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Trust aware fuzzy clustering b.s.d reliable routing in Manet

Measurement: sensors 2024年 33卷

作者： Edwin singh, C. sharon Priya, s. Muthu Kumar, b. saravanan, K. Neelima, A. Gireesha, b. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumara Coil India Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India School of Computing and Information Technology REVA University Bengaluru India Department of Information Technology R.M.D. Engineering College Chennai India Bhimavaram India Department of Electronics and Communication Engineering Sridevi Institute of Engineering and Technology Karnataka Tumkur572106 India

Mobile Ad Hoc Network (MANET) is a wireless ad hoc network that can be routed over a Link Layer Ad Hoc network. In a MANET, nodes are able to freely and adaptably communicate with one another. Despite this, MANET remains vulnerable to serious security threats that are difficult to address with existing security measures. A MANET routing protocol must consider network dynamics and energy constraints, which makes it an optimization problem. Therefore, the security of MANETs must be strengthened through the development of secure routing protocols. In this research, an Optimal Fuzzy Clustering and Trust-b.s.d Routing (OFC-TR) can reduce consumption of energy, latency, and enhance network security and longevity. The proposed OFC-TR technique is achieved in three steps. The initial phase is to organize and select cluster heads using the improved Fuzzy C-means (IFCM) method, which solves the issue of unequal distribution by assigning each sensor a level of cluster membership. The nodes will be effectively clustered using this set of rules, and the b.s. cluster head will be selected. The second segment includes Trust value calculation using a Fuzzy Cognitive Medium (FCM) which considers the values of direct and indirect trust. The third section includes the ideal routing using a bacteria Foraging Algorithm (bFA) which is implemented for successful optimal control, harmonic estimation and the transmission loss reduction. The proposed approach is implemented in MATLAb. The efficacy of the proposed OFC-TR strategy is determined by using the assessment metrics such as network lifetime, packet delivery, and energy efficiency. The proposed method achieves a better network lifetime of 52.88 %, 44.34%, and 9.42%, than E-TDGO [32], s2ALbR [33], and 3LWT-GWO [35] respectively. © 2024

关键词： Routing protocols

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Advanced hybrid ensemble gain ratio feature selection model using machine learning for enhanced disease risk prediction

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Informatics in Medicine Unlocked 2022年 32卷

作者： Pasha, syed Javeed Mohamed, E. syed Department of Computer Applications School of Computer Information and Mathematical Sciences B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computer Information and Mathematical Sciences B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

Currently, there is an increased need for employing machine learning (ML) and data mining in the healthcare system domain, applications of which play a pivotal role in providing beneficial knowledge to society by utilizing the available data. There are disease risk prediction models that either do not use feature selection or use traditional feature selection models—filter, wrapper, and embedded—which have limitations such as, not using ML, using single evaluation metric, and working on imbalanced datasets. Apart from this, there is a great scope for enhancement in the prediction performance. To address these issues, an advanced hybrid ensemble gain ratio feature selection (AHEG-Fs) model is proposed, which consists of four major feature selection techniques: an ensemble feature selection, a gain ratio feature selection, a backward feature elimination, and an area under the curve (AUC)—an additional evaluation metric of the novel feature reduction along with accuracy, for rob.s. feature selection aimed at effective disease risk prediction. The subsets of important and highly ranked features are obtained with the first two techniques. Then, the proposed model is aligned with nine ML algorithms. Additionally, in the third and fourth techniques of the proposed model, the AUCs are evaluated for the aforementioned ML algorithms and the backward feature elimination is applied to remove the redundant features, resulting in the acquisition of the b.s. subsets of highly contributing features that produce the highest precision results. Thus, four benchmarked heart disease datasets—Cleveland, Hungarian, statlog, and switzerland of the University of California, Irvine ML repository—are used, and the results are encouraging. The highest AUC and accuracy of 99.00% and 95.47%, respectively, are achieved, with 46.15% of features reduced. A 6.18% higher accuracy than recent studies was achieved with convergent speed. © 2022

关键词： Area under the curve (AUC) Cardiovascular disease (CVD) Data mining Disease risk prediction model Feature selection models Machine learning

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security for wireless sensor networks using cryptography

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Measurement: sensors 2023年 29卷

