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2024 International Conference on science, Technology, engineering and Management, ICsTEM 2024

作者： solaimalai, Gautam shanthi, J. Maria Kumar, sampath s. Khabiya, Priyanka Geetha, K. Talele, Gokul U.S. Bank GA30041 United States Department of Artificial Intelligence and Machine Learning J. B. Institute of Engineering and Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India Department of Computer Science Engineering Mandsaur University Madhya Pradesh Mandsaur458002 India Department of Computer Science G.T.N. Arts College Tamil Nadu Dindigul624005 India Department of Data Science Organization name Accenture Maharashtra Pune411013 India

ISBN: (纸本)9798350376913

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. by synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks. © 2024 IEEE.

关键词： Deep learning

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Machine Utilization Prediction in Cloud Using Informer Model

Machine Utilization Prediction in Cloud Using Informer Model

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Ubiquitous Computing and Intelligent Information systems (ICUIs), International Conference on

作者： Archana Naik Kavitha sooda Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This study investigates CPU utilization prediction in cloud environments using the Informer model, a deep learning framework optimized for long-sequence forecasting. by leveraging its ability to capture complex temporal patterns, the Informer model is benchmarked against GRU, LsTM, Prophet, and XGboost across diverse CPU utilization datasets. Performance is evaluated using RMsE, MsE, MAE, and MAPE metrics. Experimental results demonstrate that the Informer model significantly outperforms the comparison models, excelling in capturing both gradual trends and abrupt fluctuations. specifically, the Informer achieved a 47.51% reduction in RMsE over GRU, 49.90% over LsTM, 67.36% over Prophet, and 50.46% over XGboost. These findings underscore the Informer model's superior adaptability and precision, making it a robust solution for dynamic resource utilization prediction in cloud environments. Its predictive accuracy is pivotal for enhancing workload management and optimizing resource allocation.

关键词： Measurement Adaptation models Accuracy Error analysis Predictive models Market research Ubiquitous computing Resource management Forecasting Long short term memory

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Rectal Cancer Prediction and Performance based on Intelligent variational Autoencoders Machine Using Deep Learning on CDAs Dataset

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Journal of Artificial Intelligence and Technology 2023年第4期3卷 195-204页

作者： Gaganpreet Kaur Ismail Keshta Mohammad shabaz H.s.batra T.vijaya sagar bhupesh Kumar singh vaddempudi sujatha Lakshmi Department of Computer Science&Engineering University Institute of Engineering&TechnologyChitkara UniversityRajpuraPunjabIndia Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia Model Institute of Engineering and Technology Jammu Jammu&KashmirIndia Department of Biochemistry Symbiosis Medical College for WomenPuneIndia Symbiosis International(Deemed University) PuneIndia B.S.Anangpuria Educational Institute AlampurBallabgarh-SohnaFaridabadDelhi-NCRIndia Department of Computer Applications R.V.R&J.C College of EngineeringGunturAndhra PradeshIndia

A pathological complete response to neoadjuvant chemoradiotherapy offers patients with rectal cancer that has advanced locally the highest chance of ***,there is not yet a valid prediction model *** efficient feature extraction technique is also required to increase a prediction model’s ***(cancer data access system)program is a great place to look for cancer along with images or *** this study,we look at data from the CDAs system,specifically bowel cancer(colorectal cancer)*** study suggested a survival prediction method for rectal *** addition,this determines which deep learning algorithm works best by comparing their performance in terms of prediction *** initial job that leads to correct findings is corpus *** forward,the data preprocessing activity will be performed,which will comprise“exploratory data analysis and pruning and normalization or experimental study of data,which is required to obtain data features to design the model for cancer detection at an early stage.”Aside from that,the data corpus is separated into two sub-corpora:training data and test data,which will be utilized to assess the correctness of the constructed *** study will compare our autoencoder accuracy to that of other deep learning algorithms,such as artificial neural network,convolutional neural network,and restricted boltzmann machine,before implementing the suggested methodology and displaying the model’s accuracy graphically after the suggested new methodology or algorithm for patients with rectal *** criteria,including true positive rate,receiver operating characteristic(ROC)curve,and accuracy scores,are used in the experiments to determine the model’s high *** the end,we determine the accuracy score for each *** outcomes of the simulation demonstrated that rectal cancer patients may be estimated using prediction *** is shown that variational deep encoders have e

关键词： autoencoders cancer survival neural networks rectal cancer

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An Ensemble Model to Predict Network Traffic Prediction Model Using Rb Multicast Protocol 7

An Ensemble Model to Predict Network Traffic Prediction Mode...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Deepa, s. sathishkumar, P. Duraivelu, vivek suguna, R. Arunkumar, M.s. Myleshan, M.b. Kongu Engineering College Perundurai Department of Computer Technology - PG Tamilnadu Erode638060 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur502313 India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai-62 India Kongu Engineering College Perundurai Tamilnadu Erode638 060 India

