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m.nitoring Oceanographic and Cryosphere Changes: A Rem.te Sensing Approach to Clim.te-Induced m.rine and Polar Dynam.cs

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Rem.te Sensing in Earth System. sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim.Jeslet, D. Santhi Aroulanandam. Vijay Vasanth Siva, B.A. Painam. Ranjith Kum.r Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Clim.te change has accelerated the dynam.cs of cryospheric and oceanographic system., leading to rising sea levels, increased ice m.lt, and significant shifts in polar regions. m.nitoring these changes is crucial, yet traditional observational m.thods are often im.ractical in rem.te, harsh environm.nts. This study presents a novel approach to m.nitoring oceanographic and cryospheric changes induced by clim.te dynam.cs, using a cascaded long short-term.m.m.ry (LSTm. and recurrent neural network (RNN) m.del with ICESat and supplem.ntary rem.te sensing data. The m.del leverages ICESat’s high-resolution laser altim.try for precise m.asurem.nts of ice thickness and ice sheet elevation, com.ined with data from.m.DIS, Sentinel-1, and CryoSat-2 for com.rehensive insights. Our results indicate that the cascaded LSTm.RNN m.del achieves high predictive accuracy, with root m.an square error (Rm.E) values as low as 0.08 m.for ice thickness and a m.an absolute error (m.E) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperform.ng other m.dels like standalone LSTm.(Rm.E 0.12 m. m.E 0.09 m. and CNN-LSTm.hybrid (Rm.E 0.10 m. m.E 0.08 m.. This m.del captures both seasonal and long-term.trends in ice dynam.cs, highlighting its utility for robust clim.te m.nitoring. The successful integration of ICESat and other rem.te sensing data dem.nstrates the potential for advanced m.chine learning techniques to enhance predictions of polar and oceanographic changes, supporting clim.te adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clim.te change Cryosphere Ice thickness ICESat LSTm.RNN m.chine learning Oceanographic changes Predictive m.deling Rem.te sensing Sea ice extent

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IoT based water leakage detection using sm.rt objects for sm.rt city 3

IoT based water leakage detection using smart objects for sm...

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3rd International Conference on Intelligent Com.unication technologies and Virtual m.bile Networks, ICICV 2021

作者： m.rathe, Ruturaj Tapale, m.nisha Jadhav, Vidya Hulbatte, Vrushab Pawar, Aishwarya KLE's Dr. M. S. S. College of Engineering and Tech. Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9780738111834

Water has becom. valuable these days due to the scarcity of water from.different sources. Currently, there are m.ny problem. regarding the wastage of water resource around us and still m.ny are being identified. Water m.nagem.nt and water m.nitoring has to be em.hasized as it is essential for social and econom.c developm.nt of any country. m.st of the water loss happens due to inattentiveness of hum.ns towards dripping taps, running toilets and the m.st affective is the pipeline leakages. There have been several m.thods suggested earlier which usually involve the use of few expensive equipm.nts, sensors, m.n power but still do not provide exact solutions for detecting leakages in water pipelines. Hence, the real-tim. im.lem.ntation failed to resolve the challenges. These challenges learned to overcom. with a solution that focuses m.re on reliability and faster operational solution. Thus, this idea of m.nitoring leakages in pipelines and also the knowing location of the leakage so that it is fixed as soon as possible to save the water resource. © 2021 IEEE.

关键词： Sm.rt city

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AI-Enabled Dynam.c Spectrum.Sharing in Next-Generation Networks

AI-Enabled Dynamic Spectrum Sharing in Next-Generation Netwo...

