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Intelligent technologies (CONIT), International Conference on

作者： M S Guru Prasad Raghavendra M Devadas Vani Hiremani Afsar Jahan J Praveen Gujjar Aryan Dahiya Department of Computer Science and Engg Graphic Era (Deemed to be University) Dehradun India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Symbiosis Institute of Technology Symbiosis International (Deemed) University Pune India Department of Computer Applications Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

This innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. This framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. The investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. These findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. The Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. These additional insights contribute to a comprehensive understanding of the Metagenomic Face framework’s efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis.

关键词： Support vector machines Measurement Logistic regression Metagenomics Microorganisms Accuracy Machine learning algorithms

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Enhanced SNR Based Admission Control Algorithm for Vehicular Ad-Hoc Network 23

Enhanced SNR Based Admission Control Algorithm for Vehicular...

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Proceedings of the 7th International Conference on Future Networks and Distributed Systems

作者： Aws Zuhair Sameen Mohammed Hasan Alwan Mayssam Saad Kadhum Yousif I Hammadi Mokhalad Abdulameer Kadhim Alsaeedi Ahmed Aziz Mohammed Saleh Ali Muthanna Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Department of Communications Engineering University of Diyala Iraq Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Institute of Computer Technologies and Information Security Southern Federal University Russia

ISBN: (纸本)9798400709036

Vehicular Ad-hoc Network (VANET) becomes a fundamental subcategory of mobile ad-hoc networks that provides vehicles to communicate with each other and with roadside infrastructure smartly. Various VANET performance challenges are considered in terms of Quality of Service (QoS) due to vehicle speed and transmission rates. Different problems cause performance degradation as performance anomaly where high rates of vehicles have to meet the speed of the low rates vehicle while transmitting data. In this paper, a QoS-aware Signal to Noise Ratio (SNR) admission control mechanism (QASAC) is proposed to handle the performance anomaly problem while maintaining the QoS levels for different traffic types. QASAC has been compared against the latest SNR based admission control mechanism. QASAC has enhanced the performance of data delivery up to 23% in terms of data dropping rates for high priority traffic.

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Robust personnel rostering: how accurate should absenteeism predictions be?

arXiv

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arXiv 2024年

作者： Doneda, Martina Smet, Pieter Carello, Giuliana Lanzarone, Ettore Vanden Berghe, Greet Politecnico di Milano Department of Electronics Information and Bioengineering Milan Italy National Research Council Institute for Applied Mathematics and Information Technologies Milan Italy KU Leuven Department of Computer Science CODeS Gent Belgium University of Bergamo Department of Management Information and Production Engineering Dalmine Italy

Disruptions to personnel rosters caused by absenteeism often necessitate last-minute adjustments to the employees' working hours. A common strategy to mitigate the impact of such changes is to assign employees to reserve shifts: special on-call duties during which an employee can be called in to cover for an absent employee. To maximize roster robustness, we assume a predict-then-optimize approach that uses absence predictions from a machine learning model to schedule an adequate number of reserve shifts. In this paper we propose a methodology to evaluate the robustness of rosters generated by the predict-then-optimize approach, assuming the machine learning model will make predictions at a predetermined prediction performance level. Instead of training and testing machine learning models, our methodology simulates the predictions based on a characterization of model performance. We show how this methodology can be applied to identify the minimum performance level needed for the model to outperform simple non-data-driven robust rostering policies. In a computational study on a nurse rostering problem, we demonstrate how the predict-then-optimize approach outperforms non-data-driven policies under reasonable performance requirements, particularly when employees possess interchangeable skills. © 2024, CC BY.

关键词： Machine learning

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Machine Learning Applications For Protecting The Information Of Health Care department Using Smart Internet Of Things Appliances -A REVIEW 2

Machine Learning Applications For Protecting The Information...

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2nd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2022

作者： Surve, Jyoti Umrao, Dhananjay Madhavi, M. Rajeswari, T.S. Bangare, Sunil L. Chakravarthi, M. Kalyan International Institute of Information Technology Department of Information Technology Hinjewadi Pune India Engineering & Management Institute Department Computer Science and Engineering Institute/ Organization: Naraina Vidya Peet Panki Kanpur Ganga Gunj India Vr Siddhartha Engineering College Department of Computer Science and Enginnering Chalasani Nagar Kanuru Vijayawada India Koneru Lakshmaiah Education Foundation Department of English AP Vaddeswaram India Sinhgad Academy of Engineering Savitribai Phule Pune University Department of Information Technology Pune India VIT-AP University School of Electronics Engineering Amaravathi India

ISBN: (纸本)9781665437899

So, let us begin with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. computer vision and computer vision have improved a wide range of industries, including medical diagnoses, data display and procedures, science and research, and so on A number of tiny IoT devices on the market for heart rate sensor, allowing patients to roam about freely while their hearts are continually monitored. It is still difficult to guarantee ultra-accurate findings, however most current instruments can achieve accuracy rates of over 90 percentage... Monitoring cardiac rhythms, as well as glucose levels, may be challenging, and even those who are represented at medical institutions. Intermittent heart rate assessments cannot protect against sudden changes in vital signs, and standard techniques of heart rhythm surveillance used in hospitals require patients to be permanently attached to wired apparatus, limiting their mobility. © 2022 IEEE.

