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Fusion of Frequency Domain Features of Face and Iris Traits for Person Identification

Journal of The Institution of Engineers (India): Series B 2021年第5期102卷 987-996页

作者： Kagawade, Vishwanath C. Angadi, Shanmukhappa A. Department of Computer Applications Basaveshwar Engineering College Bagalkot India Department of Computer Science and Engineering Centre for Post Graduate Studies VTU Belagavi India

In this study, we present a new approach for person recognition through feature level fusion of face and iris traits. The strength of the multimodal biometric system relies much more on the consistency to determine relevant features from the individual biometric traits and fusion technique employed for integration. Hence, this paper presents a new feature extraction approach for a face–iris multimodal biometric system. The face and iris feature extraction is performed by employing Polar Fast Fourier Transform to obtain textural information in diverse scales and orientations of the face and eye images. The Canonical Correlation Analysis method is employed to integrate face and iris traits features, which reduces the new feature vector length than the input feature vectors. The proposed approach is capable to obtain and improve the discriminative power for person recognition from multi-feature data of face and iris traits. The effectiveness of the proposed model is illustrated through the use of both artificial and genuine face and iris multimodal databases, along with comparisons involving existing state-of-the-art techniques. © 2021, The Institution of Engineers (India).

关键词： Feature extraction

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Surveillance and Intruder Detection using Pyramid Vision Transformer You Only Look Once Enhanced with Gradient Weighted Class Activation Mapping 4

Surveillance and Intruder Detection using Pyramid Vision Tra...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Arunachalam, Sripriya Almusawi, Muntather Rafikiran, Shaik. Sivagami, S. Praveena, Hirald Dwaraka Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Vadapalani City Campus Chennai India College of Technical Engineering The Islamic University of Al Diwaniyah Department of Computers Techniques Engineering Al Diwaniyah Iraq Sri Venkateswara College of Engineering Department of Electrical & Electronics Engineering Tirupati India New Prince Shri Bhavani College of Engineering and Technology Department of Chemistry Chennai India Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350352931

In this era, wireless networks are economically feasible and flexible to establish in healthcare, defense, surveillance, and traffic monitoring, fire detection. Effective intruder detection systems utilize advanced technologies like Artificial Intelligence (AI), sensors, and cameras. Traditional approaches for surveillance and intruder detection had faced several challenges which include false alarms, high costs, privacy concerns, and vulnerabilities. To overcome these challenges, a Gradient-weighted Class Activation Mapping (Grad CAM) with Pyramid Vision Transformer-You Only Look Once Enhanced (PViT-YOLOE) namely GRADCAM-PViT-YOLOE is proposed to detect intruders in public places using images and videos. For preprocessing, noise reduction, image resizing, and image segmentation are considered to reduce false positives, enhance memory efficiency, and to reduce complexity. Then, PViT-YOLOE is considered for feature extraction to enhance detection of small objects, enhance robustness to variations. At last, Grad CAM is employed as classifier to detect intruders in public places with improved interpretability, robustness to noise, and flexibility. The results demonstrate the efficacy of the proposed GRADCAM-PViT-YOLOE in terms of accuracy (99.25%), precision (96.75%) recall (89.82%) and F1-Score (87.92%) when compared to the existing method like Faster Regions Convolutional Neural Networks (FR-CNN). © 2024 IEEE.

关键词： Noise abatement

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Statistical Models for Bacterial Propagation in Human Respiratory Tract

Statistical Models for Bacterial Propagation in Human Respir...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Jaiswal, Raghevendra Maurya, Rahul Singh, Rajneesh Agarwal, Ruchi Singh, Shyam Pratap Pandey, Vijay Kumar Galgotias College Of Engineering & Technology Department Of Electronics & Communication Engineering Greater Noida India Sharda University India Department Of Computer Science & Applications Sharda School Of Engineering & Technology Greater Noida India Nagpur Campus Pune India Noida Institute Of Engineering & Technology Department Of Electronics & Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