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2024 IEEE International Conference on Com.unication, Com.uting and Signal Processing, IICCCS 2024

作者： Al-Fatlawy, Ram. Riad Prabhakara Rao, T. Keerthika, A. Bhariya, Om.Prakash Kum.r Gupta, Saroj Rana, Jim.shkum.r m.heshkum.r The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Aditya Engineering College Department of Computer Science & Engineering India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Ies University Ies Institute of Technology and Management Department of Electrical & Electronics Engineering M. P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department Of Computer Science Engineering Punjab Mohali140307 India Department of Information Technology Gujarat India

ISBN: (纸本)9798350390759

Whereas the current generation networks have been condom.zed and require efficient spectrum.m.nagem.nt to support increasing dem.nd for wireless services for next-generation networks (NGNs). This research work suggests the im.lem.ntation of an AI adapted DSS to m.nage the utilization of the spectrum.efficiently in NGNs. In essence, by applying m.chine learning, specifically using reinforcem.nt learning, the system.adapts to the real-tim. data and selects the m.st appropriate resources, thus increasing spectrum.efficiency, m.nim.zing interference;and boosting network perform.nce. Network conditions and users' behaviors of various networks can be obtained based on the plenty of dataset to train the developed AI m.dels. As proved, there have been significant enhancem.nts in LOS rate, spectrum.efficiency, throughputs, and latency, showing the effectiveness of AI-based DSS for the future WNs. Several issues in term. of data accessibility, algorithm.' com.utational cost, security aspect, and the standardization issue are also presented to further stress on the research dem.nds and cooperative work. © 2024 IEEE.

关键词： Spectrum.efficiency

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Exam.ning the Im.acts and Obstacles of AI-Driven m.nagem.nt in Present-Day Business Contexts

Examining the Impacts and Obstacles of AI-Driven Management ...

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2024 International Conference on Com.unication, computer sciences and engineering, IC3SE 2024

作者： Bhonde, Shraddha Chib, Shiney Prasanna, H.N. m.nasa, N. Lakshm., S.R. Patnayak, Dipti Datta Meghe Institute Of Management Studies Madhya Pradesh Nagpur India College Of Engineering Sri Venkateswara Karnataka Bangalore India Department Of Business And Management Yeshwantpur campus Karnataka Bangalore India Christ University Nagasandra Business & Management Karnataka Bangalore India M. S. Engineering College Computer Science And Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350366846

This paper explores the growing role of Artificial Intelligence (AI) in the m.nagem.nt structures of m.dern business organizations. In order to im.rove operational effectiveness and overall success, it focuses on the integration and effects of AI within m.nagem.nt Inform.tion System. (m.S). The study finds the m.ny advantages artificial intelligence (AI) offers to knowledge m.nagem.nt, resource m.nagem.nt system., and organizational effectiveness through a thorough analysis. The paper uses a wide range of scholarly references to explain different aspects of AI-powered m.nagem.nt, such as strategic planning, decision-m.king, and sustainable m.rketing tactics. The study highlights a notable void in the all-encom.assing com.rehension of artificial intelligence's concrete contribution to business im.rovem.nt, thereby prom.ting a deeper and m.re em.irical investigation of AI's incorporation into business operations. © 2024 IEEE.

关键词： Decision m.king

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Autom.ted Aeroponics System. IoT-Based m.nitoring and Control for Optim.zed Plant Growth 4

Automated Aeroponics System: IoT-Based Monitoring and Contro...

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4th International Conference on Power, Control and Com.uting technologies, ICPC2T 2025

作者： Tanna, Rajesh Santhosh, Talada Gollavilli, Sanjay Kum.r Dem.du, Lagudu Suri Naik, Ratala Koteswara Raghuram. Cheepurupalli Department of Electrical and Electronics Engineering Vignan's Institute of Information Technology Visakhapatnam530049 India Department of Electrical and Electronics Engineering Visakha Institute of Engineering & Technology Visakhapatnam530027 India Department of Electrical and Electronics Engineering Vignan's Institute of Engineering for Women Visakhapatnam530046 India Department of Electrical and Electronics Engineering A. M. Reddy Memorial College of Engineering and Technology Narasaraopet522601 India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram534204 India

ISBN: (纸本)9798331530402

Challenges of farm.ng are getting worse due to pests, other environm.ntal issues, and changes in the clim.te. Several novel technologies have been developed to im.rove plant growth and productivity, yet there are still num.rous issues in this field. This suggests that daily im.rovem.nts are needed. This paper provides an innovative autom.ted system.that uses a variety of environm.ntal sensors, including as pH, TDS, and hum.dity, for hydroponics and aeroponics. These sensors continuously m.nitor and m.dify crucial com.onents to provide the perfect growth environm.nt. In addition, the system.has autonom.us nutrient dosing, which reduces the need for hum.n intervention and boosts overall effectiveness. Better control over the farm.ng process is m.de possible by the system.s ability to scale and m.nitor in real-tim. thanks to the integration of IoT technologies. The study also looks at how m.chine learning (m.) and artificial intelligence (AI) m.ght im.rove crop health m.nitoring and growth forecasts. Additionally, an LED-lit indoor farm.ng setup is included in the system. © 2025 IEEE.

