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4th International Conference on Signal Processing and Communication, ICSPC 2023

作者： Betsy, Sherine Wise Murugesan, Anitha Ganapathy, N. Bala Sundara Pughazendi, N. Computer Science and Engineering Panimalar Engineering College India Panimalar Engineering College Department of Cse India Information Technology Panimalar Engineering College India

ISBN: (纸本)9798350300772

The quick development of the Internet-of-Medical-Things (IoMT) brings the medical services environment into another reality comprised of tidy medical tools and applications that give numerous advantages, like remote clinical help, convenient organisation of drugs, constant checking, preventive consideration, and wellbeing instruction. Nevertheless, in spite of the important benefits, this fresh reality raises concerns about cybersecurity and solitude since susceptible IoMT gadgets can access and deal with independently collected patient information. Besides, the persistent advancement of cyberattacks, malware, and zero-day weaknesses requires the improvement of the fitting countermeasures. In this article, we illustrate an intrusion detection and prevention system (IDPS) that can ensure the medical care interchanges that depend on the hypertext transfer protocol (HTTP) and the transmission control protocol (TCP). The primary commitment of this study lies in the way that a functioning learning approach is demonstrated and taken on to retrain progressively the regulated classifiers behind the implemented IDPS. The assessment examination exhibits the effectiveness of this work compared with HTTP and TCP cyberattacks, illustrating how the whole exactness is enhanced in the different re-training stages. © 2023 IEEE.

关键词： Transmission control protocol

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Corner Key Point Extracted Deep Learning Framework Based Birds Species Classification 8

Corner Key Point Extracted Deep Learning Framework Based Bir...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Devi, M. Shyamala Jamal Mohammed, R. Hemanth, S. Harish Raj, B. Gajapathy, R. Kamalesh, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798331505462

Climate change and habitat destruction have caused the extinction of many bird species. Classifying bird species is a crucial aspect of ornithology, as it aids in the comprehension of species distribution, habitat needs, and environmental changes that impact bird populations. Ornithologists can use variations in bird species distributions to measure the health of a particular environment. Expert ornithologists have relied heavily on visual inspection, which can be tedious, time-consuming, and unreliable when classifying bird species. The development of technology can be used to categorize the bird species. This paper proposes Corner Key Filtered VGG16 (CKP-VGG16) that categorizes eight bird species with high accuracy. The execution was carried out using the 4000 bird images from the KAGGLE Birds species Dataset. First, the CKP-VGG16 model separates the images of birds, and then it labels each image. After data augmentation on the labeled images, 84,000 bird images are produced. The augmented images are transformed into grayscale versions, which are then used in filtering techniques to produce Corner Key Point Filtered (CKP) bird image. The performance is analyzed by applying the CKP filtered bird images to the current CNN models. The application demonstrates that the suggested CKP-VGG16 model performs significantly well in classifying bird species, with a high accuracy of 99.7%. © 2024 IEEE.

关键词： Birds

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Regularized Dense Layered LeNet towards Mushroom Type Classification 5

Regularized Dense Layered LeNet towards Mushroom Type Classi...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Devi, M. Shyamala Harivishnu, K.C. Suriya, Jaya V. Jayasuriya, K. Jason, Infant G.A. Farih, M.D. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350361155

Among the edible varieties of fungi, mushrooms possess the strongest nutrients found in the plant. Nevertheless, because of their great demand for food and significant benefits to medical research, it is imperative to identify the different varieties of mushrooms within the current species. The deep learning technology has recently rendering the CNN models towards classification of various object in interdisciplinary fields. This paper proposes Regularized Dense Layered LeNet (RDL-LeNet) that categorizes the mushroom types with high accuracy. The Mushrooms Classification Dataset, which includes 3150 mushroom pictures was used for this execution. The Mushrooms Classification dataset was fitted with the existing CNN models to select the best CNN model. To determine the most efficient CNN framework, the Mushrooms Classification dataset was fitted using the available CNN models. When compared to the other CNN model, the LeNet performs better, with an accuracy rate of 90.67%. LeNet has now been chosen to adjust the quantity of dense layers. LeNet typically consist of three convolutions, two pooling layers, and two dense layers. In order to suggest the recommended framework RDL-LeNet, the LeNet is now changed by adding an additional dense layer that is made up of a single block of batch normalization, relu optimization, and drop out at the end that further compresses the output into vector size of 84. The RDL-LeNet is made up of three convolutional layers, two pooling layers, and three dense layers. The third dense layer consists of a single block that is made up of batch normalization, relu optimization, and dropout, ultimately leading to the final output layer. The suggested RDL-LeNet and the current CNN have been implemented with the Mushroom Classification Dataset. The experiment demonstrates that the suggested RDL-LeNet model works better in the categorization of mushroom types, with a high accuracy of 98.68%. © 2024 IEEE.