Diffusive Molecular Communication (DMC) enabled bacterial propagation within biological systems is employed in different medical applications. In the other words, DMC is being used to study the propagation of bacteria inside the human body. However, DMC and thus the bacterial propagation model depend on the type of deployment used. Various deployment geometries including Rectangular, Spherical and Cylindrical. In this research, a DMC system within a cylindrical biological deployment is developed for the human respiratory tract. It is expected that the information molecules diffusing across the fluid medium would degrade and be influenced by flow. Further, the Cylindrical Biological Deployments (CBDs) offer versatility in representing different tissues or organs. Therefore, in this research paper different statistical models are derived and analyzed under CBD deployment using bacterial concentration f(y) In particular, statistical models for bacterial propagation in terms of probability (Pb), non-binding probability (Pnb), evade probability (Pe(y)), and the probability of binding rate (Pb(y)) are presented. Also, presented models are analyzed numerically using simulation. The numerical simulation demonstrates excellent agreement with the theoretical foundation. The result has been simulated in MATLAB-20. © 2024 IEEE.

关键词： Fading channels

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Comparing Collaborative Filtering Algorithms in AI-Powered Recommendation Systems for E-Commerce 4

Comparing Collaborative Filtering Algorithms in AI-Powered R...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aamer, Najmuddin Mittal, Tanusha Muthusamy, Nantha Kumar Battula, R. S. S. Raju Ajith, P.S. Soudagar, ManzooreElahi M. Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Maharashtra Dehradun410003 India OntrailInc Dba Uprise San FranciscoCA94104 United States Aditya University Department of Computer Applications Andhra Pradesh Surampalem533432 India Brindavan College of Engineering Department of Mathematics Karnataka Bengaluru560063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. This paper investigates the effectiveness of collaborative filtering algorithms in AI-powered recommendation systems, with a focus on user-based collaborative filtering (UBCF). A comprehensive method is employed to guarantee consistency across different rating scales, beginning with data normalisation to standardise user-item interaction matrices. To decrease dimensionality and maintain the most significant patterns of user-item interaction, Principal Component Analysis (PCA) is employed for feature selection. The classification system uses similarity measures and UBCF to predict user preferences based on historical data. The algorithms are evaluated on key metrics like precision, recall, and RMSE using publically available datasets. The outcomes demonstrate the impact of preprocessing and dimensionality reduction on UBCF's performance as well as its efficacy in providing customised recommendations. This research contributes to the improvement of collaborative filtering algorithms for e-commerce systems, which will raise customer satisfaction and enable scalability in dynamic online markets. © 2025 IEEE.

关键词： Collaborative filtering

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

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Machine Learning Based ADMET Prediction in Drug Discovery 4

Machine Learning Based ADMET Prediction in Drug Discovery

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

作者： Bhavitha Lekshmi Prasad, P.S. Ani, R. Deepa, O.S. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham coimbatore India

ISBN: (纸本)9798350305258

A drug is a substance or compound with the ability to treat or prevent disease in people or animals. Drug discovery is the process of identifying prospective medications for certain ailments, and it can take several years to accomplish. However, the introduction of machine learning has simplified the procedure. Virtual screening's major goal is to minimize the virtual compound space of biological molecules. To do this, we developed the VIRTUAL SCREENING TOOL, a virtual molecular descriptor calculator that can analyze molecules and assess their drug-likeness based on specific rules. We used Django with PyCharm, a Python IDE, to create a molecular description calculator, fingerprint generator, and prediction interface. The tool begins by inputting SMILES, which is the chemical structure of the molecule, and outputs molecular descriptors, which are the properties of the molecules, including their physicochemical properties, absorption, distribution, metabolism, lipophilicity, excretion, toxicity, drug-likeness rules, and fingerprint generation. The outputs are available in CSV format for download. © 2023 IEEE.

关键词： Drug Fingerprint Physiochemical property Prediction SMILES

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Blockchain for 5G-Enabled IoHT—A Framework for Secure Healthcare Automation

Blockchain for 5G-Enabled IoHT—A Framework for Secure Healt...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Alam, Md Imran Ahmad, Md Oqail Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Haneef Qidwai, Khalid Ali Department of Computer and Network Engineering Jazan University Jazan45142 Saudi Arabia Department of Computer Applications B.S Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9789811976148

