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2020 International Conference on electronics and Sustainable Communication Systems, ICeSC 2020

作者： Jain, Soniya Patel, Trupesh M.E. Computer Engineering Silver Oak College of Engineering Technology Ahmedabad Gujarat India M.E. Computer Engineering S.P.B Patel Engineering College Mehsana Gujarat India

ISBN: (纸本)9781728141084

In the environment of the internet of things decentralized type of network is utilized, where nodes communication takes place and data is transferred to the internet via the base station. The communication routing is applied using RPL in IoT. For LLNs (Low Power and Lossy Network) it is a systematic routing protocol. As static topologies, RPL was initially designed, for a lot of uses mobility assistance continues to be a significant necessity of LLNs. Additionally, the component of the applications mobility support for many sink nodes rather compared to a sink node. This's because of the point that in an information assortment wireless sensor networking (WSN), just a sink very easily results to a much faster power depletion, a lot more package loss, maximum latency, and smaller sized community range. Deploying several sinks in the networking is able to assist you to resolve the issues. This particular paper concentrates on dealing with mobility assistance in RPL with several sinks. A resolving response to this particular issue is deploying several sinks in wireless sensor networking. In direction to enhance the wireless sensor network performance overall in phrases of Packet loss, Lifetimes, every sensor node communicates just with the closest sink. When the sinks are actually distributed more than places that are diverse, this could lead to an average decrease in the num.e. of hops and hence the growth in reduction and functionality of the ingested energy. © 2020 Ieee.

关键词： energy utilization

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Adaptive error Correction and machine Learning for Robust Optical Fibre Communication 2

Adaptive Error Correction and Machine Learning for Robust Op...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vembarasi, K. Kumar, S. Praveen Kumar, T. Senthil Senthilkumar, S. Rajakumaran, m. Ganeshamoorthy, R. E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam611002 India M Kumarasamy college of engineering Department of Electronics and Communication Engineering Tamil Nadu Thalavapalayam639113 India Sastra Deemed University School of Computing Tamil Nadu Thanjavur613401 India Rajalakshmi Engineering college Department of Electronics and Communication Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350368413

Optical fibre communication systems are key for high-speed data transmission but they face challenges such as transmission errors and signal degradation. Hence this study proposes an innovative method that combines adaptive error correction technique with machine learning algorithm to enhance the robustness and reliability of optic fibre communication. Adaptive error correction methods are utilized, specifically low-density parity check codes and Turbo codes which are known for their efficiency in handling high error rates in data transmission. These techniques dynamically adjust their error correction parameters based on the different conditions of optical fibre communication channel. Then, the integration of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are utilized to detect and correct errors in real time. For training and evaluation, the International Telecommunication Union (ITU-T) are employed. This dataset includes different signal parameters and error scenarios. experimental evaluation demonstrate that this method eliminates the bit error rate and enhance the signal to noise ratio compared to traditional error correction methods. It also ensures minimal latency, making the method suitable for high-speed optical fibre communication system. This study contributes to the field of optical fibre communication by providing a robust solution that utilizes the strengths of both adaptive error correction and machine learning. © 2024 Ieee.

关键词： Convolutional neural networks

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exploring potential predictors of psychological distress among employees: A Generalized ARC-GRU Based model

Exploring potential predictors of psychological distress amo...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Vasanti, G. Karthi, R. Bisht, Amisha Rao, B. Srinivasa Thulasimani, T. Vasuki, S. Aditya Institute of Technology and Management Department of Basic Science and Humanities Tekkali India E.G.S. Pillay Engineering College Department of Management Studies Nagapattinam India Department of Humanities and Social Sciences Dehradun India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science Engineering Hyderabad India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India

ISBN: (纸本)9798350360660

It seems to have an increase in employee reports of psychological distress across a wide range of industries. Research from the past indicates that a significant portion of the global workforce has dealt with emotional and mental health challenges. The worker's physical, social, and occupational functioning could be negatively impacted if these concerns are not addressed sooner. The three steps that make up this suggested approach are preprocessing, feature extraction, and model training. Factorization is a part of data preprocessing that involves encoding input values into categorical variables, which are also known as enumerated types. Data visualization, training speed, accuracy, and overfitting prevention are all enhanced by feature extraction. In order to train the model, a Generalized ARC-GRU was utilized. Traditional methods, such as GRU and GARC, are surpassed by the suggested methodology. After applying the method, the accuracy increased by 91.48 percent. © 2024 Ieee.

