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Survey on limitations, applications and challenges for machine learning aided hybrid FSO/RF systems under fog and smog influence

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JOURNAL OF MODERN OPTICS 2024年第4-6期71卷 101-125页

作者： Khalid, Hira Sajid, Sheikh Muhammad Nistazakis, Hector E. Ijaz, Muhammad Natl Univ Comp & Emerging Sci Dept Elect Engn Lahore Pakistan Natl & Kapodistrian Univ Athens Dept Phys Sect Elect Phys & Syst Panepistimiopolis 15784 Zografou Greece Manchester Metropolitan Univ Fac Sci & Engn Dept Engn Manchester England

This paper provides a concise overview of hybrid Free Space Optical (FSO)/Radio Frequency (RF) systems operating under fog and smog channels. With the rapid growth of wireless communication and the emergence of the Internet of Things (IoT) have brought about unprecedented connectivity and transformed various aspects of our daily lives. However, certain environmental conditions pose significant challenges to wireless communication systems like FSO and RF technologies. Fog and smog with its dense moisture-laden atmosphere and presence of particulate matter cause absorption and scattering of light, leading to reduced link quality and limited range for FSO communication system. Whereas, RF systems are although less affected by fog/smog as compared to rain but still have inherent limitations in terms of bandwidth and capacity. To overcome these challenges and ensure reliable communication, researchers have turned their attention to hybrid FSO/RF systems which can combine the strengths of FSO and RF technologies and mitigate the limitations of each technology. This work summarizes and explores the hybrid system architecture and machine learning techniques used to enhance the performance and adaptability of wireless communication systems. Additionally, we present the existing experimental research on hybrid FSO/RF systems, performance evaluation metrics, and future research directions to pave the way for further *** FSO: Free Space Optical;RF: Radio Frequency;IoT: Internet-of-Things;OS: Operating Systems;OWC: Optical Wireless Communication;LOS: Line-of-Sight;LPF: Low Pass Filter;AM: Amplitude Modulation;FM: Frequency Modulation;PSK:Phase Shift key;QAM: Quadrature Amplitude Modulation;OFDM: OrthogonalFrequency Division Multiplexing;LF: Low frequency;M-L: Medium frequency;UHF:Ultra High Frequency;SHF: Super High Frequency;EHF: Extremely High frequency;PM: Phase Modulation;QPSK: Quadrature Phase Shift Key;DSSS: Direct SequenceSpread Spectrum;FHSS: Frequen

关键词： Free space optical communication fog and smog attenuation hybrid FSO/RF machine learning algorithms

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Empirical Evaluation of Deep learning Techniques for Fish Disease Detection in Aquaculture Systems: A Transfer learning and Fusion-Based Approach

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IEEE ACCESS 2024年 12卷 176136-176154页

作者： Biswas, Subir Muduli, Debendra Ariful Islam, Md Shantanu Kanade, Anuradha Zamani, Abu Taha Pandurang Kanade, Shantanu Parveen, Nikhat CV Raman Global Univ Dept Comp Sci & Engn Bhubaneswar 752054 Odisha India Dr Vishwanath Karad MIT World Peace Univ Dept Comp Sci & Applicat Pune 411038 India Northern Border Univ Dept Comp Sci Ar Ar 73211 Saudi Arabia Symbiosis Int Deemed Univ Symbiosis Sch Online & Digital Learning Pune 412115 Maharashtra India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522302 Andhra Pradesh India

In aquatic environments, the health of fish populations is crucial for maintaining ecological balance and sustaining aquaculture industries. Timely and accurately detecting fish diseases is paramount for effective management and mitigation strategies. This paper presents a novel approach for fish disease detection leveraging transfer learning and ensemble techniques. Our method combines features extracted from three pre-trained deep learning models, namely VGG-16, MobileNet-V2, and Inception-V3, with a Support Vector machine (SVM) for classification. Through empirical experimental evaluation on a comprehensive dataset, we demonstrate the effectiveness of our proposed model in accurately detecting various fish diseases. The results showcase significant improvements in sensitivity and specificity compared to existing approaches. Additionally, we analyze the impact of different transfer learning strategies and feature fusion techniques on the model's overall performance. Our findings underscore the potential of transfer learning and ensemble methods in enhancing fish disease detection systems, offering promising avenues for future research in aquatic health management and aquaculture.