作者： Ramadevi, P. Ayyasamy, s. suryaprakash, Yalla Anilkumar, Chunduru Vijayakumar, s. sudha, R. Institute of Aeronautical Engineering Dundigal Hyderabad India School of Computer Science and Engineering VIT University Vellore Campus India Department of Computer Science and Engineering MVGR College of Engineering Andhra Pradesh Vizianagaram India Department of Information Technology GMR Institute of Technology Rajam Andhra Pradesh Vizianagaram India Department of English B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai48 India Department of Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad India

WsNs (Wireless sensor Networks) are becoming more and more capable with each passing day. Widespread usage of WsNs is helping mitigate many dangers. Protecting these networks from risks need proper security measures. This paper offered a security strategy that makes use of IEKC (Improved Elliptic Key Cryptography). Data encryption and decryption are accomplished via IKEC. A major feature of this technique is that node's source codes maintain Private Keys which protect other keys in non-volatile memories. The suggested technique prevents replay, Dos and sybil attacks. In this node, cluster chiefs are chosen using CsO (Chicken swarm Optimization) and nodes are organised according to network size. According to the findings of the simulation, this security mechanism effectively counters different attacks with fewer resources, and it also significantly strengthens the network's resistance to node capture. Results from experiments show that the suggested model is effective regarding PDR (packet delivery ratio), end-to-end latency, throughputs, and energy consumptions. © 2023

关键词： sensor nodes

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ILAPP: An Efficient Design of sign Language Recognition system using Intelligent Learning Assisted Prediction Principles

ILAPP: An Efficient Design of Sign Language Recognition Syst...

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

作者： K. Radha R. Harikrishnan s. Niresh Kumar Hubert Mary. L N. Rajendran Department of CSE Dr.Mahalingam College of Engineering and Technology Pollachi Coimbatore Department of ECE S.A. Engineering College Chennai Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai Department of ECE Jeppiaar Institute of Technology Chennai Department of Information Technology B.S. AbdurRahman Crescent Institute of Science and Technology Vandalur Chennai

In this study, we introduce ILAPP (Intelligent Learning Assisted Prediction Principles), an innovative design for a sign Language Recognition system. ILAPP harnesses the power of Convolutional Neural Networks (CNN) for efficient feature extraction and employs the renowned LeNet-5 Model for rob.s. classification. sign language recognition is a critical technology that bridges communication gaps for individuals with hearing impairments. Our system, ILAPP, offers an efficient and effective solution to this challenge. The core of ILAPP lies in its utilization of CNNs to extract essential features from sign language images, capturing intricate patterns and nuances crucial for accurate classification. The LeNet-5 Model is employed for classification, benefiting from its proven capabilities in image recognition tasks. Our research focuses on enhancing the accuracy and efficiency of sign language recognition, aiming to provide a practical tool for the deaf and hard-of-hearing community. ILAPP represents a significant advancement in this field, and its promising results underscore its potential for real-world applications. This study showcases the fusion of intelligent learning principles with state-of-the-art models, highlighting the importance of leveraging AI in addressing accessibility and communication *** our study, the proposed model demonstrates an impressive accuracy rate of 98.23% when compared to existing models. This high accuracy rate signifies the exceptional performance and effectiveness of our model in sign language recognition.

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Clinical big Data Predictive Analytics Transforming Healthcare: - An Integrated Framework for Promise Towards Value b.s.d Healthcare

Clinical Big Data Predictive Analytics Transforming Healthca...

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作者： Naqishbandi, Tawseef Ahmad Ayyanathan, N. Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India

Modern data driven clinical healthcare application system development requires interdisciplinary and technical expertise to find hidden values from large volume of clinical data. Predictive big data analytics in combination with other technologies like machine learning is growing and is attracting much attention. Therefore, there is a need of integrated healthcare framework which can utilize the power of Predictive analytics, big data;Machine learning. In this paper, we have presented an integrated frame work for handling clinical data, which can act as reference for adoption and integration of clinical data. The purpose of the proposed integrated framework for Healthcare Clinical big data predictive analytics is to explore and combine the power of various analytical techniques and technologies so as to provide a comprehensive solution for value b.s.d healthcare. This framework is further committed to transform our perspective towards value b.s.d healthcare. © 2020, springer Nature switzerland AG.