关键词： Social psychology

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melon(Cucumis melo)fruit‑specific monoterpene synthase

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molecular Horticulture 2023年第1期3卷 357-360页

作者： Kathrine H.Davidson Syamkumar Sivasankara Pillai Yukihiro Nagashima Jashbir Singh Rita metrani Kevin mCrosby John Jifon Bhimanagouda Patil Seyednami Niyakan Xiaoning Qian Hisashi Koiwa Department of Horticultural Sciences Vegetable and Fruit Improvement CenterTexas A&M UniversityTX 77843 College StationUSA Texas A&M AgriLife Research and Extension Center 2415 E Business 83WeslacoTX 78596USA Department of Food Science and Technology Texas A&M UniversityCollege StationTX 77843USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA TEES‑AgriLife Center for Bioinformatics&Genomic Systems Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Molecular and Environmental Plant Sciences Texas A&M UniversityCollege StationTX 77843USA

muskmelon(Cucumis melo L.)is one of the important horticultural crops of the Cucurbitaceae *** production of melon fruits was approximately 27 million tons,with the United States production yielding 616,050 tons(FAO 2018).melon is diploid(2n=24)and has an approximate genome size of 450 mbp(Arumuganathan and earle 1991).A high-quality reference genome of melon(DHL92 v4.0)covers 358 mbp pseudomolecules(Castanera et al.2019).

关键词： crops yielding melon

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Latency minimization in 5G Network Backhaul Links Using Chaotic Algorithms

Latency Minimization in 5G Network Backhaul Links Using Chao...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Shanthi, K.G. Jayanthi, N. Ali Sohail, moham.e. Dinesh Babu, m. Chairma Lakshmi, K.R. mritthula, B. R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India Tamil Nadu Chennai600117 India Jazan University College of Computer Science & Information Technology Department of Computer & Network Engineering Jazan Saudi Arabia Rajalakshmi Institute of Technology Chembarabakkam Department of Mechanical Engineering Tamilnadu Chennai India R.M.K. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India R.M.K. Engineering College C.S.E. Department Tamilnadu Chennai India

ISBN: (纸本)9798350344387

In the realm of 5G wireless networks, achieving ultra-low latency is of paramount importance to support real-time applications and services. A critical factor influencing overall network latency is the backhaul links connecting remote base stations to the core network. This research delves into the application of chaotic algorithms for latency minimization in 5G network backhaul links. Chaotic algorithms, known for their sensitivity to initial conditions, offer a unique approach to optimizing network performance. By harnessing the inherent random.e.s and deterministic chaos, these algorithms can effectively explore the solution space for optimal routing and resource allocation. Through comprehensive simulations and performance evaluations, this study demonstrates the potential of chaotic algorithms to significantly reduce latency in 5G backhaul networks. The results underscore the viability of utilizing chaos- inspired techniques to address latency concerns, opening new avenues for enhancing the overall efficiency of 5G networks. © 2023 Ieee.

关键词： 5G networks backhaul links chaotic algorithms latency minimization network performance

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Securing IoT Networks in Healthcare for enhanced Privacy in Wearable Patient monitoring Devices

Securing IoT Networks in Healthcare for Enhanced Privacy in ...

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2024 Ieee International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Tiwari, Vibha Jharbade, Nitin Chourasiya, Prashant Supraja, B. Wani, Priyanka Sujit maurya, Rashmi Madhav Institute of Technology and Science Department of Centre for Artificial intelligence M. P. Gwalior India GGITS Dept of Computer Science & Business System M.P. Jabalpur India IES Institute of Technology and Management IES University Department of Computer Science & Engineering M.P. Bhopal462044 India Guru Nanak Institutions Technical Campus Department of CSE - AIML Hyderabad India Dr. D. Y. Patil Institute of Technology Dept. of E&TC Maharastra Pune India

ISBN: (纸本)9798331539948

The project aims to make smart patient monitoring devices more private. This is crucial to healthcare IoT network security. Strong security is needed to protect patient data in a growing digital healthcare system. Our research develops a whole security system with sophisticated encryption, user-centric identity, blockchain integration, and deep learning to detect unusual activity. As mentioned before, sophisticated patient monitoring gadgets in contemporary healthcare have increased security vulnerabilities. A powerful and adaptable security system is needed to secure patient data;hence, a multidimensional approach is studied. This study's approaches include experimental design, dataset preparation, and measure choice. The security framework uses cutting-edge cryptography, including eCC, homomorphic encryption, and blockchain. Also crucial are recognizing users based on biological features and spotting abnormalities using deep learning and LSTm networks. mixed methods mitigate all security issues. The findings reveal that the recom.e.ded strategy boosts encryption to 256 bits. Since authentication accuracy has reached 98%, user-centered authentication approaches work. Cryptographic processes employ resources effectively, and anomaly detection rates reflect how successfully the approach detects unusual device behavior. elliptic Curve Cryptography (eCC), which may give 256-bit encryption, is stronger than sym.e.ric encryption (128 bits). User-centric authentication was 98% accurate, outperforming public key infrastructure (95%). With a 92% success rate, LSTm networks can predict abnormal activity in connected devices. The proposed approach performs cryptographic operations fast, with 120 operations per unit time. In conclusion, this research introduces a healthcare IoT security solution. The combined encryption, identity, blockchain, and deep learning technology is secure. The recom.e.ded solution protects patient data on personal tracking devices with greater encrypt

关键词： Differential privacy

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meme expressive Classification in multimodal State with Feature extraction in Deep Learning

Meme Expressive Classification in Multimodal State with Feat...