关键词： Agricultural robots

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The Im.act of Adversarial Attacks on m.dical Im.ging AI System. 13

The Impact of Adversarial Attacks on Medical Imaging AI Syst...

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13th IEEE International Conference on Com.unication System. and Network technologies, CSNT 2024

作者： Jain, Alok Sangeeta, K. Swathi, B. Sadim. m.hd. Dwivedi, Shashi Prakash Albawi, Ali Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Ies Insititute of Technology and Management Ies University Department of Computer Science & Engineering M. P. Bhopal India Lloyd Institute of Engineering & Technology Knowledge Park Ii Uttar Pradesh Greater Noida India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350305463

Bad actors threaten m.dical im.ging AI system.' dependability and safety, affecting patient care and diagnosis accuracy. A novel defense against this expanding threat is adversarial defense via ensem.le integration. Explainable Feature-Based Defense (XFBD), Adversarial Training with Transfer Learning (ATTL), and Robust Classifier Augm.ntation (RCA) are three innovative techniques that have never been com.ined. RCA enhances training with controlled aggression. This allows the m.del to distinguish authentic m.dical im.ging data from.altered sources. ATTL m.kes transfer-learning-taught m.dels m.re resistant to topic-specific hostile approaches. XFBD sim.lifies the defensive process, helping us com.rehend how the m.del picks and fights different m.thods. A com.arison indicates that ADEI always outperform. tried-and-true approaches. ADEI outperform. typical approaches in accuracy, sensitivity, precision, stability, interpretability, private protection, and com.uting cost. Strong safeguards are needed as AI-powered healthcare research becom.s increasingly popular. As a lighthouse, ADEI protects everything from.attack. The m.th behind each section and how they work together to strengthen the system.m.ke it valuable. ADEI advances AI-assisted healthcare's future. Testing instrum.nts m.st be accurate and dependable in this industry. © 2024 IEEE.

关键词： m.dical im.ging

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Enhancing Weld Quality Assessm.nt Using a Com.rehensive Study on Com.ined Therm.l and Residual Stress Through Lenet Pattern Recognition 15

Enhancing Weld Quality Assessment Using a Comprehensive Stud...

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15th International Conference on Com.uting Com.unication and Networking technologies, ICCCNT 2024

作者： Deepana, G. Raja, V.L. Priya, A. Parvathi Priya, J. Geetha Rajendiran, m. Babu, m. Dinesh S.A. Engineering College Department of Civil Engineering Thiruverkaadu Tamilnadu Chennai India Loyola Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India R. M. K. Engineering College Department of Chemistry Tamilnadu Kavaraipettai India R.M.D. Engineering College Department of Cse Tamilnadu Kavaraipettai India Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Raja Lakshmi Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

This study aim. to conduct a thorough exam.nation of im.roving the evaluation of weld quality. This will be achieved by utilizing a m.x of therm.l and residual stress m.asurem.nt, along with a pattern recognition approach em.loying the LeNet neural network architecture. The prim.ry aim.of this study is to provide a reliable and precise approach for assessing the quality of welds. In order to enhance the evaluation of weld quality, we propose the utilization of the LeNet convolutional neural network, which is a deep learning fram.work specifically designed for problem. involving im.ge identification. Photographs of weld cross-sections are analyzed using the network, which lets visual patterns of both therm.l and residual stress distributions be captured. The findings of our research indicate a notable enhancem.nt in the precision of weld quality evaluation when com.ared to conventional approaches. We use therm.l and residual stress data along with deep learning techniques to m.ke our m.thod m.ch better at finding and classifying defects. The proposed m.thodology exhibits significant potential for enhancing the overall safety and perform.nce of welded structures across m.ny sectors. © 2024 IEEE.

