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Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

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Journal of Optical Communications 2024年第2期45卷 347-354页

作者： Meenu Sharma, Anshu Malhotra, Rahul Department of Electronics and Communication Engineering CT Institute of Engineering Research and Technology Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology & Research Greater Kailash Maqsudan Punjab Jalandhar144008 India Computer Science and Engineering CT Institute of Technology and Management Shahpur Near Lambra Punjab Jalandhar144020 India

In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Fog

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Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of Computer Science Shaanxi Normal UniversityXi'an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

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A novel method for indian vehicle registration number plate detection and recognition using CNN

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8563-8585页

作者： Pandey, Vibha Choubey, Siddhartha Patra, Jyotiprakash Mall, Shachi Choubey, Abha Department of Computer Science Engineering Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science Engineering Shri Shankaracharya Institute of Professional Management and Technology Chhattisgarh Raipur India School of Computer Science and Engineering Galgotias University Greater Noida India

Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as "extraction," "preprocessing," and "segmentation," and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. © 2024 – IOS Press. All rights re

关键词： Image classification

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A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model 2

A Hybrid IoT and Machine Learning Approach for Crop Recommen...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Suresh, A. Geetha Vani, B. Lavanya, M. Kumar, R.G. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering A.P India

ISBN: (纸本)9798350317558

Agriculture is a key component of a nation's economy. In the traditional method, farmers rely on experience to determine the ideal climate and environment for a given crop. technology is advancing, and while the system depends on it to some extent, improvements are still required. This study presents an Internet of Things (IoT) based crop recommendation system that uses Machine learning (ML) to use the value that IoT devices collect from the soil. Initially, we utilized baseline ML classifier models like Support vector machine (SVM), K-Nearest Neighbor(KNN), Random forest (RF), and Random forest (NB).Then we employed an ensemble model to detect the suitable crops. We employed several IoT sensors in our research. The DHT11 sensor collects real-time environmental data, such as temperature and humidity. During crop recording, vital soil parameters like potassium and phosphorus - which are essential for crop growth - are measured using an NPK sensor, a pH sensor, and a rain sensor. These sensors can be seamlessly integrated by the IoT device, offering an effective way to collect crucial agricultural data. The Voting Base Ensemble performs exceptionally well in terms of precision, recall, and F1 score measures. The voting Base Ensemble algorithm improves metrics such as F-score, recall, and precision by up to 98%. A thorough evaluation of soil conditions is made possible by the integration of various sensors, and the application of an ensemble model enables accurate crop recommendations based on environmental factors. © 2024 IEEE.

关键词： Agriculture Crop Recommendation Ensemble IoT Machine Learning

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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

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Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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Fruit Quality Monitoring System using CNN 1

Fruit Quality Monitoring System using CNN

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

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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Fog-Assisted Blockchain-IoMT Healthcare Framework with Role-Based Access Control for Critically Ill Patients

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SN computer science 2024年第6期5卷 1-13页

作者： Mallick, Soubhagya Ranjan Lenka, Rakesh Kumar Tripathy, Pradyumna Kumar Rao, D. Chandrasekhar Sharma, Suraj Ray, Niranjan Kumar School of Technology Woxsen University Telangana Hyderabad 502345 India Department of Computer Science Central University Odisha Koraput 764021 India Computer Science and Engineering Silicon University Odisha Bhubaneswar 751024 India Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela 769015 India Computer Science and Engineering Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur 495009 India School of Computer Science and Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar 751024 India

Hospitals, clinics, pharmaceutical firms, diagnostics centres, pathology labs, insurance companies, and emergency vehicles must be connected in a unified system to better serve patients worldwide with cutting-edge medical care. Most previously existing systems rely on cloud-based platforms, which face several technological challenges, including data availability, data accessibility, processing delays, scalability concerns, problems gathering real-time data, and difficulties monitoring patients. Information about a person’s health is extremely private and should be protected with the utmost care. To address these issues, We propose a decentralised, secure, and efficient framework incorporating the internet of things, fog computing, and blockchain technology to collect, process, manage, and store healthcare data. A unique role-based access control is used in the proposed framework to prioritise critically ill patients while maintaining confidentiality, scalability, data availability, and system security. Finally, we evaluate the efficiency of our proposed framework by conducting experiments on device registration time, transaction latency, transaction throughput, and access control in medical environments using realistic healthcare scenarios. We then compare the results to existing fog models to determine the system’s performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain Cloud computing Fog computing Healthcare IoMT Scalability