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electrical, electronics, Information and Communication Technologies (ICeeICT), International Conference on

作者： A. Barveen S. Geetha m.K.mohamed Faizal Department of Computer Science and Engineering M.I.E.T. Engineering College Trichy Department of Computer Applications University College of Engineering Trichy Tamilnadu

memes are a socially interactive way to communicate online. memes are used by users to communicate with one another on social networking sites and other forums. memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. every year, meme mimicking applications are created that allow users to use the imitated meme expressions. memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTm-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and extended LSTm-RNN for meme image characterization. Through training and validation, extended LSTm-RNN achieved 0.98% accuracy with better performance than CNN.

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Blood Disease Detection System Based on Haemogram Report Using Decision Tree Algorithm 2nd

Blood Disease Detection System Based on Haemogram Report Usi...

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2nd NIeLIT's International Conference on Communication, electronics and Digital Technologies, NICeDT 2024

作者： Gaikwad, Deepali K. Gaikwad, Asha Gaikwad, Ashok Department of Computer Science and IT Dr. Babasaheb Ambedkar Marathwada University M.S Chh. Sambhajinagar India P. E. S. College of Engineering M.S Chh. Sambhajinagar431001 India Institute of Management Studies and Information Technology Vivekanand College Campus M.S Chh. Sambhajinagar India

ISBN: (纸本)9789819736034

There is enormous amount of medical data in healthcare systems gathered from numerous medical tests performed in numerous fields. In order to diagnose diseases early on or even before they manifest, it is still necessary to extract hidden information from medical data. The classifiers use the characteristics of the complete blood count to forecast information about potential blood illnesses in early stages, which may improve the likelihood of a cure. The art of machine learning is in charge of building predictive models based on prior data. The illness prediction procedure may decrease the num.e. of fatalities and improve the quality of life for those tract these diseases. In this study applied decision tree classifier on blood test report dataset and identify various blood diseases like anemia, leukemia, lymphoma, sickle-cell, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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enhanced Route Optimization Using Bat Algorithm in mANeT 1

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Sugumaran, S. Sivasankaran, V. maheswar, R. Thangaraj, P. Chitra, m.G. Department of ECE Sreenivasa Institute of Technology and Management Studies AP Chittoor India School of EEE VIT Bhopal University Bhopal India Department of ECE KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science M.M.E.S Women’s Arts and Science College Melvisharam India

ISBN: (数字)9789819914104

ISBN: (纸本)9789819914098

The mANeT is described as a wireless dynamic topology network. It doesn’t have a centralized unit to control the other nodes and nodes are configured within the network. The trust route is very challenging issues in the network. The available routes are not sure to complete this data transmission task perfectly due to the dynamic topology and less centralized configuration. This reduces network performance such as packet drop or incomplete data transmission. In this paper, the enhanced Route optimization techniques are introduced with the support of the Bat Algorithm (eROiBA) in mANeT. This technique creates the trust and best route from source to destination. In fact, trust and the best routes are selected by the node iteration condition and the average total energy level of the nodes between the sources to destination. The iteration of the sender node is a detail of the previous performance ratio of the adjacent node evaluated by the source node. The eROiBA is simulated in ns-2 tools and observes this outcome such as packet delivery ratio, end-to-end delay and throughput. The results of the eROiBA is improved over existing work. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Topology

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Supervised mL based model to Predict Satisfaction of Student Performance During the Pandemic

Supervised ML based Model to Predict Satisfaction of Student...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Prajkta P. Chapke Anjali B. Raut Department of Computer Science and Engineering H.V.P.M’s C.O.E.T. Amravati India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The coronavirus, COVID-19 has spread around the globe, causing unprecedented changes in major areas including education and the economy. Amid a pandemic, the educational sector is relying heavily on performance prediction models to aid in the study of student status and the subsequent improvement of performance. The term “educational Data mining” describes the practice of using data mining principles and methods in the classroom. The explosion of educational data in recent years has made educational data mining a hot issue. methods for discovering latent patterns in educational data might be developed within this area. The data can be learned more about student’s, their learning habits, and how well they will do in school by applying the techniques drawn from.e.ucational Data mining. This study aims to predict how universities would modify e-learning in the aftermath of the outbreak by investigating student’s actual experiences utilizing e-learning facilities during this time. A formal questionnaire was distributed and an online survey was created for this purpose. Following an exhaustive survey, a good outcome was achieved in terms of the dataset. In addition, the selected supervised machine learning algorithms such as multi-Layer Perceptron (mLP), Random Forest (RF), Support Vector machine (SVm), and XG Boost (XGB) are utilized to train the prediction model to determine how likely students are to adopt the e-learning approach in the future. mLP produced the best results compared to other approaches, with an accuracy of 84.1%. The unexpected emergence of the epidemic caused tremendous disruptions to the academic pursuits of people.

关键词： Support vector machines Surveys Radio frequency COVID-19 electronic learning Pandemics Biological system modeling

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