ISBN: (纸本)9798350340600

A vehicular Ad Hoc Network (vANET) is an effective method for addressing group-oriented services. It is possible to support such services through multicast routing. As a result, packet delivery ratios must be improved, delays must be reduced, and overheads must be reduced. Consequently, multicast routing protocols for vANETs must be stable and reliable. A Receiver based Multicast protocol is proposed in our paper that determines how multicast traffic should be forwarded. In the packet header, Rb Multicast stores a list of destinations. Multicast members to whom this packet is addressed are listed in this destination list. Consequently, there is no need to store tree state at intermediate nodes and no multicast tree is required. According to this study, the objective of the study was to predict network traffic by using the v2R dataset, and to use the receiving packets sent by vehicles to the RsUs as a measure of network activity. Our analysis of network traffic flow resulted in the prediction of network flow. Using ensemble classification metrics, we evaluated the data. simulated results indicate that the proposed model has a superior performance in terms of execution time and prediction accuracy compared to other algorithms. © 2023 IEEE.

关键词： vehicular ad hoc networks

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A novel CZP-bC-FLAsH ADC using zero phase bubble error robust controller for reliable vLsI circuits

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International Journal of Information Technology (singapore) 2022年第1期14卷 155-163页

作者： Kalaiyarasi, M. saravanan, s. Karunanithi, K. Raja, s.P. Department of Electronics and Communication Engineering V.S.B. Engineering College Karur India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore 632014 India

To process the data, accurate and reliable, analog to digital converter (ADC) is an essential element in most applications such as satellite communication, instrumentation and measurement, industrial automation, defence, and aerospace applications. In reliable vLsI circuits, to mitigate the wastage of power, high quality and high-resolution Clocked Flash ADCs are utilized. It converts an analog electrical signal into a digital discrete-valued signal using a parallel set of comparators and the transformation time is very fast in flash ADC. Even though many approaches were proposed to convert the analog signal to a digital signal, the analog signal could not correctly convert because of its error. This paper proposed a novel flash ADC namely Clocked Zero Phase bubble Error Controller Flash ADC (CZP-bC-Flash ADC) using Clocked-Improved Threshold Inverter Quantization (CITIQ) comparator and Zero Phase bubble Error Robust Controller (ZPbEC). CITIQ comparator uses two cascaded inverters, Preamplifier and two parallel sets of PMOs and NMOs transistors whereas ZPbEC calculates the transfer function of the system based on the changes of the input and locates the error. There is a noticeable basis for eliminating the circuit board that unnecessarily spoils energy. The proposed CZP-bC-FLAsH is executed using a tanner EDA with 250 nm size. When compared with QvC ADC technique, the error detection accuracy of proposed method has increased by 43% with the help of ZPbEC Controller. The simulation results showed that the proposed Controlled ADC method reduces power consumption by 63.32% when compared with QvC ADC method as well as compared with modified QvC ADC, it decreases to 60.98%. While the error controller controls the error signal by 56% compared with previous F-ADC and corrects the bits up to 4 and gives an efficiency of 97%. © 2021, bharati vidyapeeth's Institute of computer Applications and Management.

关键词： Clocked flash Comparator Error controller Neural compensation vLsI

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Fusion-based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and Image Enhancement in Low-Light IoT Environments 2

Fusion-based Digital Twin Framework with Convolutional Neura...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICs 2024

作者： Kumar, K. Ashok barmola, Pareshwar Prasad velmurugan, s.P. Manjunath, A.C. soni, Umang Maranan, Ramya Department of Physics B V Raju Institute of Technology Medak Narsapur India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

Image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PsNR) of 32.45 db and a structural similarity index (ssIM) of 0.87, reflecting an 18% improvement in PsNR and a 12% gain in ssIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction. © 2024 IEEE.

关键词： Wavelet decomposition

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Intelligent Campus safety Management using IoT and CNNs for surveillance, Access, and Emergency Response 3

Intelligent Campus Safety Management using IoT and CNNs for ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： sasirekha, v. Ramadevi, R. Presitha Aarthi, M. Malarvizhi, C. Mohankumar, N. velmurugan, s. Srm Institute of Science and Technology Faculty of Management Studies Vadapalani Campus Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

This paper introduces a novel strategy for improving campus security by combining Convolutional Neural Networks (CNNs) with Internet of Things (IoT) technology. For a smart campus safety management system, the suggested solution integrates advanced monitoring with access control and emergency response methods. IoT devices are placed in key locations across the campus to gather data in real-time. This data is then analyzed using CNNs to ensure strong monitoring and the identification of anomalies. Implementing CNNs allows for the detection of possible dangers, while access control measures guarantee protected entry points. The system makes use of AI-powered intelligent emergency response methods to optimize and speed up crisis management. Through the implementation of this all-encompassing strategy, want to build a safer and smarter educational ecosystem by guaranteeing the safety of campus settings. The research showcases the possibility for scalable deployment in varied campus contexts by demonstrating the usefulness of the proposed system via simulations and real-world deployments. © 2024 IEEE.