关键词： Fish Diseases Accuracy Aquaculture Deep learning Biological system modeling Feature extraction Support vector machines Transfer learning machine learning algorithms VGG-16 MobileNet-V2 SVM

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DDoS Attack Detection on Cloud Computing Services using algorithms of machine learning: Survey

DDoS Attack Detection on Cloud Computing Services using Algo...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Chilla Sathvika Vuyyuru Satwika Yarrapothu Sruthi Maddali Geethika Suneetha Bulla Swathi K Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Nowadays cloud computing services have become the most popular internet-based computing and many organizations use their services. Due to this, many cyber-attacks are happening in the cloud. One of those attacks is the Distributed-Denial-Of-Service (DDoS) attack. It floods unreal traffic, hence troubles the availability of the resources. This article is about DDoS attacks and detection of DDoS attacks using machine learning. There are many famous machine learning algorithms such as naïve bayes, random forest, support vector machines etc. These machine learning algorithms can be used to detect the DDoS attacks on doud. There are several datasets available for the researchers to test their proposed models which include NSL-KDD, ICDX, CIDDS-001, CICIDS 2017 etc. This paper presents a detailed study on different machine learning based techniques proposed by various authors to detect the DDoS attack in the cloud environment. A brief explanation has been provided on the available datasets and further discussed about the general methodology.

关键词： Support vector machines Cloud computing machine learning algorithms Heuristic algorithms Organizations Denial-of-service attack Feature extraction

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Enhancing Urban Parking Efficiency Through machine learning Model Integration

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IEEE ACCESS 2024年 12卷 81338-81347页

作者： Sneha Channamallu, Sai Kermanshachi, Sharareh Michael Rosenberger, Jay Pamidimukkala, Apurva Univ Texas Arlington Dept Civil Engn Arlington TX 76019 USA Univ Texas Arlington Dept Ind Engn Arlington TX 76019 USA

An increase in vehicular traffic and a scarcity of parking spaces are creating significant challenges for urban parking management. This study aims to tackle these issues that escalate congestion and pollution and decrease urban productivity, by utilizing machine learning models to accurately predict parking space availability and categorize occupancy levels. It employs a dataset from a college campus garage collected from January 2022 to June 2023 and analyzes the performance of random forest, decision tree, linear regression, and support vector models by comparing them, using multiple evaluation metrics. The results revealed that the random forest model was the most reliable, as it demonstrated strong performance in both the regression and classification analyses and was adept at estimating the exact number of available parking spaces. A concurrent classification analysis that categorized parking occupancy into different levels proved valuable for enhancing the quality of communication and decision-making. An analysis of the importance of various features clearly highlighted the influence of the day of the week on parking demand and patterns;the impact of seasonality on the volume of parking usage;and the time of day, which plays a crucial role in determining parking behavior. The research will benefit urban planners, facility managers, and policymakers by providing them with insights and tools that will enhance the urban parking experience and address the complex challenges of modern urban environments.

关键词： Predictive models machine learning Random forests Decision trees Urban areas Adaptation models machine learning algorithms Support vector machines Linear regression Parking management predictive modeling random forest model decision tree support vector machine linear regression

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Exploring Quantum machine learning for Enhanced Skin Lesion Classification: A Comparative Study of Implementation Methods

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IEEE ACCESS 2024年 12卷 104568-104584页

作者： Reka, S. Sofana Karthikeyan, H. Leela Shakil, A. Jack Venugopal, Prakash Muniraj, Manigandan Vellore Inst Technol Ctr Smart Grid Technol Sch Elect Engn Chennai 600127 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India

Skin diseases affect millions of people worldwide, leading to significant healthcare burdens and challenges in diagnosis and treatment. In the past few years, machine learning techniques have demonstrated potential in assisting dermatologists with diagnosing various skin conditions. As in, conventional machine learning algorithms might encounter challenges in handling the complexity and distinction of skin disease classification tasks, primarily because of the intricate nature of medical image data with its high dimensional properties. In this work, the main analysis is done based on exploring quantum machine learning models for skin disease classification. This approach blends with the aspects of quantum computing with the conventional machine learning techniques to push the boundaries of skin disease classification. This work harnesses the HAM10000 dataset, an extensive compilation of categorized images portraying common skin lesions, to train and assess the efficacy of the proposed methodologies. Quantum computing libraries such as PennyLane and Qiskit is used in this study. Using different combination of qubit rotation encoding and decoding using three types of Pauli gates such as Pauli X, Y and Z gates are implemented and compared using proposed Quanvolutional neural network. Features extracted using MobileNet pre-trained network is used to build Quantum support vector classifier. These quantum machine learning models are compared with some well-known pre-trained models such as Resnet50, Inception-Resnet, Densenet121, DenseNet201 and MobileNet. The combination of RY qubit rotation and PauliZ gate in quantum convolution layer in Quanvolutional neural network produced the optimal classification accuracy of 82.86% more than any other models included in this study. In contrast, Quantum Support Vector Classifier produced similar classification accuracy of 72.5% with respect to pre-trained models.