关键词： Predictive analytics

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Design of adaptive hybrid classification model using genetic-b.s.d linear adaptive skipping training (GLAsT) algorithm for health-care dataset

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Advances in Computational Intelligence 2022年第2期2卷 1-10页

作者： Ramasamy, Manjula Devi Periasamy, Keerthika Periasamy, suresh Muthusamy, suresh Panchal, Hitesh solanki, Pratik Arvindbhai Panchal, Kirti Department of Computer Science and Engineering Kongu Engineering College (Autonomous) Erode India Department of Information Technology Kongu Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Kongu Engineering College (Autonomous) Erode India Department of Mechanical Engineering Government Engineering College Patan India B.S. Patel Polytechnic Ganapt University Mehsana India B & B Polytechnic Vallabh Vidyanagar Anand India

Machine-learning techniques are being used in the health-care industry to improve care delivery at a lower cost and in less time. Artificial Neural Network (ANN) is well-known machine-learning techniques for its diagnostic applications, but it is also increasingly being utilized to guide health-care management decisions. At the same time, in the healthcare industry, ANN has made significant progress in solving a variety of real-world classification problems that range from linear to non-linear and also from simple to complex. In this research work, an Adaptive Hybrid Classification Model named as Genetic-b.s.d Linear Adaptive skipping Training (GLAsT) Algorithm has been proposed for the health-care dataset. It has been designed as two-stage process. In first stage, Genetic Algorithm (GA) is adapted to optimize the Learning rate. After optimizing the Learning rate, the optimal Learning rate has been set to the ANN model is ŋ = 1e−4. In the second stage, The training process is carried out using the Linear Adaptive skipping Training (LAsT) algorithm, which reduces the total training time and thus increases the training speed. As a result, the highlighted characteristics of LAsT have been integrated with GA to accomplish rapid classification and enhance computational efficiency. On 8 different health-care datasets extracted from the UCI Repository, the proposed GLAsT algorithm outperforms both the bPN and LAsT algorithms in terms of accuracy and training time, according to simulation results. The result analyses have proved that the efficiency of this proposed GLAsT Algorithm outperforms over the existing techniques such as bPN and LAsT in terms of accuracy and training time. On various datasets, experimental results show that GLAsT improves accuracy from 4 to 17% over bPN training algorithm and reduces overall training time from 10 to 57% over bPN training algorithm.

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Metaheuristic Algorithm for Automatic Cruise Control system 3

Metaheuristic Algorithm for Automatic Cruise Control System

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3rd International Conference on smart Electronics and Communication, ICOsEC 2022

作者： Prasanna, V. Nelson, A. Hnaumanthakari, sudheer Kumar, V. Kamal Kirubakaran, s. Kumar, M Jogendra B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Tamil Nadu Vandalur600048 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India Faculty of Science and Technology Icfai Foundation for Higher Education Department of Electronics and Communication Engineering Telangana Hyderabad501503 India Kpr Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai641407 India Cmr College of Engineering and Technology Department of Computer Science and Engineering Telangana Kandlakoya501401 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Accidents are less likely to happen when employing self-driving cars. However, automotive applications require more time and cost, simulation tool is a much more cost-effective and time-saving option. To reduce driver fatigue and ensure pedestrian safety, a vehicle's cruise control depends heavily on control engineering. To aid in vehicle safety, the automotive manufacturing sector is forecast to develop a range of cruise control strategies. because of elevated traffic on the roads, there is a significant need for the development of Automatic Cruise Control (ACC). The feedback control system's architecture is essential for the achievement of ACC. This paper presents the controller tuning techniques for designing an ACC system. Researchers frequently utilized Ziegler-Nichols techniques in tuning Proportional Integral Derivative (PID) controllers. These tuning processes may be time-consuming and difficult for a complex nonlinear structure. This paper shows how to employ metaheuristic methodologies to quickly fine-tune the PID controller's parameter. The first stage is to construct a mathematical model of the cruise control system on the vehicle. The PID settings are then optimized using the firefly and cuckoo search optimized algorithm. both metaheuristic PID controllers achieve the necessary vehicle velocity, according to simulations. From the results, it also inferred that the designed controller helps the system to attain the required velocity smoothly, with negligible overshoot and minimal rise time. © 2022 IEEE.