关键词： Anomaly detection

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Enhancing Data Privacy in IoT Cloud Environments with Trust Management

Enhancing Data Privacy in IoT Cloud Environments with Trust ...

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Recent Trends in computer science and Technology (ICRTCsT), International Conference on

作者： Himanshu Rai Goyal A. vanitha T. Karthikeyan Dr Priyabrata Adhikary R. saranya s. Kiran Kumar Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Management Studies Saveetha Engineering College Chennai Biomedical Engineering Sona College of Technology Salem Tamilnadu Department of Mechanical Engineering New Horizon College of Engineering Bangalore Department of Computer Science and Engineering V. S. B. Engineering College Karur Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Telangana

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

A new era of connectedness, convenience, and efficiency has arrived with the introduction of the Internet of Things (IoT), which has revolutionized the way we engage with the world around us. Data privacy in IoT cloud settings is an urgent problem, yet this shift is inevitable. The goal of this research is to improve data privacy by using trust management systems, and a technique to do so has been presented. Our method includes building a trust model to quantify the reliability of IoT devices and cloud service providers, and a privacy model to evaluate the potential dangers of data sharing. To find a happy medium between data value and data privacy, these trust and privacy evaluations lead to the idea of privacy-preserving data sharing. Our findings show that our method is useful, providing information on reliability, privacy risk, and the opportunity for businesses to make educated choices about data sharing. Our approach has far-reaching ramifications for many groups of people, including the IoT sector, businesses, regulators, and the general public. With the goal of improving and extending data privacy solutions in the ever-changing IoT world, future research paths include personalization, real-time adaption, scalability, user-centric controls, and ethical concerns.

关键词： Data privacy Cloud computing Regulators scalability Data models Internet of Things Reliability

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Energy Proficient and Dependable Cluster Routing in Wireless sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Hemavathi, s. Jeya Ganesan, J. Geetha, G. Kishore Kumar, K. Kohila, R. Sri Sairam Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India B v Raju Institute of Technology Department of Computer Science Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9781665462631

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed by an energy depletion observing system through the wireless sensor network (WsN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered by the captured nodes. Energy-efficient and Dependable Cluster Routing (EPDC) in WsN. This approach aims to reduce energy expenditure and enhance reliable routing in WsNs. The highest energy and highest available bandwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the observed information from the cluster member and then forwarded it to the bs through a relay node. The forwarder relay node is selected based on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to bs in the WsN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the obstacle and build the obstacle-aware route efficiently. The simulation results explain why the proposed method has better residual energy. In addition, it minimized both the network delay and the packet loss rate in the WsN. © 2023 IEEE.

关键词： bandwidth

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An Efficient Learning Assisted Malaria Parasites Identification Methodology based on blood smear Images

An Efficient Learning Assisted Malaria Parasites Identificat...

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2023 International Conference on Innovative Computing, Intelligent Communication and smart Electrical systems, ICsEs 2023

作者： Lalitha, s.D. Remya, P. Priyanka, R. Rajendran, N. sivakamasundari, N. R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal Chennai India Mailam Engineering College Department of Chemistry Mailam India S.A. Engineering College Department of EEE Chennai India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Information Technology Vandalur Chennai India Hindustan Institute of Technology and Sciences Department of Mechatronics Padur Chennai India

ISBN: (纸本)9798350319200

Malaria remains a global health challenge, necessitating accurate and efficient methods for the identification of malaria parasites from blood smear images. In this study, we propose an innovative approach for malaria parasites identification that leverages the power of deep learning and gradient boosting. Our methodology combines the feature extraction capabilities of the MobileNet convolutional neural network with the robust classification capabilities of XGboost. The MobileNet model is employed to extract discriminative features from blood smear images, providing a rich representation of the parasites' morphological characteristics. subsequently, XGboost is utilized as the classification model to distinguish between infected and uninfected blood samples based on these extracted features. This hybrid approach showcases remarkable accuracy and efficiency, offering a reliable solution for malaria diagnosis. Experimental results demonstrate the superiority of our methodology, achieving precise identification of malaria parasites while minimizing false positives. The proposed model takes the lead with an impressive accuracy score of 0.98, signifying its exceptional capability in distinguishing between different classes within the dataset. This model, tailored for the specific task at hand, utilizes a combination of innovative techniques to achieve superior *** innovative learning-assisted approach holds great promise for enhancing malaria diagnosis, particularly in resource-constrained settings, and represents a significant step towards combatting this life-threatening disease more effectively. © 2023 IEEE.

关键词： Malaria Parasites MobileNet XGboost

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