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Establishing a Secure Mobile Online Banking System using AES

Establishing a Secure Mobile Online Banking System using AES

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Delhi Section Conference (DELCON), IEEE

作者： R. C. Karpagalakshmi J. Lenin Robin Rohit V. K. Saravanan Rajesh Sharma R. Sumit Kumar Banshal Department of Computer Science and Information Technology Alliance College of Engineering and Design Alliance University Bangalore India School of Computer Science & Engineering and Information Science Presidency University Bangalore India Department of Information Science Engineering New Horizon College of Engineering Bangalore India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Using a mobile phone or other portable device to conduct financial transactions online is known as “mobile banking.” Activities such as verifying one's account balance, transferring funds to another account, making bill payments, and finding a local automated teller machine (ATM) are included under this classification. Due to the significant expenses associated with implementing this technology, Authentication of users, data privacy, non-repudiation, data integrity, and obtainability of services are all areas where people have voiced concerns. The analysis described in this paper introduces a more advantageous and convenient framework for addressing these issues. The proposed approach effectively accomplishes its intended goals via the use of a widely adopted technique known as salting, which involves the application of the Secure Hash Algorithm (SHA512) to obfuscate sensitive information such as user IDs and passwords. The encoding and decoding procedures used the Advanced Encoding Standard (AES) technique while implementing one-time passwords (OTPs) was implemented to enhance user authentication. The design approach used PHP, JScript, CSS, and a MySQL database. The password recovery technique was used to assess the model's credibility and reliability and acquire the outcomes. This program enhances the process of recovering passwords across various scenarios. It intercepts network traffic using dictionary attacks, brute-force assaults, and cryptanalytics attacks to decrypt encrypted passwords. Additionally, it uncovers password bins, retrieves secreted passwords and analyses routing protocols. The gathered findings support the idea's feasibility since the encrypted and hashed data demonstrates resistance to unauthorized access.

关键词： Online banking Automated teller machine Authentication Passwords Telecommunication traffic Mobile handsets Encoding Routing protocols Internet Standards

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IoT-Enabled Health Monitoring System for Safeguarding Vital Organs with Cloud-Based Diagnosis and Advanced Algorithms 4

IoT-Enabled Health Monitoring System for Safeguarding Vital ...

引用

4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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Hybrid Machine Learning Model for Predicting Damage Scale in Sunflower Leaves affected by Aster Yellow Disease

Hybrid Machine Learning Model for Predicting Damage Scale in...

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Recent Advances in Information technology for Sustainable Development (ICRAIS), International Conference on

作者： Anuj Kumar Jain G S Pradeep Ghantasala Rahul Bhandari Gaganpreet Kaur Rajesh Sharma R Akey Sungheetha Department of Computer Science and Engineering Chandigarh University Mohali Punjab India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bengaluru India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350354461

ISBN: (纸本)9798350354522

Through this work a thorough summary of a predictive model specifically designed to evaluate Aster Yellows infections in sunflower leaves is presented. Exhibiting remarkable accuracy are the model's performance measures, which span from 97.74% to 97.89% for various Damage Scale categories and include precision, recall, and F1-Score. These measurements demonstrate how well the model performs in accurately categorizing cases within each group and highlight how consistently it measures the severity of Aster Yellows disease. The support values clarify the significant representation of cases on every Damage Scale, demonstrating the model's resilience to a variety of situations. This work trains and evaluates the model using the 6880-picture dataset, yielding an astonishing 97.81% overall accuracy, recorded repeatedly at 97.82% on average due to the high averages (of macro, weighted, or micro numbers within each kind). It not only shows high performance across the dataset but also its consistency across Damaged Scales is further supported. And not just the model's reliability, modest variations in support rates also increase its dependability: highlighting its capacity to manage multiple diverse Aster Yellows infection scenarios. Through the model, they can clearly distinguish between different levels of damage with an accuracy of 99 percent overall, which betokens its potential application in precision farming. Now, thanks to these compelling test numbers, farmers and agricultural experts will no longer have to guess at how to distinguish and treat sunflower plants suffering from Aster Yellows infection. In summary: The abstract deftly depicts this model's quantitative performance, general versatility, and possible pragmatic utility, making it a bright spot in the history of precision farming.