关键词： Fungi

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Normalized AlexNet Deep Learning based Edibility Classification of Sporocarp 2

Normalized AlexNet Deep Learning based Edibility Classificat...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Irene, B. Kanagavalli, A. Kousika, A. Joy Praisy, E. Humaira, S. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

The manufacturing and dissemination of spores is the main purpose of sporocarps which is the specialized type of configuration found in freshwater plants of the Salviniales family. Though sporocarp are mainly used for reproduction, some of them are consumable by the animals and birds for the extraction of rich nutrients. Sporocarp categorization is an extremely difficult task as various species of sporocarp share significant appearance. Some sporocarp may be poisonous that lead to brain damage and infection to the animals. This paper proposes Normalized AlexNet that classifies the edible and poisonous sporocarps with high efficiency. For the sake of this investigation, 1800 sporocarp images from the Sporocarp edible Dataset were used. The best CNN model was chosen by applying the Sporocarp edible dataset to the current CNN models. With an accuracy of 89.67%, AlexNet performs better than the other CNN models. AlexNet has been chosen to fine-tune its single dense layer at this level. The AlexNet typically consists of two blocks with a single convolution including a max pooling layer, one block with three convolutions along with a max pooling layer, and one last block with three dense layers. The proposed Normalized AlexNet was now suggested by modifying the second dense layer with a single block of batch normalization, relu optimization, and drop out with a vector size of 4096. The suggested Normalized AlexNet and the current CNN have both been fitted with the Sporocarp Edible Dataset. The demonstration illustrates that the Normalized AlexNet model performs better in the edibility categorization of sporocarp, with a high accuracy of 98.76%. © 2024 IEEE.

关键词： Convolution

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An Integrated Blockchain Framework for Secure Data Sharing in IoT Fog Computing

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清华大学学报自然科学版（英文版） 2025年第3期30卷 957-977页

作者： Peda Narayana Bathula M.Sreenivasulu Department of Computer Science Engineering Jawaharlal Nehru Technological UniversityAnanthapur 515002India Department of Computer Science Engineering Matrusri Engineering CollegeHyderabad 500059India

The importance of secure data sharing in fog computing is increasing due to the growing number of Internet of Things(IoT)*** article addresses the privacy and security issues brought up by data sharing in the context of IoT fog *** suggested framework,called"BlocFogSec",secures key management and data sharing through blockchain consensus and smart *** existing solutions,BlocFogSec utilizes two types of smart contracts for secure key exchange and data sharing,while employing a consensus protocol to validate transactions and maintain blockchain *** process and store data effectively at the network edge,the framework makes use of fog computing,notably reducing latency and raising *** successfully blocks unauthorized access and data breaches by restricting transactions to authorized *** addition,the framework uses a consensus protocol to validate and add transactions to the blockchain,guaranteeing data accuracy and *** compare BlocFogSec's performance to that of other models,a number of simulations are *** simulation results indicate that BlocFogSec consistently outperforms existing models,such as Security Services for Fog Computing(SSFC)and Blockchain-based Key Management Scheme(BKMS),in terms of throughput(up to 5135 bytes per second),latency(as low as 7 ms),and resource utilization(70％to 92％).The evaluation also takes into account attack defending accuracy(up to 100％),precision(up to 100％),and recall(up to 99.6％),demonstrating BlocFogSec's effectiveness in identifying and preventing potential attacks.