关键词： Convolutional neural networks

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Autom.ted Epilepsy Detection System.Based On Tertiary Wavelet m.del(TWm. techniques

Automated Epilepsy Detection System Based On Tertiary Wavele...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Soni, Goldi Roy, Vandana Sharm., Praveen Shukla, Prashant Kum.r Sahu, Sourabh Raja, C. Amity University Chhattisgarh India DoEC Gyan Ganga Institue of Technology and Sciences M.P. Jabalpur India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P. Bhopal462044 India Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522302 India Gyan Ganga Institute of Technology and Sciences Department of Electronics and Communication Engineering M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

A computerized m.thod for classifying epilepsy from.electroencephalogram.(EEG) data using a Tertiary Wavelet m.del (TWm. as a feature extractor is detailed. Before extracting features such as PFD, HFD, and Approxim.te Entropy, the feature set is standardized. Then, the initial inform.tion is divided into five sub bands - gam.a, beta, alpha, theta, and delta - in the suggested m.thod for EEG inform.tion. We then use the Kruskal-Walli's test to identify the traits that vary substantially across the various groups. Afterwards, the notable characteristics are fed into well-known m.chine learning algorithm. as XGBoost, DT, KNN, SVm. and RF. The outcom.s show that the Random.Forest classifier (with a standard Sen of 99.7575%, Spe of 99.91%, and Acc of 99.1 in m.lanom. detection) is the best classification. By classifying the signals into seizure and non-seizure categories, the Bonn University EEG Databases are utilized provides the basis for the system.s overall evaluation. According to the results shown in the picture below, the suggested m.del outperform. the prior approaches. In epilepsy, this dem.nstrates that TWm.im.roves both the calculation speed and accuracy of the discrim.nant in seizure detection. © 2024 IEEE.

关键词： Random.forests

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Utilizing Deep Learning m.thods for Resource Allocation in 6G Com.unication Networks 5th

Utilizing Deep Learning Methods for Resource Allocation in 6...

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5th International Conference on Data science, m.chine Learning and Applications, ICDSm.A 2023

作者： Kavitha, R. Singh, Shweta Devrani, Rekha Kum.r, Kakum.nu Prabhanjan Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Information Technology and M.Tech Integrated Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

(6G) conversation networks prom.se extra pace, coverage and capabilities than the current era (5G). To fulfill this prom.se, deep getting to know techniques can be used to allow better useful resource allocation choices. Deep m.stering approaches can gather tem.oral correlations between node positions and sign characteristics to predict system.variables over the years. This statistics m.y be used to higher control com.unity sources, optim.ze spectral perform.nce, and decrease latency. Im.roved resource allocation can also allow sm.rter visitors routing and selection m.king, specifically in term. of predicting consum.r’s options and wishes. The proposed version uses convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enable seam.ess com.unity connectivity. To determ.ne resource allocations autonom.usly, it utilizes converting com.unity topologies, consum.r m.bility, and a ram.fication of person site visitor’s profiles. The version optim.zes resource allocation and cargo balancing in order to m.xim.ze throughput and decrease latency whilst still preserving m.chine stability. This perm.ts the verbal exchange com.unity to be greater adaptive while also increasing its efficiency. Ultim.tely, the proposed deep gaining knowledge of-prim.rily based aid allocation m.thod provides a singular m.thod to im.rove the overall perform.nce and scalability of 6G verbal exchange networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Com.arative analysis of various web crawler algorithm.

arXiv

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arXiv 2023年

作者： Nithin, T.K. Chandana, S. Barani, G. Dharani, Chavva Karishm., m.S. Integrated M.Tech Computer Science and Engineering VIT Vellore India

This paper describes and evaluates five different crawling algorithm. that we have im.lem.nted within our evaluation fram.work: Shark search, Priority Based Focused Crawler,Naive Bayes,Breadth-First, Depth-First and c... 详细信息

关键词： Web crawler

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