Medical care has evolved into a lifeline for many people;due to this, there is an explosion in the field of medical big data. Wearable technology based on the Internet of Things (IoT) is being used by healthcare professionals to speed up diagnosis and treatment. There are now numerous sensors and devices, which are connected to the Internet. Internet of HealthThings (IoHT) can be used in many different ways in healthcare. Sensors and remote-monitored medical devices can help patients get more accurate and efficient diagnostics. This paper focuses on healthcare automation using the Internet of Things (IoT)-enabled devices. IoT-driven healthcare models can keep patients healthy and safe, enabling doctors to provide better care. In addition, healthcare frameworks significantly reduce healthcare costs and improve performance. Blockchain systems are transparent and thus safe to store and share private information. Data collected by medical devices attached to the body is kept in a cloud server alongside relevant medical reports as part of the proposed framework. The use of blockchain and 5G technology means that patients’ data can be securely transmitted at high speeds and with rapid response times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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LSEA-IOMT: On the Implementation of Lightweight Symmetric Encryption Algorithm for Internet of Medical Things (IoMT) 7th

LSEA-IOMT: On the Implementation of Lightweight Symmetric En...

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7th International Conference on Emerging applications of Information Technology, EAIT 2022

作者： Saif, Sohail Das, Priya Biswas, Suparna Department of Computer Applications Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India Department of Computer Science Chakdaha College West Bengal Chakdaha India Department of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India

ISBN: (纸本)9789811951909

The Internet of Medical Things (IoMT) has made a monumental improvement in patient monitoring, early detection of diseases and treatment methods. This not only enriched the quality of living but also the healthcare cost and errors are minimized. This promising technology has greatly helped the patient and frontline healthcare workers. However, security and privacy breaches are one of the major concerns in IoMT due to wide variety of devices and communication network. The lack of security can affect a patient life since IoMT devices transmit patient data to a cloud-based medical server over insecure channel. Hence, maintaining the privacy and confidentiality becomes an utmost issue worth of further exploration and resolution. Cryptography is one of the primitive technologies which can ensure the confidentiality of a data;however, the complex solutions are not a good choice for resource-constrained IoMT device. Accordingly, we have designed a novel lightweight symmetric encryption algorithm (LSEA) for low-powered IoMT devices. Proposed algorithm uses 8-bit block ciphering technique using two 8-bit secret keys. Physiological parameter such as pulse rate of human body has been considered as one the secret keys while designing the algorithm. Security analysis shows that the algorithm is resilient to known plain text attack and known cipher text attack. LSEA has been tested in Arduino with varying payload. Experimental result depicts that it requires only 1.25 ms time for 16 byte and 7.4 ms for 512 byte data. Proposed algorithm has been compared to AES in a similar experimental environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Multi-Objective Cuckoo Search Optimization Algorithm for Optimal Resource Allocation in Cloud Environment 3

Multi-Objective Cuckoo Search Optimization Algorithm for Opt...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Saif, Mufeed Ahmed Naji Vasudha Niranjan, S K Murshed, Belal Abdullah Hezam Sri Jayachamarajendra College of Engineering Vtu Department of Computer Applications Mysore India University of Mysore Department of Studies in Computer Science Mysore India

ISBN: (纸本)9781665494991

Cloud technology refer to a pool of virtualized heterogeneous computational resources which are served to different customers as a service through the internet. These virtualized resources get provisioned to cloud users dynamically based on demand. however, dealing with resources in such a dynamic environment is still a challenging task. Deciding the way, the available cloud computational resources get allocated to the workload requires efficient provisioning and allocation techniques. The clients need to operate their various applications on cloud servers with variant Quality of Service (QoS) requirements based on the workloads and type of application whereas the objective of the cloud providers is effectively reducing the energy consumption by optimizing the CPU and bandwidth utilization and maintaining the Service Level Agreement (SLA). This, however, makes resource provisioning and allocation a very complicated task. This article implements the meta-heuristic Multi-Objective Cuckoo Search Algorithm (MOCSA) for efficient resource allocation to dynamic upcoming user workloads and scheduling user tasks on cloud VMs, which aim at minimizing the energy consumption with efficient utilization of CPU and bandwidth. The implementation and simulation experiments were conducted on the Cloudsim simulation toolkit. The evaluation results proved that MOCSA performed well in terms of performance, energy consumption, CPU utilization, bandwidth utilization, and SLA violation. © 2022 IEEE.

关键词： Resource allocation

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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