B.N.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: 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

Machine Learning Observation on the Prediction of Diabetes Mellitus Disease
subject: Diabetes; Machine Learning; Prediction; Training Model
description: 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.
creator: Kumar P.D.; Priya S.L.; Kumar K.P.; Rego A.M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: 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

An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
subject: CNN; Deep Learning; Diabetes mellitus (DM); ENCODE; epigenomic; FACS; GWAS
description: 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.
creator: Padmaja K.; Mukhopadhyay D.
source: 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030082-6
format: Online
language: English
type: Conference paper
coverage: Padmaja K., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Mukhopadhyay D., Christ (Deemed to Be University), Department of Cse, Bangalore, India

Development and Evaluation of an Artificial Intelligence-Based System for Pancreatic Cancer Detection and Diagnosis
subject: Artificial intelligence; Deep learning; Machine learning; Pancreatic cancer; Validation studies
description: 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.
creator: Upreti K.; Mittal S.; Vats P.; Haque M.; Pawar V.; Haque M.
source: Communications in Computer and Information Science, Vol-1920, pp. 26-38.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 18650929; ISBN: 978-303145120-1
format: Online
language: English
type: Conference paper
coverage: 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

Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
subject: Deep learning; Inception-V3; Malware; ResNet-50; Transfer learning; VGG-16
description: 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.
creator: Jeyalakshmi V.S.; Krishnan N.; Jayapriya J.
source: Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 39-51.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981993009-8
format: Online
language: English
type: Conference paper
coverage: 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

Analysis of Fraud Prediction and Detection Through Machine Learning
subject: Genetic Algorithm (GA); Insurance fraud; K-means; Machine Learning
description: 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.
creator: Upreti K.; Sharma A.; Khatri V.; Hundekari S.; Gautam V.; Kapoor A.
source: 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030082-6
format: Online
language: English
type: Conference paper
coverage: 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

Structural Health Monitoring Using Machine Learning Techniques
subject: Activation function; Artificial Neural Networks; Decision Tree; K-Nearest Neighbor; Logistic Regression; Machine learning algorithms; Random Forest
description: 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.
creator: Monisha P.; Arunraja A.; Judeson Antony Kovilpillai J.
source: Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023, pp. 275-282.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835032284-2
format: Online
language: English
type: Conference paper
coverage: 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

The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
subject: Artificial Neural Network(ANN); Decision Tree (DT); Gradient Boosting (GB); Logistic Regression (LR); Nae Bayes (NB); Network Intrusion Detection System (NIDS); Random Forest (RF); Support Vector Machine (SVM)
description: With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE.
creator: Varghese N.; Vivek R.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Varghese N., CHRIST (Deemed to Be University), Department of Computer Science, Bangalore, India; Vivek R., Krupanidhi Degree College, BCA Department, Bangalore, India

An Predictive Deep Learning Model is used to Identify Human Tissue-Specific Regulatory Variations For Diabetes
subject: Deep Learning; Genomic; Insulin; Regulatory; RESNET; T2D; TCF7L2; Tissue
description: A predictive deep learning model is designed to predict a target variable based on a set of input variables to diagnose the tissue base regulatory variants in the human islets. In this article, the identification on human tissue-specific regulatory variations for Diabetes using the Pima dataset converting data into images, and then the input variables may include genetic data, gene expression data, and the proposed model uses Pima Indian dataset with the attributes such as age, sex, and BMI to predict whether a person has Diabetes or not. And this dataset is incorporated a combination two layered ResNet18 + ResNet50 and SVM classifier. The results obtained are compared with KNN, Naive bayes, SVM Random Forest, Gradient descent and the accuracy achieved is 98%. 2023 IEEE.
creator: Padmaja K.; Mukhopadhyay D.
source: 2023 IEEE 4th Annual Flagship India Council International Subsections Conference: Computational Intelligence and Learning Systems, INDISCON 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033355-8
format: Online
language: English
type: Conference paper
coverage: Padmaja K., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Mukhopadhyay D., Christ (Deemed to Be University), Department of Cse, Bangalore, India