关键词： Optimization

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An efficient text-b.s.d image retrieval using natural language processing (nlp) techniques 6th

An efficient text-based image retrieval using natural langua...

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6th International Conference on Information system Design and Intelligent Applications, INDIA 2019

作者： Ashok Kumar, P.M. subha Mastan Rao, T. Arun Raj, L. Pugazhendi, E. Department of Computer Science Engineering K.L. University Vaddeswaram522502 India Department of Computer Science Engineering CMR Technical Campus Hyderabad501401 India Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Department of Information Technology Anna University MIT Campus Chennai600048 India

ISBN: (纸本)9789811553998

The image retrieval system is a computer system for browsing, searching and retrieving images from a large datab.s. of digital images or text. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive. To address this, there has been a large amount of research done on automatic image annotation. With the rapid development of information technology, the number of electronic documents and digital content within documents exceeds the capacity of manual control and management. The usage of images is increased in real time. so, the proposed system concentrates on retrieving image by using the text-b.s.d image retrieval system. Text documents are given as input to the preprocessing stage, and features are extracted using TF-IDF. Finally, document clustering method can be used to automatically group the retrieved documents into list of meaningful categories. Document clustering clusters the document of different domains and latent Dirichlet allocation (LDA), each document may be viewed as a mixture of various topics where each document is considered to have a set of topics, and after that relevant documents are retrieved and then images of those relevant documents are retrieved. © springer Nature singapore Pte Ltd 2021.

关键词： K-means clustering

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Applying convolutional attention mechanisms and Human Memory search for effective English-Urdu translation

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engineering Applications of Artificial Intelligence 2025年 155卷

作者： Jayasakthi Velmurugan, K. Faheem Nikhat, H. suresh, K. Hemavathi, s. Kavitha, V. Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu India Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science & Technology Vandalur Tamilnadu 600 048 India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai603203 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai603203 India

In recent decades, machine translation has become a prominent area of research, with the primary objective of overcoming language barriers. Early approaches primarily centered on word-for-word translation between source and target languages. However, there has been a shift towards data-driven models, such as neural machine translation and statistical methods, driven by advancements in Artificial Intelligence (AI), communication and computing technologies. This paper introduces a novel Convolutional Attention Mechanism-b.s.d Human Memory search (CAM-HMs) algorithm for translating English into Urdu, to achieve high-quality and effective machine translation. The proposed model consists of several key phases, including pre-processing, sentence padding, word embedding, encoding, decoding, and target text generation. A new spider web-b.s.d search strategy is also incorporated to enhance the translation process in neural machine translation. The performance is evaluated using a UMC005 English-Urdu dataset, Parallel corpus for English & Urdu language, and the Roman Urdu to English Translation Dataset. Various automatic evaluation metrics such as the bilingual Evaluation Understudy (bLEU), National Institute of standards and Technology (NIsT), Word Error Rate (WER), Accuracy, Precision, Recall, and F-Measure are used to assess the model's efficiency, and its output is compared to that of Google Translate. The proposed model achieves an average bLEU score of 82.14 %, NIsT of 79.51 %, WER of 2.77 %, Accuracy of 98.99 %, Precision of 98.95 %, Recall of 98.90 %, and F-Measure of 98.92 %, demonstrating its effectiveness in producing high-quality machine translation results. © 2025

关键词： Deep learning

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Imputation Using Machine Learning Techniques 4

Imputation Using Machine Learning Techniques

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4th International Conference on computer, Communication and signal Processing, ICCCsP 2020

作者： sivakani, R. Ansari, Gufran Ahmad Deemed to University B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Vandalur India

ISBN: (纸本)9781728165097

In today's environment to find the missing value in the data set has become the biggest challenge for the industry people, scientists, academicians and for the researchers. With the incomplete dataset we are not able to apply algorithm to find the result. If the dataset is not complete then only we can apply algorithms and then result can be analyzed to get the efficient output. In this paper we have analyzed some of the missing value generation techniques and algorithms and also the result is compared with among techniques and algorithms which gives the b.s. solution for imputing the data in a incomplete dataset. We have considered some of the missing value handling techniques such as deletion, imputation, EM algorithm, KNN algorithm and Random Forest. Also applied on the incomplete dataset MRI of the brain available in the Research Center of Alzheimer's disease in Washington University. © 2020 IEEE.

关键词： Decision trees

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