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检索条件"机构=B.E. Computer Science Engineering"

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Detection and Categorization of Conflict Flows Within SDN environments using Machine Learning Approach 2

Detection and Categorization of Conflict Flows Within SDN En...

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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Kousalya, S. Sundararajan, Kanthimathi Vasudevan, I. Jaichitra Sheela, L. Jaba Thyagarajan, Sarunya Panimalar Engineering College M.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

Software-Defined Networking (SDN) is known for its remarkab.e.versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. 'As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.' © 2024 Ieee.

关键词： Network security

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Ob.e.t Recognition based on Deep Learning Algorithms using embedded IoT with Interactive Interface 7

Object Recognition based on Deep Learning Algorithms using E...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： borde, Swapna Patil, Chandan Sonawane, Chinmay Singh, Mankrit Vidyavardhini's College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini's College of Engineering and Technology Vasai India

ISBN: (纸本)9798350397253

Ob.e.t detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time ob.e.t detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have b.e. implemented and studied in terms of ob.e.t detection. To refer to a few the YOLO family, RCNN family, SSD etc. This research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different ob.e.ts and items. The IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on ob.e.t detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) The web application is running on is divided into two parts, learning and practice. The learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the ob.e.t and reads out its name in the language the user is learning. The practice has the same setup, with the application asking the user to show a particular ob.e.t. Points are awarded for correct guesses, making the learning process more interactive and involving. © 2023 Ieee.

关键词： Ob.e.t detection

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Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions

Data dimensionality reduction techniques for Industry 4.0: R...

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作者： Chhikara, Prateek Jain, Nikhil Tekchandani, Rajkumar Kumar, Neeraj B.E. Computer Engineering Thapar Institute of Engineering and Technology Patiala India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

From the last few years, we have witnessed the fourth generation industrial revolution (Industry 4.0), impact of which will be seen in the years to come in various disciplines such as healthcare, transportation, IoT, smart grid, autonomous vehicles, and image processing. These applications in Industry 4.0 may have data in the form of images, speech signals, videos having high dimensions containing multiple dimensions to represent data along different axis. So, the complexity of data processing increases with an increase in the dimensions of the dataset. Complexity can be viewed in terms of detecting and exploiting the relationships among different features of the dataset. These complexities among different attributes can be reduced with the help of dimensionality reduction techniques. These techniques reduce the dimensions from the original input dataset to a lower dimensional dataset. Dimensionality reduction methods are broadly categorized into two types as feature extraction and feature selection. In feature selection method, out of the original set, a sub.e. of features are identified to get a smaller sub.e. which can be used to build the model whereas, the feature extraction method reduces the dataset of high dimensions to a lower dimension space, that is, a space with a less number of features having different values in comparison to the original dataset. Keeping focus on these points, in this article, we have compared and analyzed different data dimensionality reduction techniques which reduce the dimensions of a large and complex dataset during data processing. In addition, we have discussed various data dimensionality reduction techniques and compared these techniques with respect to various parameters. The comparison among various techniques provides insights to the readers about the applicability of a specific technique to the stand-alone or a group of applications. © 2020 John Wiley & Sons, Ltd.

关键词： Smart power grids

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battery Monitoring System with IoT Integration

Battery Monitoring System with IoT Integration

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Advancements in electrical, electronics, Communication, Computing and Automation (ICAeCA), International Conference on

作者： Sanjith Krishna S Premchand V H Shri Ram b Sakthi Satchidhanandan S Anitha N Hariharan S.V B.E. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

battery Monitoring Systems (bMS) have played a pivotal role in our trajectory towards greener and sustainab.e.living, especially in our commutation methods. The rise of electric cars and two-wheelers has put renewed focus on high density energy storage and their safety. existing bMS’s often provide the bare minimum in terms of protection and regulation of energy, often falling short of certain safety standards or lack thereof. Within this research, we task ourselves to identify and provide an alternative, viab.e. cost-effective method to ensure safe monitoring and especially safe debugging of faulty battery packs. We employ off-the-shelf components and integrate them into existing bMS to provide an easier way for debugging via Internet of Things (IoT) to allow local technicians access to the same information as the Original equipment Manufacturer (OeM) themselves. Therefore, our aim is to provide a platform for people to troub.e.hoot their bMS’ on electric vehicles and perform predictive maintenance via remote monitoring, providing the same flexibility of a regular combustion car.