关键词： Measurement Training Accuracy Predictive models Reliability Sustainable development Farming Diseases Resilience Pragmatics

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Deep Convolutional Multi-Relational Graph Attention Network for Autonomous Drone Navigation in Urban Wind Zones

Deep Convolutional Multi-Relational Graph Attention Network ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： C. Pabitha K. Paul Joshua S Pradeep Sophia S Rajesh Sharma R D. Karthik Prabhu Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SCAD College of Engineering and Technology Tirunelveli Tamil Nadu India Department of Computer Science and Engineering MAlla Reddy Engineering College for Women Secunderabad Telangana India Department of computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Computer Technology Alliance College of Engineering and Design Alliance University Bangalore India Department of Electrical and Electronics Engineering Ramco Institute of Technology Rajapalaiyam Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Drones are essential for civil engineering operations like logistics and data collecting. Current autonomous drone studies mainly concerns itself with safe path planning in static scenarios; however one of the major challenges of over urban environment is often dynamic factors such as wind, building layouts, and signal coverage that can significantly impact direction stability. For autonomous navigation across dynamic wind zones, this study proposed a new Shape-aware Mesh Dual Lightweight Deep Convolutional Multi-Relational Graph Attention Network with Growth Optimizer (SAMDLDCMRGANet-GO) that leverages local data from RGB cameras and GPS. The method incorporates a Dual Aggregation Transformer for feature extraction, Shape-aware Mesh Normal Filtering for preprocessing, and the LDCMRGAN et for efficient path planning. The proposed approach demonstrates its efficacy in navigating complicated surroundings while retaining high precision in obstacle avoidance and target acquisition, achieving an impressive 99.14% recall, 99.15% accuracy, an outstanding EVA of 0.99, and a low RMSE of 0.41.

关键词： Navigation Convolution Filtering Urban areas Transformers Feature extraction Cameras Path planning Drones Global Positioning System

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IoT Applications in Smart Building Energy Management

IoT Applications in Smart Building Energy Management

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ravichandra, Thangjam Karumudi, Madhavi Maheswari, M. Uma Leela, S. Thamizhkani, B. Kanniyappan, S.P. Alliance School of Business Alliance University Karnataka Bengaluru562106 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Bowrampet Telangana Hyderabad500043 India Rajalakshmi Engineering College Department of Mechatronics Thandalam Tamil Nadu Chennai602105 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600073 India R.M.K. Engineering College Department of Civil Engineering Tamil Nadu Kavaraipettai601206 India

ISBN: (纸本)9798331543617

The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. Data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency. © 2024 IEEE.

关键词： Green buildings

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An Empirical Comparison of Classification Machine Learning Models Using Medical Datasets 4th

An Empirical Comparison of Classification Machine Learning M...

引用

The 4th International Conference on Data Science, Machine Learning 2022

作者： Saketha Rama, B.V. Suryanarayana, G. Ansari, Mohd Dilshad Begum, Ruqqaiya Department of Computer Science Engineering Indian Institute of Information Technology Design and Manufacturing Tamil Nadu Kancheepuram India Department of Information Technology Vardhaman College of Engineering Telangana State Hyderabad India Guru Nanak University Hyderabad India Department of Computer Science Engineering Vardhaman College of Engineering Telangana Hyderabad India

ISBN: (纸本)9789819920570

Classification is a supervised learning model where the class labels are accurately identified for future samples. Medical data is an important source for understanding and improving health outcomes and classification algorithms are often used to analyze these data. Learning models give significant experiences into the situational needs of patients. Various hypotheses have been carried out on different datasets yet it is truly challenging to track down which model is suitable. Proposed work compares the performance of classification models like LR, DT, SVM, NB, KNN, and RF on various datasets. SVM classifier yields accuracy of 0.59 for the Diabetic dataset as it considers individual model opinion, while RF classifier surpassed them both with accuracy 0.9974 for the breast cancer Wisconsin dataset since it is an ensemble approach that takes majority opinions. These findings highlight the need for careful consideration of the choice of classification model when analyzing medical data and provide valuable insights for researchers and practitioners working with these data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Low Readout Noise Photodiode based CMOS Image Sensor with High Fill Factor for Biomedical application

Low Readout Noise Photodiode based CMOS Image Sensor with Hi...