关键词： BlocFogSec Internet of Things(IoT) fog computing blockchain

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Employing Seasonal Decomposition for Dynamic Stock Price Predication within the realm of web based application 4

Employing Seasonal Decomposition for Dynamic Stock Price Pre...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jennifer, D. Jackulin, T. Retika, S. Samyuktha, G. Saima Nooreen, S. Reshmi, R. Panimalar Engineering College Computer Science and Engineering Tamil Nadu Chennai123 India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This research endeavor constitutes an intricate fusion of advanced quantitative methodologies within a cutting-edge web-based predictive analytics framework tailored for stock market forecasting. The amalgamation of fundamental analysis metrics encompasses a meticulous examination of financial ratios, earnings reports, and economic indicators, affording a nuanced comprehension of underlying asset valuations. Concurrently, the incorporation of elaborate technical indicators scrutinizes intricate market trends, oscillators, and moving averages, offering a granular perspective on potential entry and exit points for investors. Augmenting this analytical prowess is the integration of sentiment analysis, where OpenAI's state-of-the-art language models decode market sentiment from an expansive corpus of real-time stock news. This linguistic analysis discerns subtle nuances, capturing market sentiment dynamics and providing an additional layer of insight for predictive modelling. Furthermore, the research introduces a pioneering approach to time series analysis, leveraging sophisticated algorithms to dissect historical stock data. This entails the identification of latent patterns, trends, and seasonality, significantly enhancing the temporal dimension of predictive modelling. The holistic synergy of these diverse quantitative models unfolds within a unified web application, constituting a transformative paradigm in algorithmic trading and investment strategies. In conclusion, empirical analyses validate the efficacy of this integrative approach, substantiating its potential to redefine the landscape of predictive analytics within the dynamic and complex milieu of stock market prognostication. This comprehensive platform not only enhances the precision of market predictions but also provides investors and traders with a sophisticated toolkit for navigating the intricacies of modern financial markets with informed and strategic decision-making. © 2024 IEEE.

关键词： Predictive analytics

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ReLU Activated Attention UNet Encipher Decipher Framework based Species Classification of Butterfly

ReLU Activated Attention UNet Encipher Decipher Framework ba...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Devi, M. Shyamala Jeeva, R. Jagadheeswaran, R. Kumar V, Hemand Erwin Nicholas, M. Keerthi Vasan, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350363289

The recognition and categorization of butterflies is crucial for the preservation of butterfly species in the fields of entomology, computer vision and deep learning. Environmentalists have long utilized butterflies as model organisms in the research on the effects of habitat degradation, dispersion, and warming temperatures. This paper recommends Relu Activated Attention UNet (RAA-UNet) that categorizes the nine butterfly species with high precision. The Butterfly Dataset contains 1664 butterfly images that were used in this investigation. The dataset consists of 832 photos of butterflies and 832 related images that have been segmented. The proposed RAA-UNet starts by creating masked butterfly pictures by masking the original image with a segmented butterfly image. To classify the butterfly species, the masked butterfly pictures are assigned to Attention UNet with Decoder and Encoder combined with the Attention gate and activated using Relu Activation function. The proposed RAA-UNet and conventional CNN models are fitted to the masked butterfly pictures. As demonstrated by the experiments, the RAA-UNet model works better with a high accuracy of 97.75% in the classification of butterfly species. © 2024 IEEE.

关键词： Invertebrates

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An Approach for Enhancement of Retail Shops and Rental System 5

An Approach for Enhancement of Retail Shops and Rental Syste...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Monisha, G.S. Daniel George, S. Elavarasar Eshwanth, A.G. Bharathwaj, M. Panimalar Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350338287

This M-commerce is a rapidly growing trend that offers many benefits for both businesses and customers. It is convenient, secure, and allows businesses to reach more customers than ever before. As technology advances, M-commerce will become even more integrated into our lives. The future of mobile commerce is bright, and businesses should take advantage of this opportunity to reach more customers and increase sales. By taking the time to create a mobile-friendly website and leveraging the latest technologies, businesses can ensure a successful m-commerce experience for their customers. The main purpose is to take measure to reduce the interaction of agent between Buyer and Seller which makes profit to the third party agent. The existing system results in downturn in economy of retail shops as well as middle level family. The Proposed System will make more interaction between Buyer and Seller by making chat box to communicate Seller for more details. This also makes Profit to Retail shops and make a enhancement in Rental system in E-commerce. © 2023 IEEE.

关键词： Profitability

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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