关键词： Temperature measurement buildings Debugging Programming electric vehicles batteries State of charge Internet of Things Remote monitoring Predictive maintenance

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Understanding Structure Of LLM using Neural Cluster Knockout 5

Understanding Structure Of LLM using Neural Cluster Knockout

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： bhile, Pranav Arvind Maes, Pattie B.E. Computer Science & M.Sc. Physics Bits Pilani Goa Campus Sancoale Goa India Mit Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9798350385649

This research work presents a groundb.e.king approach at the intersection of neuroscience and generative Artificial Intelligence (AI), focusing on the application of neuroscience techniques to neural networks, specifically Large Language Models (LLMs). Central to this study is the concept of 'neural cluster knockout' in LLMs, a method inspired by lesion studies in neuroscience involving the systematic removal of neuron clusters to decipher their role within the model. The research underscores the opaque nature of neural networks, particularly LLMs, which are often critiqued for their 'black box' operation. by adopting neuroscience principles, particularly lesion studies, this paper aims to illuminate the inner workings of neural networks, enhancing our understanding of their functionalities. This is crucial in an era increasingly reliant on AI in various sectors, where insights from this study could lead to the development of more efficient, transparent, and accountab.e.AI systems. Methodologically, this study involved Principal Component Analysis (PCA) and neural cluster knockout through iterative zeroing, applied to the Large Language Model named LLaMA. This approach enab.e. the identification of significant neuron clusters and their functional impacts when deactivated. The results reveal both critical and redundant neurons within LLMs, demonstrating that some clusters are vital for accuracy, while others may impede efficiency or contribute to errors. This research contributes significantly to the AI field, offering a novel perspective on the intricate architecture of LLMs. It lays a foundation for future advancements in AI, envisioning refined and efficient LLMs capab.e.of more accurate and reliab.e.performance. © 2024 Ieee.

关键词： efficiency

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Identification of Therapeutic Plants Using Machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on emerging Trends in Information Technology and engineering, ic-eTITe 2024

作者： Rohini, S. Keerthana Shree, M.U. Jayalakshmi, R. Malathi, M. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 Ieee.

关键词： Machine learning

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A Stacked Model for Approving bank Loans

A Stacked Model for Approving Bank Loans

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Networking and Communications (ICNWC), International Conference on

作者： G.Asish Preetham A. Poornachand S.bangaru Kamatchi Department Of Computer Science Engineering (B.E.) Sathyabama Institute of Science and Technology Chennai India

Financial institutions in the United States provide a wide range of products and services but interest earned on customers’ credit card balances is their primary source of revenue. That way, they may profit from the income collected on the money they lend out. The profitability of a bank is very sensitive to the performance of its loan portfolio, namely to the amount to which borrowers are meeting their repayment obligations. The bank’s NPAs may be lowered if it can identify potential loan defaulters in advance. Therefore, it is crucial to investigate this occurrence. Modern studies have demonstrated that there are a plethora of approaches to the question of preventing loan default. However, it is crucial to investigate the characteristics of the various approaches and compare them in order to make the most accurate forecasts possib.e.and so maximize earnings. The topic of forecasting loan defaulters is studied using the Logistic regression model, a key method in predictive analytics. The Kaggle data is used for analytics and modelling. Several models of hr have b.e. run, and various performance metrics have b.e. calculated. Sensitivity and specificity are two examples of performance metrics used to evaluate the models. It has b.e. proved conclusively that the model yields varying outcomes. The model is slightly better because it takes into account factors (client ob.e.vab.e.traits like age, intent, good credit, sum, timeframe, etc.) that should be considered to accurately determine the likelihood of insolvency on loan in addition to information about the customer’s checking account (which shows the customer’s wealth). Thus, the proper clients to be addressed for loan giving may be simply recognised using a lr technique, which evaluates the chance of default on credit. A bank shouldn’t only look for wealthy borrowers, the model suggests; there are plenty of other factors to consider when making credit decisions. To put our plan into action, we’ve chosen to work with

关键词： Measurement Knowledge engineering Analytical models Profitability Stacking Machine learning Predictive models

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Study on Machine Learning and back Propagation for Crop Recommendation System

Study on Machine Learning and Back Propagation for Crop Reco...

