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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Detection of Brain Tumor using Convolution Neural Network through Detection of Metastasis

Detection of Brain Tumor using Convolution Neural Network th...

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Devices for Integrated Circuit (DevIC)

作者： Soumen Santra Suman Sardar Swarnadeep Sil Rudra Sarkar Arpan Deyasi Dept. of Computer Application Techno International New Town Kolkata India Dept. of Electronic Science Acharya Prafulla Chandra College New Barrackpore India Dept. of Electronics and Communication Engineering RCC Institute of Information Technology Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

A novel model using convolution neural network has been developed for near accurate detection of brain tumor by carefully preprocessing the data and addressing class imbalance. Dataset was gathered between December 2017 and March 2018, consists of 7,001 JPG MRI pictures, 3,800 of which show a healthy brain, and 3,201 of which show a brain with tumors; results 94.14% accuracy in training, 94.33% accuracy in testing and 95.12% accuracy in validation. Insignificant losses are reported in all the cases, speaks in favor of quality of segregation between pathological and normal tissues. Present study provides 92.39% sensitivity, 96.63% specificity and 97.03% precision which clearly outperforms peer-reviewed works carried out using CNN; and therefore, justifies novelty of the work.

关键词： Training Pathology Accuracy Sensitivity Convolution Neural networks Brain tumors Brain modeling Integrated circuit modeling Testing

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A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-Enabled Real-Time Human Detection System for Disaster Management

A Comprehensive Framework for Unmanned Aerial Vehicle (UAV)-...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Upadhyay, Govind Murari Joshi, Mukesh Rathi, Preeti Vats, Prashant Narula, Meenakshi Gupta, Shashi Kant Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Graphic Era Hill University School of Computing Uttarakhand India K.R. Mangalam University Dept. of CSE School of Engg.and Techn. Haryana Gurugram India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India Jagannath International Management School Department of Information Technology New Delhi India ITM University Gwalior Department of CSE Madhya Pradesh India

ISBN: (纸本)9798350378092

Disasters pose significant challenges to emergency response efforts, often hindering timely and effective interventions. In recent years, integrating Unmanned Aerial Vehicles (UAVs) has emerged as a promising approach to enhance disaster management capabilities. This paper presents a novel real-time framework leveraging UAV technology for disaster response. The framework encompasses UAV deployment strategies, real-time data acquisition through aerial imaging and remote sensing, robust data transmission using advanced communication infrastructure, automated AI and machine learning analysis, and dynamic resource coordination based on actionable insights. By integrating these elements, the proposed framework aims to improve situational awareness, optimize response efforts, and facilitate informed decision-making during disaster scenarios. This paper discusses the key components of the framework, highlighting its potential impact on enhancing disaster response capabilities in real-time scenarios. © 2024 IEEE.

关键词： Decision making

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Analysis and Research on Students' Learning Behavior Based on Lag Sequence Analysis 4

Analysis and Research on Students' Learning Behavior Based o...

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4th International Conference on computer Information Science and Artificial Intelligence, CISAI 2021

作者： Han, Jiazheng Wei, Zhengang Ocean University of China Dept. of Computer Application Technology Qingdao China Ocean University of China Dept. of Software Engineering Qingdao China

ISBN: (纸本)9781665406925

with the effective practical application of data mining technology in other fields, many researchers have used data mining technology to analyze the learning process data in the field of education and teaching, and explore the hidden and potentially valuable information and knowledge. This paper mainly mines the association in students' learning behavior data through lag sequence analysis algorithm (LSA), and shows learners' learning trajectory, willingness and cognitive process in the learning process. Help to grasp the potential behavior mode of students' learning and effectively guide the development and implementation of follow-up teaching work. © 2021 IEEE.

关键词： Data mining

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Mohd Zaid Nadeem Ahmad Mohammed Aljamal Sarosh Patel Asif Khan dept. of Computer Science and Engineering Integral University Lucknow India dept. of Electronics and communication Engg Goel Institute of Technology And Managment Lucknow India School of Engineering University of Bridgeport USA dept. of Computer Application Integral Robotics Lab Integral University Lucknow India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people.

关键词： Temperature measurement Temperature sensors Temperature distribution Vehicle safety Wheels Weather forecasting Transportation Real-time systems Monitoring Wireless fidelity

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A New Quantum Inspired Algorithmic Approach to Finding Minimum Spanning Tree

A New Quantum Inspired Algorithmic Approach to Finding Minim...

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IEEE India Conference (INDICON)

作者： Amrit Kaur Sulabh Bansal Dept. of Computer Application Manipal University Jaipur Jaipur India Department of Information Technology Manipal University Jaipur Jaipur India

One of the most common and well-known problems in combinatorial optimization is the minimum spanning tree. With the emergence of complex data structures, new, more complex, but asymptotically efficient algorithms have been proposed. In this research, we present a new evolutionary algorithm influenced by quantum mechanics, known as quantum-inspired evolutionary algorithm (QIEA) to find the minimum spanning tree of a complete weighted undirected graph as input and computational results to demonstrate the effectiveness of proposed algorithm. The proposed algorithm is named QIEA-MST and is based on concept and principle such as qubits and the superposition, coherence and the entanglement of different qubits of quantum state. The algorithm is simple and easy to understand and easy to implement. Some types of scientific and engineering problems will benefit from the proposed method.