关键词： Health care

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Evolutionary Method Based on Artificial Bee Colony and Ontological Approach for Structural Identification of Interval Discrete Models of Objects with Distributed Parameters 12

Evolutionary Method Based on Artificial Bee Colony and Ontol...

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12th International Conference on Advanced computer Information technologies, ACIT 2022

作者： Dyvak, Mykola Melnyk, Andriy Martsenyuk, Yevgeniya Rohatynska, Nina Brukhanskyi, Ruslan Pytel, Sviatoslav West Ukrainian National University Department of Computer Science Ternopil Ukraine West Ukrainian National University Criminal Law and Procedure Department of Law Enforcement Ternopil Ukraine West Ukrainian National University Department of Accounting Economic and Legal Support for Agroindustrial Business Ternopil Ukraine Education and Research Institute of Innovative Educational Technologies West Ukrainian National University Ukraine

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

The problem of structural identification of interval discrete models of objects with distributed parameters is considered in the paper. This paper also proposes an ontological approach as a control tool that greatly simplifies the systematic standardized methods of storage models, the process of their construction and appropriate usage in practice. This paper also describes the features of the construction of the corresponding ontological model, the pattern of choice of a nonlinear model. Relevant experimental studies have also been conducted to confirm the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Knowledge management

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A Key Factor in Traffic management - Vehicle Speed Prediction Using Machine Learning 4

A Key Factor in Traffic Management - Vehicle Speed Predictio...

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4th International Conference on Computation, Automation and Knowledge management, ICCAKM 2023

作者： Maurya, Ayush Dwivedi, Ashish Pratap, Saurabh Dubey, Suchi Zhou, Fuli Shukla, Vinod Kumar Shambhunath Institute of Engineering and Technology Department of Computer Science and Engineering U.P Prayagraj India Jindal Global Business School O.P. Jindal Global University Sonipat India Department of Mechanical Engineering Varanasi India Manipal Academy of Higher Education Dubai United Arab Emirates College of Economics and Management Zhengzhou University of Light Industry Zhengzhou450001 China Amity University Dubai Department of Engineering and Architecture United Arab Emirates

ISBN: (纸本)9798350393248

The analysis of transportation data is considered as an essential application for Machine Learning (ML) based-intelligent transportation design and control especially for safety and improving energy management. There is a requirement to identify efficient methods for optimizing transportation operations and enhance traffic management. This study adopts exploratory data analysis to analyse transportation data including year, month, vehicle type, time period, area type, functional classification. Predictive analytics is used to predict vehicle speed as dependent variable. The study adopts four supervised ML models such as Multiple Linear Regression (MLR), Random Forest Regression (RFR), Decision Tree (DT) and Artificial Neural Network (ANN) approach for analysing the speed prediction factors. Speed prediction assists as a foundation of advanced traffic management systems. The results from the findings suggest that ANN and RFR are the commendable model for performance analytics. © 2023 IEEE.

关键词： Machine learning

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An implementation of Wireless Mesh Routing Protocol system against Dos attacks in IoT-based Assistance system

An implementation of Wireless Mesh Routing Protocol system a...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Dharmveer Yadav Ramakrishnan Raman Durgaprasad Gangodkar Sanjeev Kumar Joshi B. Sreedevi Ch. Raghava Prasad Department of Computer Science St. Xavier’s College Jaipur Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Srinivasa Ramanujam Centre SASTRA Deemed University Kumbakonam Tamilnadu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

The Internet of Things' (IoT) smart items are crucial to the future of ambient assisted living (AAL), notably in the field of e-Healthcare (IoT). In our AAL example, these items use a wireless mesh network to gather and transmit real-time patient data to the hospital server (WMN). It is possible for an attacker to redirect network traffic through numerous denial-of-service attacks since mesh networks are multi-hop in nature (DoS) assaults, which would compromise the mesh routing protocol's proper operation. Based on a comparison analysis, we select the most appropriate secure mesh routing mechanism for IoT-based AAL applications in this work. The protocol's resistance to Denial-of-Service (DoS) assaults is next examined. The protocol is modeled and examined with respect to With an emphasis on the hello flooding attack, data packet delivery ratio, latency, and throughput are examined. The protocol provided is completely immune against DoS attacks, according to simulation results, and may be the prime possibilities for safe routing in Internet - of - things AAL applications.