关键词： Skin Diseases Accuracy Computational modeling machine learning Lesions machine learning algorithms HAM10000 dataset Qiskit quantum machine learning quanvolutional neural network quantum support vector classifier skin lesion PennyLane

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Enhancement of Bayesian Seismic Inversion Using machine learning and Sparse Spike Wavelet: Case Study Norne Field Dataset

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1-1页

作者： Monroe, Jorge A. Teruya de Figueiredo, Jose J. S. de J. S. Souza, Igor Fed Univ Fac Geophys Lab Petrophys & Rock Phys BR-66075110 Belem Brazil Natl Inst Petr Geophys INCT GP Salvador BA Brazil

The concept of uncertainty is fundamental in seismic inversion modeling, as it pertains to the imprecision or lack of certainty inherent in the model's results. In this work, we present a comprehensive study that integrates machine learning (ML), sensitivity analysis on well data, and the sparse spike wavelet to enhance to quality of Bayesian linearized inversion (BLI). Furthermore, sensitivity analysis was conducted to assess diverse combinations of attributes to facilitate the identification of the optimal model configuration and ensure robust results. Subsequently, the wavelet, obtained through the application of the sparse spike convolution on seismic data, was used to further augment the quality of results in the BLI process inversion. This wavelet yields a more precise and efficient representation of seismic events, enabling an enhanced quality in the interpretation of data in the Bayesian context of seismic information. By integrating the sparse spike wavelet with the well and seismic data, a substantial improvement in inversion quality is achieved compared to the initial inversion performed using the original well data and the Ricker wavelet. The combination of ML, sensitivity analysis, and sparse spike wavelet in the BLI inversion process generates results that are more precise and reliable, facilitating well-informed decision-making in the exploration of underground resources. In our analysis, we applied the BLI inversion process in real data from the Norne Field in the North Sea, Norway.

关键词： Data models Bayes methods Mathematical models Uncertainty Analytical models machine learning algorithms machine learning Bayesian linearized inversion (BLI) machine learning (ML) petrophysical properties seismic and well-log data

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A New Data Science Model With Supervised learning and its Application on Pesticide Poisoning Diagnosis in Rural Workers

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IEEE ACCESS 2024年 12卷 40871-40882页

作者： Carvalho, Jaqueline C. S. Pimenta, Tales C. Silverio, Alessandra C. P. Carvalho, Marcos A. Carvalho, Joao Paulo C. S. Univ Fed Itajuba Inst Syst Engn & Informat Technol BR-37500903 Itajuba MG Brazil Jose do Rosario Vellano Univ Dept Comp Sci BR-37130000 Alfenas MG Brazil Brescia Univ Math & Nat Sci Div Owensboro KY 42301 USA

In a Data Science project, it is essential to determine the relevance of the data and identify patterns that contribute to decision-making based on domain-specific knowledge. Furthermore, a clear definition of methodologies and creation of documentation to guide a project's development from inception to completion are essential elements. This study presents a Data Science model designed to guide the process, covering data collection through training with the aim of facilitating knowledge discovery. Motivated by deficiencies in existing Data Science methodologies, particularly the lack of practical step-by-step guidance on how to prepare data to reach the production phase. Named "Data Refinement Cycle with Supervised machine learning (DRC-SML)", the proposed model was developed based on the emerging needs of a Data Sciense project aimed at assisting healthcare professionals in diagnosing pesticide poisoning among rural workers. The dataset used in this project resulted from scientific research in which 1027 samples were collected, containing data related to toxicity biomarkers and clinical analyses. We achieved an accuracy of 99.61% with only 27 rules for determining the diagnosis. The results optimized healthcare practices and improved quality of life in rural areas. The project outcomes demonstrated the success of the proposed model.