Design and Simulation of a Multi-purpose Adjustable Modular Robot for Precision Agriculture
subject: Agriculture; Design; GAZEBO; Modeling; Simulation
description: Global population growth, climate change, and labor shortages all represent substantial obstacles to meeting global food needs, and agricultural robots provide a possible solution. This work uses a survey to evaluate user behavior toward using agricultural wheel robots on small farms. The survey was conducted in various parts of India (Coimbatore, Bhubaneswar, and Silchar), where 250 large and medium commercial farmers participated. After the survey, a new robotic system architecture is a multi-purpose, adjustable, modular, and affordable robotic platform designed for precision agriculture. A unique feature is added to the design, which helps the robot to adjust by itself based on the row distances and crop heights. The software was designed using the Fusion 360, and simulation is carried out in GAZEBO and Robot Operating System (ROS). 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Raut R.S.; Mahanta G.B.; Rout A.; Pattanayak S.K.; Biswal B.B.
source: Smart Innovation, Systems and Technologies, Vol-342, pp. 915-927.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981990263-7
format: Online
language: English
type: Conference paper
coverage: Raut R.S., NIT Silchar, Assam, 788010, India; Mahanta G.B., Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Chennai Campus, Tamil Nadu, Chennai, 6001103, India; Rout A., Department of Mechanical Engineering, Christ University, Karnataka, Bangalore, 560029, India; Pattanayak S.K., NIT Silchar, Assam, 788010, India; Biswal B.B., Department of Industrial Design, NIT Rourkela, Odisha, 769008, India

Securing International Law Against Cyber Attacks through Blockchain Integration
subject: Blockchain; cybersecurity; data integrity; intrusion detection; PCA
description: Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity. 2023 IEEE.
creator: Acharjee P.B.; Kumar M.; Krishna G.; Raminenei K.; Ibrahim R.K.; Alazzam M.B.
source: 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023, pp. 2676-2681.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835039926-4
format: Online
language: English
type: Conference paper
coverage: Acharjee P.B., Christ University, Business and Management, Maharashtra, Pune, 412112, India; Kumar M., Lovely Professional University, Centre for Distance & Online Education, Punjab, Phagwara, 144001, India; Krishna G., Uttaranchal University, Uttaranchal Institute of Technology, Uttarakhand, Dehradun, 24800, India; Raminenei K., Sr University, School of Computer Science and Artificial Intelligence, Telangana, Warangal, 506371, India; Ibrahim R.K., Al-Farahidi University, Medical Technical College, Baghdad, 10021, Iraq; Alazzam M.B., Ajloun National University, Information Technology College, Ajloun, Jordan

Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm
subject: 'Inverse Effect'; Behavioral Finance; Earnings Call Transcripts; Earnings per Share; Efficient Market Hypothesis; Lexicon Based Sentiments Analysis; Market Performance; Polarity; Stock Price; Weak Market Hypothesis
description: This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE.
creator: Nagendra B.V.; Simha J.B.; Abhi S.; Kumar Chandar S.
source: 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023 - Proceedings, pp. 368-375.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034148-5
format: Online
language: English
type: Conference paper
coverage: Nagendra B.V., Christ (Deemed to Be University), Bengaluru, India; Simha J.B., Reva University, Reva Academy for Corporate Excellence, Bangalore, India; Abhi S., Reva University, Reva Academy for Corporate Excellence, Bangalore, India; Kumar Chandar S., Christ (Deemed to Be University), Bengaluru, India

Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection
subject: CNN (convoluted neural network); Computer Vision; Drone Detection; UAS (Unmanned Aerial System); unmanned aerial vehicles (UAVs); Yolo (You Only Look Once); Yolov7; Yolov8
description: Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications. 2023 IEEE.
creator: Agarwal K.; Ashik Sanyo M.S.; Bakshi S.; Vinay M.; Jayapriya J.; Deepa S.
source: 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835030082-6
format: Online
language: English
type: Conference paper
coverage: Agarwal K., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Ashik Sanyo M.S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Bakshi S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Vinay M., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Deepa S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India

Brain Tumor Detectin Using Deep Learning Model
subject: Brain-Tumor detection; Deep Leaning; DenseNet201; Inception V3; MobileNet; ResNet50
description: Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE.
creator: Menachery J.; Anand I.K.; Thiruthuvanathan M.M.
source: Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033577-4
format: Online
language: English
type: Conference paper
coverage: Menachery J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Anand I.K., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Thiruthuvanathan M.M., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India

Machine Learning Algorithms for Prediction of Mobile Phone Prices
subject: K-nearest neighbor; Machine learning; Mobile phone; Phone price prediction; Price range; Support vector machine
description: The drastic growth of technology helps us to reduce the man work in our day-to-day life. Especially mobile technology has a vital role in all areas of our lives today. This work focused on a data-driven method to estimate the price of a new smartphone by utilizing historical data on smartphone pricing, and key feature sets to build a model. Our goal was to forecast the cost of the phone by using a dataset with 21 characteristics related to price prediction. Logistic regression (LR), decision tree (DT), support vector machine (SVM), Naive Bayes algorithm (NB), K-nearest neighbor (KNN) algorithm, XGBoost, and AdaBoost are only a few of the popular machine learning techniques used for the prediction. The support vector machine achieved the highest accuracy (97%) compared to the other four classifiers we tested. K-nearest neighbors 94% accuracy was close to that of the support vector machine. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
creator: Jose J.; Raj V.; Seaban S.V.; Jose D.V.
source: Lecture Notes in Networks and Systems, Vol-537 LNNS, pp. 81-89.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2023-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-981993009-8
format: Online
language: English
type: Conference paper
coverage: Jose J., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Raj V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Seaban S.V., Department of Computer Science, Rajagiri College of Social Sciences, Kalamassery, India; Jose D.V., Department of Computer Science, Christ University, Bangalore, India

A Conceptual Framework for AI Governance in Public Administration - A Smart Governance Perspective
subject: Artificial Intelligence; Ethics; Public Administration; Regulation Theory; Smart Governance
description: With the public governance lagging behind the fast evolving of AI in their attempts to yield sufficient governance, corresponding principles are necessary to be in par with this dynamic advancement. As AI becomes more pervasive and integrated into various domains, there is a growing need for AI governance models that can ensure that the development and deployment of AI systems align with ethical, legal, and social standards. There are some answers that literature puts forward to the question onthe way the government and public administration has to react to the huge concerns related to AI and usage of policies to avoid the emerging challenges. In this survey, AI problems and the prior AI regulation techniques are analyzed. In this research study, a governance model for AI is proposed by combining all the facets and also implements a new procedure for governing AI. This study will help the decision makers to make smart government a reality by using AI governance framework. 2023 IEEE.
creator: Rajagopal M.; Sivasakthivel R.; Ramar G.; Mansurali A.; Karuppasamy S.K.
source: 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023 - Proceedings, pp. 488-495.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835034148-5
format: Online
language: English
type: Conference paper
coverage: Rajagopal M., Christ (Deemed to Be University), Lean Operations and Systems, School of Business and Management, Bangalore, India; Sivasakthivel R., Christ (Deemed to Be University), School of Sciences, Department of Computer Science, Bangalore, India; Ramar G., Christ (Deemed to Be University), School of Sciences, Department of Computer Science, Bangalore, India; Mansurali A., Central University of Tamilnadu, Department of Commerce and Management, India; Karuppasamy S.K., Kalasalingam Academy of Research and Education, School of Computing, Department of Computer Science & Information Technology, Krishnankoil, India