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Sustainab.e.Computing and Data Communication Systems (ICSCDS), International Conference on

作者： Polu Shrikhar Reddy bollimuntha Amarnath M Sankari Dept of Computer Science Engineering (B.E.) Sathyabama Institute of Science and Technology Chennai India

India may be the largest exporter of farm products, but the country’s farms are not very productive. Inadequate agricultural production means farmers bring home much less money. The producers need a performance improvement if there is a rise in their revenue. Farmers may boost output by cultivating land more effectively by first determining what will thrive there. The land’s potential production can be enhanced by growing the correct crops. As a result, crop recommendation algorithms may be quite useful for agriculturalists. Crop yields are influenced by a wide variety of external influences. The production is affected by environmental conditions like as heat, moisture, soil pH, precipitation, and the availability of nutrients like potash, ammonium, & phosphate. There is a lot of confusion among farmers regarding which crops should be produced in particular regions to get the highest possib.e.yield and financial return. As a result, the purpose of this study is to show how a ml algo may be used to forecast crops and associated prices. Soil is fundamental to the proper functioning of the terrestrial ecosystem, and its primary duty is the cultivation of crops for the purpose of providing food for the world’s ever-increasing population. Yet global soil deterioration poses a serious challenge to food security. The stresses of urbanization and industry, among others, contribute to soil degradation. Habitat loss, altered green spaces, soil erosion, unchecked livestock, illegal dumping, and ineffective land management are all major contributors to soil deterioration. More than 1.5 billion people are in danger of losing their way of life due to the rapid pace of land degradation, which now stands at 24 percent (350M ***). In this situation, keeping up with the rising demand for food is a prob.e. made more difficult by the need to maintain soil quality). As a result, maintaining agricultural output and adapting to the uncertainties brought by climate change needs constant

关键词： Degradation Uncertainty Soil measurements Sociology Crops Production Soil

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Predicting Disasters from Tweets Using GloVe embeddings and beRT Layer Classification 1

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11th International Advanced Computing Conference, IACC 2021

作者： Ranade, Aabha Telge, Saurav Mate, Yash B.E. Computer Engineering Vivekanand Education Society’s Institute of Technology Mumbai India MS Computer Science University of Southern California California United States

ISBN: (数字)9783030955021

ISBN: (纸本)9783030955014

Twitter, a social media platform, has quickly become one of the most reliab.e.sources of news and other information. With an ever-increasing number of users and tweets, it’s feasib.e.to use Twitter data to learn about a variety of interesting things that are happening around us. During a disaster, people can get real-time information from Twitter to give and receive help. This paper deals with the extraction of Twitter data in order to identify the tweets that give information about disasters. This is essentially a binary classification prob.e.. GloVe Global Vectors for Word Representation embeddings have b.e. implemented to convert the tweets into vectors which are then trained using the beRT (bidirectional encoder Representations from Transformers) model to classify the tweets into two categories: tweets related to disasters and tweets unrelated to disasters. This can be helpful to know how many tweets are related to disasters and are truly informative. existing research focus on using LSTM, classification models (Random forest, Decision trees, Naive bayes) which do not give accurate results. The results obtained in the proposed solution got an accuracy of around 87% in both training and validation parts. Thus, the beRT model is better as compared to other models. © 2022, Springer Nature Switzerland AG.

关键词： embeddings

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A Framework for Video based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has b.e. designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e.text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the sub.e.uent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 Ieee.

关键词： Convolutional neural networks

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