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Trust and QoS-Driven Query Service Provisioning Using Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1827-1844页

作者： K.Narmatha K.Karthikeyan Department of Computer Science and Engineering SRM Institute of Engineering and TechnologyRamapuramChennaiIndia Department of Information Technology Coimbatore Institute of TechnologyCoimbatoreIndia

The growing advancements with the Internet of Things(IoT)devices handle an enormous amount of data collected from various applications like healthcare,vehicle-based communication,and smart *** research analyses cloud-based privacy preservation over the smart city based on query ***,there is a lack of resources to handle the incoming data and maintain them with higher privacy and ***,a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment.A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards *** handling privacy,other inter-connected metrics like efﬁciency are also essential,which must be considered to fulﬁl the privacy ***,this work provides a query-based service model and clusters the query to measure the relevance of frequently generated ***,a Bag of Query(BoQ)model is designed to collect the query from various *** is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get theﬁnal *** processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Opti-mizer(GWO).It is used to attain global and local solutions regarding privacy *** iterative data is evaluated without any over-ﬁtting issues and computational complexity due to the tremendous data handling *** on this analysis,metrics like privacy,efﬁciency,computational complexity,the error rate is *** simulation is done with a MATLAB 2020a *** proposed model gives a better trade-off in contrast to existing approaches.

关键词： Cloud computing IoT privacy security grey wolf optimization

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Cohesive Heartbeat Monitoring for Geolocation and Log Correlation in Campus Networks 15

Cohesive Heartbeat Monitoring for Geolocation and Log Correl...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganya, R. Shazil Nath, Hiran V. Vellore Institute of Technology Chennai Department of Computer Science and Engineering Chennai India Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India National Institute of Technology Department of Computer Science and Engineering Kozhikode Calicut India

ISBN: (纸本)9798350370249

A campus network is made up of a dense interconnection of local area networks within a limited geographical area characterized by a centralized infrastructure that interconnects various facilities within an institution, providing communication and data exchange services to support organizational activities. In a campus network, relevant information about connected clients such as connection status, duration, and relative location has to be maintained and updated in real-time to facilitate availability, log correlation and monitoring capabilities. It is important to establish a multi-pronged system in place to combat security threats and potential data loss. In this paper, we examine several existing standard practices and identify the operational and functional bottlenecks associated with each implementation with respect to the campus network. The study underscores the critical need for robust and adaptive network architectures and practices to safeguard organizational communication and data integrity with emphasis on the dynamic landscape of campus environments. Finally, we propose a novel cohesive heartbeat monitoring system that leverages data from various sources to ensure connection availability and provide advanced logging capabilities. © 2024 IEEE.

关键词： 802.1X campus network heartbeat logging RADIUS SNMP

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Using Contact Tracing technology for Communicable Disease Tracking

Using Contact Tracing Technology for Communicable Disease Tr...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Dandavate, Prajkta Dutta, Atharva Kulkarni, Atharva Raut, Atharva Atkari, Darshan Auti, Sakshi Joshi, Atharva Vishwakarma Institute of Technology Computer Engineering - AI Pune India Vishwakarma Institute of Technology Computer Engineering Pune India Vishwakarma Institute of Technology Computer Science - AI/DS Pune India Vishwakarma Institute of Technology Computer Science Pune India Vishwakarma Institute of Technology Computer Engineering AIDS Pune India

ISBN: (纸本)9798331517953

Having recently tackled the COVID-19 pandemic, a new dimension to healthcare management has emerged. Irreparable damages were done due to the inadequacy of the existing systems and the lack of knowledge in most of the population in this field. Effective tracking and alerting of communicable diseases has never been of more significance. Our project harnesses advanced contact tracing technology to help users monitor and better manage their health and reduce the risk of exposure to communicable diseases. The app gives real time notifications to the user anytime they are near a potential disease carrier alerting them thereby avoiding potential infections. The innovated system has a questionnaire consisting of simple yes or no questions for symptom detection and provides precautionary measures based on the user's responses. The app also utilizes a radar giving the users better knowledge about their surroundings. Additionally, the app offers online consultations with doctors. Moreover, it provides emergency helpline access to the users. All these features help in more effective tracking of healthcare for not only emergency situations like pandemics, but further provides a complete solution for day-to-day management of healthcare also informing the users about safety measures so they can be well prepared if they ever develop an illness. © 2024 IEEE.

关键词： COVID-19

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