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检索条件"机构=B.E. Computer 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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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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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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***: A pilot study for a personality-based friend recommendation system 7

***: A pilot study for a personality-based friend recommenda...

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7th Ieee International conference for Convergence in Technology, I2CT 2022

作者： Mody, Vidhi Mody, Vrushti Parekh, Soham Mistry, Kamal Narsee Monjee Institute of Management Studies Computer Engineering India D.J. Sanghvi College of Engineering B.E. Computer Engineering India Narsee Monjee Institute of Management Studies India

ISBN: (纸本)9781665421683

Social networks have become very popular in today's age. As a result, Friend Recommendation Systems (FRSs) have gained immense importance. Teenagers today are often depressed and feel lonely. Social Networks, if used wisely can help a user connect to the right set of friends which can help to solve the above-mentioned challenges. Most popular social media platforms rely on homophily-based FRSs for their friend recommendations. That is, they use the user's social graph to match him/her with other users. Users now are concerned about how their personal data is being used and its privacy. Therefore, most users do not reveal their personal information on social networks. Also, there is no evidence to the accuracy of user information. Therefore, the existing recommendation systems in social media prove to be inaccurate in a lot of cases. Furthermore, the main purpose of these traditional recommendation systems is to connect pre-existing friends online rather than suggesting users that could be potential friends. To connect users to a new set of people, there is a need to design a friend recommendation system that matches users based on something other than existing connections. In this paper, we propose a system which uses personality analysis along with hybrid filtering to make personalized friend recommendations along with other filtering options for age range, location, gender, etc. This system would solve the cold start prob.e. which occurs because in the present scenario people don't like spending time to specify their preferences for friend recommendations. The filtering options are of further interest to the user as there is a possibility of connecting to a new bunch of friends in person too. © 2022 Ieee.

关键词： Recommender systems

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Smart Remote Physical Therapy: Deep Learning-based Patient Monitoring

Smart Remote Physical Therapy: Deep Learning-Based Patient M...

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Green Industrial electronics and Sustainab.e.Technologies (GIeST), Ieee International Conference on

作者： Thirumangai S Thilagan P Anitha N B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Mechatronics Engineering Kumaraguru College of Technology Coimbatore India Mathematics School of Foundational Sciences Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331540142

ISBN: (纸本)9798331540159

This study is centred on the development of a methodology for remote health surveillance of patients undergoing physical therapy, employing pose detection technology. The approach utilizes advanced deep learning algorithms to assess patients’ body positions during physical exercises, offering instantaneous feedback to both patients and therapists. This method of remote monitoring coupled with personalized feedback aims to enhance patient outcomes, decrease healthcare expenditures, and bolster patient engagement. by integrating pose detection technology, the system ensures precise tracking of patients’ advancements, facilitating tailored therapy interventions and optimizing treatment effectiveness.

关键词： Deep learning electric potential Accuracy Telemedicine Surveillance Medical treatment Wearab.e.devices Wearab.e.sensors Research and development Remote monitoring

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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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Real-time Sign Language Recognition using Machine Learning and Neural Network

Real-time Sign Language Recognition using Machine Learning a...

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2022 International Conference on electronics and Renewab.e.Systems, ICeARS 2022

作者： Matlani, Roshnee Dadlani, Roshan Dumb.e. Sharv Mishra, Shruti Tewari, Mrs. Abha B.E. Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India Vivekanand Education Society's Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665484251

Languages based on gestures have recently garnered a lot of interest. In sign language, they are one of the nonverbal communication modalities. It is most commonly used by deaf and dumb individuals with vocal or hearing issues in order to convey and talk with one other and normal people without disabilities. The motion, form, and location of hands, along with the body gestures and facial expressions, are used for conveying the meaning in American Sign Language(ASL). ASL translator also aids in the communication between signers and non-signers. The most efficient way in which they can convey and communicate is through using gestures and that is particularly known as Sign language. Hearing-impaired persons find it very complex and a prob.e. to communicate with the normal people that are in the rest of the world, and as a result, they are isolated, that is the humans with speech impediments communicate via gestures, which makes it difficult for them to mix with the ab.e.bodied. Hence, an interpretive system is required to serve as a link between them and individuals who do not understand their sign language. To assist them overcome these obstacles, we provide a project that will be beneficial to them. This project proposes a Machine Learning-based method for recognising hand gestures and translating them into speech/text, as well as the reverse, converting speech/text to *** introduces a conceptual framework that can interpret sign language autonomously, allowing deaf and dumb individuals to convey more readily with one another and with regular people. Recognition systems and motion tracking are two important and crucial areas of study and are still in their infancy. © 2022 Ieee.

关键词： Audition

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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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