引用

2022 IEEE Delhi Section Conference, DELCON 2022

作者： Katikala, Hima Bindu Pitchaiah, Telagathoti Murthy, G. Ramana Vignan's Foundation for Science Technology and Research Department of Electronics and Communication Engineering 522213 India Alliance College of Engineering and Design Alliance University Department of Electronics and Communication Engineering Bangalore562106 India

ISBN: (纸本)9781665458832

The Complementary Metal-Oxide Semiconductor(CMOS) technology image sensor contemplate rather than Charge Coupled Device (CCD), owing to low noise, dark current, miniaturization, decline production cost, chip area susceptibility, and also the picture quality. Particularly in the visual prosthesis implantable devices, the signal process begins from capturing the image through the sensor. In this paper, a 180nm CMOS Image sensor (CIS) 128(mathrmH)ast128(mathrmV) pixel array designed with the proposed pixel leaf cell using photodiode can operates in three modes. The new leaf cell designed by photodiode specification reduces dark current and improves Full-Well Capacity (FWC) Fill Factor (obtained by having 20.5pF diode capacitance) is perfectly measured. Further by adopting the Correlated Double Sampling (CDS) technique in the column readout circuitry reduces the temporal noise in image sensor with evidence of better closed-loop gain linearity. The leaf cell layout generation characterizes the CIS parametric analysis. © 2022 IEEE.

关键词： Image sensors

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Development of a Real-time Location Monitoring App with Emergency Alert Features for Android Devices 4

Development of a Real-time Location Monitoring App with Emer...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Gowda, Dankan V. Prasad, K.D.V. Shekhar, R. Srinivas, Rachakonda Srinivas, Kale Naga Venkata Lakineni, Prasanna Kumar Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Pune India Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of CSE GITAM School of Technology GITAM University Visakhapatnam India

ISBN: (纸本)9798350305258

Smartphones have completely altered the mobile communication scene. Wi-Fi, global positioning system navigation, high-resolution cameras, and touchscreens with high-speed internet access are just some of the cutting-edge capabilities that these devices offer, allowing users to stay in constant contact with the present. Since many of these features are embedded deeply in the mobile operating system, they are typically inaccessible to the average user. However, Google released Android, a revolutionary operating system. Because of its open system architecture, this platform encourages third-party development and a debugging environment that users may change to create their own unique apps. In this research project, we examine the development of an Emergency Based Remote Collateral Tracking System app on the Android mobile platform from Google. There are three main forms of emergencies: those involving the heart, those involving personal safety, and those involving the roads. Users who own and operate motor vehicles are the primary focus of this app. Our program can keep tabs on the driver's pulse by connecting to a heart rate monitor. Our application has a backup function in case of anomalies. First, it sends SMS messages containing the user's location data after using GPS to do so. © 2023 IEEE.

关键词： Android application development Emergency alert system GPS tracking Location monitoring app Location sharing Real-time location tracking

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Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bale, Ajay Sudhir Arulananth, T.S. Saranya, S. Gadag, Tejashwini Sathiamoorthy, J. Ponnuru, Sudhakiran New Horizon College of Engineering Dept. of Ece Karnataka Bengaluru India Mlr Institute of Technology Department of Electronics and communication Engineering Telangana Hyderabad500043 India Bms Institute of Technology & Management Dept. of Mathematics Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Rmk Engineering college Department of Computer science and Design Kavarapettai India

ISBN: (纸本)9798331543617

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time. © 2024 IEEE.

关键词： Electronic health record

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Revolutionizing Data Capitalization: Harnessing Blockchain for IoT-Enabled Smart Contracts 2

Revolutionizing Data Capitalization: Harnessing Blockchain f...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Thingom, Chintureena Joshi, Anjali Sudman, Md Sakiul Islam Tammina, Manoj Ram Agal, Sanjay Byeon, Haewon Department of CSE Alliance College of Engineering & Design Alliance University India Marathwada Mitra Mandal's Institute of Technology Lohagaon Pune India Department of Electrical Engineering University of Texas at Arlington Arlington76013 United States Bread Financial ColumbusOH United States Department of Computer Engineering Dr. V. R. Godhania College of Engineering and Technology Chhaya Gujarat Porbandar360578 India Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of

ISBN: (纸本)9798350305418

The use of blockchain smart contracts in the monetization of IoT data assets addresses challenges in personal data ownership, quantifiable tracking of data assets, and efficient value transfer within IoT systems. By leveraging blockchain digital fingerprints, the ownership and control of data are transferred from device manufacturers to users, enabling individual data rights. Through techniques such as lifecycle management and digital signatures, device status and data hash values are stored on the blockchain, ensuring data reliability. Smart contracts are used to construct third-party data trading platforms, guaranteeing data sharing security and facilitating seamless data monetization and value transfer. Quantitative analysis of attack possibilities and success probabilities demonstrates that blockchain smart contract technology provides data tamper resistance and eliminates trust issues in data transactions. With the aid of blockchain smart contracts, the association of IoT data becomes feasible, facilitating the transfer and sharing of data values among IoT devices. © 2023 IEEE.

关键词： Blockchain Data Capitalization Digital Fingerprints Digital Signatures IOT Smart Contract

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