关键词： Data models Data science Support vector machines machine learning algorithms Training Solid modeling Pesticides Decision support systems machine learning Toxicology Diagnostic expert systems decision support system machine learning pesticide poisoning diagnosis

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Music Emotion Recognition Based on Deep learning: A Review

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IEEE ACCESS 2024年 12卷 157716-157745页

作者： Jiang, Xingguo Zhang, Yuchao Lin, Guojun Yu, Ling Sichuan Univ Sci & Engn Sch Automat & Informat Engn Zigong 643000 Peoples R China Artificial Intelligence Key Lab Sichuan Prov Yibin 644005 Peoples R China

In recent years, with the development of the digital era, music emotion recognition technology has been widely used in the fields of music recommendation system, music classification, psychotherapy, music visualization, background music generation, smart home, and other applications of music emotion recognition, and has received attention from all walks of life. Especially the rapid development of artificial intelligence and deep learning, the music emotion recognition model using efficient deep neural network composition has become the mainstream model. This paper provides a more detailed overview of music emotion recognition, first introducing the background of music and emotion, and briefly summarizing the content of related works as well as the content framework. In the process, we also compare the similarities and differences in the content of other researchers' reviews of related research areas. And in the middle section, we provide a detailed account of datasets, emotion models, feature extraction, and emotion recognition algorithms. Finally, we discuss the current challenges in music emotion recognition and explore future research priorities.

关键词： Emotion recognition Reviews Music Feature extraction Deep learning Brain modeling Speech recognition machine learning algorithms Visualization Support vector machines Artificial intelligence Music emotion recognition deep learning artificial intelligence music emotion datasets

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Improving electric vehicles sustainability: Accurate forecasting of lithium-ion battery health using machine learning models

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JOURNAL OF ENERGY STORAGE 2024年 103卷

作者： Yang, Chao Ye, Zhihao Xiong, Xin Su, Chang Xie, Mengli Naval Univ Engn Sch Elect Engn Wuhan 430000 Hubei Peoples R China Guangdong Univ Technol Sch Adv Mfg Jieyang 522000 Guangdong Peoples R China

Electric vehicles are increasingly acknowledged as a promising solution for addressing global warming and climate change, with their effectiveness greatly influenced by the attributes of energy storage systems. Although lithium-ion batteries are presently the primary choice for electric vehicle energy storage, their deterioration over time poses challenges in maintaining performance. This research introduces a technique for accurately forecasting battery health, which is crucial for ensuring safety and gaining consumer trust. By utilizing various machine learning algorithms such as Random Forest Regression, Light Gradient Boosting Regression, Support Vector Regression, and Stacked Regressor, this study investigates advancements in estimating the state of health of batteries. Feature extraction and selection, and Pearson correlation analysis, play a significant role, with seven key variables identified as inputs for prediction models, categorically capturing essential aspects of battery performance. The characteristics categorized within class A comprised the Voltage-time graph first turning point, Voltage-time graph second turning point, two turning point slop, and turning points in the voltage-time graph yielding superior outcomes compared to other categorized features. The study illustrates the effectiveness of the proposed method using the experimental battery dataset, achieving an impressive SOH prediction accuracy of 99.36 % with the Stacked regression model. This multi-feature model signifies a substantial progression in addressing existing limitations in lithium-ion batteries' SOH estimation, offering potential avenues for enhancing electric vehicle performance and sustainability.

关键词： Electric vehicles Lithium-ion batteries State of health Energy storage systems machine learning algorithms Stacked regressor

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machine learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

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WATER 2024年第7期16卷 941页

作者： Abbas, Farkhanda Cai, Zhihua Shoaib, Muhammad Iqbal, Javed Ismail, Muhammad Arifullah Alrefaei, Abdulwahed Fahad Albeshr, Mohammed Fahad China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tianjin Univ Sch Civil Engn State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China China Univ Geosci Sch Environm Studies Wuhan 430074 Peoples R China Karakoram Int Univ Dept Comp Sci Gilgit 15100 Pakistan China Inst Water Resources & Hydropower Res IWHR State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China King Saud Univ Dept Zool Coll Sci POB 2455 Riyadh 11451 Saudi Arabia

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone;a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

关键词： groundwater modeling Water Quality Index machine learning algorithms water quality assessment

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