关键词： Resistance Wireless communication Ambient assisted living Wireless mesh networks Denial-of-service attack Routing Throughput

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LEARNING DISCRIMINATIVE FEATURES FROM SPECTROGRAMS USING CENTER LOSS FOR SPEECH EMOTION RECOGNITION

arXiv

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arXiv 2025年

作者： Dai, Dongyang Wu, Zhiyong Li, Runnan Wu, Xixin Jia, Jia Meng, Helen Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin Hong Kong

Identifying the emotional state from speech is essential for the natural interaction of the machine with the speaker. However, extracting effective features for emotion recognition is difficult, as emotions are ambiguous. We propose a novel approach to learn discriminative features from variable length spectrograms for emotion recognition by cooperating softmax cross-entropy loss and center loss together. The softmax cross-entropy loss enables features from different emotion categories separable, and center loss efficiently pulls the features belonging to the same emotion category to their center. By combining the two losses together, the discriminative power will be highly enhanced, which leads to network learning more effective features for emotion recognition. As demonstrated by the experimental results, after introducing center loss, both the unweighted accuracy and weighted accuracy are improved by over 3% on Mel-spectrogram input, and more than 4% on Short Time Fourier Transform spectrogram input. Copyright © 2025, The Authors. All rights reserved.

关键词： Spectrographs

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Integrating an Encryption Standard and Cloud Computing to Improve the Safety of Sensitive Medical Data

Integrating an Encryption Standard and Cloud Computing to Im...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： Amjed Sahib Noori Alshami Ahmed R Hassan Mustafa Al-Tahee Ibrahim N. Abdullah Zain Jaffer Saif O. Husain Department of Scholarships& Cultural Relations Ministry of Higher Education &Scientific Research Department of Medical Devices Engineering Technologies National University of Science and Technology Dhi Qar Nasiriyah Iraq Medical instruments engineering techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering AI-Turath University College Baghdad Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The Internet of Medical Things (IoMT) has improved people's quality of life by making it possible to connect biomedical sensing equipment for health monitoring. Current e-healthcare also recognizes the value of technologies like health data exporting to the cloud. Both of these approaches have their limitations, though. The biggest challenge is presented by the limited resources of sensor devices and the need to ensure the privacy of sensitive medical information. Sensitive data refers to the subset of personal information that necessitates an extra layer of security because it can reveal extremely private aspects of an individual's life. A person's health information is extremely personal. Encryption is the safest method for transferring and storing sensitive information. Only approved individuals with the correct key have access, so the possibility of abuse within the firm is greatly diminished. Numerous business functions are moving to cloud computing, from data backup and recovery to email and virtual desktops to software development. To effectively use solutions, pressing issues must be addressed swiftly. Taking the necessary precautions is essential for avoiding harm when receiving medical care. This research intends to provide patient-centric protection for sensitive medical data in the cloud by employing a tiered architecture of security activities and the notion of Integrated Encryption Standard and Mobile Cloud Computing (IES-MCC). IES-MCC utilized Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance the security settings and eliminate the smoothness to clean and secure cloud data storage paths. The IES-MCC model is used to examine the simulation assessment scores of the various elements and features, and the results are the most precise and efficient compared to an existing method.

关键词： Cloud computing Privacy Pressing Encryption Sensors Security Protection

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Integrating optical communication in MIMO-OTFS using hybrid signal detection methods: analyzing the throughput and spectrum

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Journal of Optical Communications 2024年

作者： Kumar, Arun Kanjilal, Pushpendu Syamala, Maganti Nanthaamornphong, Aziz College of Computing Prince of Songkla University Phuket Thailand Department of Electronics and Communication Engineering New Horizon College of Engineering Bengaluru India Department of Electronics and Communication Engineering B. P. Poddar Institute of Management and Technology Kolkata India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India

MIMO-OTFS (multiple input multiple output in orthogonal time-frequency space) modulation is a key component of sophisticated optical radio systems, helping to increase their speed and range. The increased capacity of optical communication can be put to use in a number of applications. In order to minimize latency, we optimize the bit error rate (BER) and power spectral density (PSD) of the framework by applying the hybrid signal detection technique known as zero forcing equalizer and minimal mean square error (ZFE + MMSE) to the MIMO-optical OTFS system (16 × 16, 64 × 64, and 256 × 256) in this article. The suggested ZFE + MMSE works better than the traditional signal detection algorithms, according to the work’s experimental study. Furthermore, without using any detection techniques, the MIMO-OTFS outperforms the MIMO-OFDM by attaining an SNR gain of 1.2–2.3 dB. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Bit error rate

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