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Classifying the DNA sequence using Optimized Naive Bayes Approach: A Comparative Analysis of Machine Learning Algorithms

Classifying the DNA sequence using Optimized Naive Bayes App...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Aditya Pai H Chandrappa S Guru Prasad M S Ganesh Pathak Atul Thakare Pratik Kumar MIT School of Computing MIT Art Design and Technology University Pune India Dept. of Computer Science and Engineering JAIN (Deemed-to-be University) Bengaluru India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Department of Computer Application Graphic Era (Deemed-to-be University) Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In bioinformatics, the exact classification of DNA sequences is essential to increasing comprehension of genetic structures and functionalities. This research introduces an enhanced Naive Bayes approach (ENB) specifically designed to handle more complexities related to DNA sequence data. This research integrates advanced feature weighting and optimized probability estimation techniques to enhance classification accuracy substantially. The proposed work demonstrated superior efficacy to traditional ML algorithms like Random Forest, SVM, Decision-Trees, K-Nearest Neighbors, Gradient Boosting etc. The proposed method attained a level of accuracy of 98%, and proposed approach performs better than the other models, with 94% accuracy in Logistic Regression coming in second.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Nearest neighbor methods Boosting Bayes methods Complexity theory Medical diagnosis Random forests

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Energy Efficient Location-Based XML Indexing in Real-Time Wireless Data Broadcasting for IoT Devices

Energy Efficient Location-Based XML Indexing in Real-Time Wi...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Vikas Goel Ashutosh Sharma Mayank Singh Amit Kumar Goyal Vikas Jain Dept. of IT KIET Group of Institutions Ghaziabad UP India Dept. of IT IMS University Campus Ghaziabad UP India Dept. of CSE GL Bajaj Institute of Technology Gr. Noida India Dept. of CSE Chandigarh University Punjab India Dept. of Computer Application SCRIET C.C.S. University Campus Meerut India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The Internet of Things (IoT) is fast revolutionizing a number of sectors by performing real-time data collection and analysis. Effective data broadcasting protocols become increasingly important as IoT devices. Specifically designed for Internet of Things applications, this paper proposes a comprehensive framework for location-based XML indexing in real-time wireless data broadcasting. To enable effective geographic-based XML data searching, we propose a spatial XML indexing technique. Our proposed broadcasting strategy maximizes network use also. The suggested solution ensures energy efficiency of various IoT devices while addressing the issues of scalability and changing data environments.

关键词： Wireless communication Performance evaluation Protocols Scalability XML Broadcasting Real-time systems Energy efficiency Internet of Things Indexing

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Development of Cloud Centric Healthcare Monitoring System For Cardiac Patients embedded in IoT Platform

Development of Cloud Centric Healthcare Monitoring System Fo...

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International Conference on Emerging Trends in Engineering and technology, ICETET

作者： Adarsh Ravi Mishra Ravi Mishra Ragini Shukla Dept. of Computer Application and Information Technology Dr. C.V. Raman University Bilaspur India Dept. of Electronics and Telecommunication G H Raisoni Institute of Engineering and Technology Nagpur India

In terms of profit and employment, the Indian economy’s healthcare sector has emerged as one of the most significant sectors. The Indian healthcare sector is experiencing rapid growth due to improved coverage, expanded functionalities and services and increased public and private spending. Due to which the life expectancy has increased dramatically. As the population of individuals rises in many societies, the number of people affected by chronic diseases, such as diabetes, cardiovascular disease, obesity, and so on, rises as well. As a result, Internet of Things (IoT)-based personalised healthcare is becoming a more significant idea.. This paper’s main goal is to illustrate a cardiac patient e-health data acquisition, transfer, and monitoring system. The smart health monitoring system prototype uses a wireless sensor network to measure various health data, which are then sent to the concerned individual through GPRS. These parameters are also saved in Amazon cloud services for future use.

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Detect Phishing Website by Fuzzy Multi-Criteria Decision Making 1

Detect Phishing Website by Fuzzy Multi-Criteria Decision Mak...

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1st International Conference on AI in Cybersecurity, ICAIC 2022

作者： Shah, Rajeev Kumar Hasan, Mohammad Kamrul Islam, Shayla Khan, Asif Ghazal, Taher M. Khan, Ahmad Neyaz Kathmandu Nepal Center for Cyber Security Faculty of Information Science & Technology Selangor Malaysia Ucsi University Institue of Computer Science and Digitial Innovation Malaysia Integral University Dept. of Computer Application Lucknow India Sjyline Collge University United Arab Emirates

ISBN: (数字)9781665400435

ISBN: (纸本)9781665400435

Phishing activity is undertaken by the hackers to compromise the computer networks and financial system. A compromised computer system or network provides data and or processing resources to the world of cybercrime. Cybercrimes are projected to cost the world 6 trillion by 2021, in this context phishing is expected to continue being a growing challenge. Statistics around phishing growth over the last decade support this theory as phishing numbers enjoy almost an exponential growth over the period. Recent reports on the complexity of the phishing show that the fight against phishing URL as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. Previous research have proposed different methods including neural network, data mining technique, heuristic-based phishing detection technique, machine learning to detect phishing websites. However, recently phishers have started to use more sophisticated techniques to attack the internet users such as VoIP phishing, spear phishing etc. For these modern methods, the traditional ways of phishing detection provide low accuracy. Hence, the requirement arises for the application and development of modern tools and techniques to use as a countermeasure against such phishing attacks. Keeping in view the nature of recent phishing attacks, it is imperative to develop a state-of-the art anti-phishing tool which should be able to predict the phishing attacks before the occurrence of actual phishing incidents. We have designed such a tool that will work efficiently to detect the phishing websites so that a user can understand easily the risk of using of his personal and financial data. © 2022 IEEE.

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