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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Deshmukh, Gopal B. Kashid, Shraddha S. Gawali, Piyush P. Mahalle, Parikshit N. Sule, Bipin Ashokrao, Patil Rahul Ghorpade, Pradip P. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Sciences Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India Computer Engineering Department Sandip University Mahiravani Trimbak Road Maharashtra Nashik India

ISBN: (纸本)9789819766772

The study of sentiment analysis in natural language processing (NLP) holds tremendous promise for extracting meaningful emotional cues from textual data as a part of its core capabilities. This paper conducts an extensive exploration of sentiment analysis techniques powered by machine-learning classifiers, tailored to accommodate a variety of datasets. As a critical tool for assessing public opinion and sentiment, this study guides you through the crucial steps of data acquisition, preprocessing, feature extraction, and classifier selection, which are integral to the assessment of public sentiment. The research examines machine-learning approaches to sentiment analysis using a wide range of datasets, including social media content, product reviews, news articles, and much more, to determine if they are adaptable and scalable. In this study, "a number of classification models are examined, including Naive Bayes, Support Vector Machines, Random Forests, and deep learning models, and how their performance varies depending on the type of data". As a result of this analysis, the paper emphasizes the importance of utilizing machine-learning classifiers to distill nuanced sentiments, enabling applications in a wide range of fields, from brand reputation management to political sentiment tracking. With impressive accuracy, precision, F-measure, and correctly classified instances, Naive Bayes demonstrates rapid learning capabilities, while One-R displays promise with 91% accuracy, 97% precision, and 92% F-measure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Visual Pre-Training for Navigation: What Can We Learn from Noise?

Visual Pre-Training for Navigation: What Can We Learn from N...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yanwei Wang Ching-Yun Ko Pulkit Agrawal Department of Electrical Engineering and Computer Science MIT

One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://***/noise2ptz/

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Crowdfunding Fraud Prevention using Smart Contracts

Crowdfunding Fraud Prevention using Smart Contracts

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

作者： Kumar, Akshay Lamba, Jhanvi Rawal, Bharat S Sathiyanarayanan, mithileysh Alvarez, Nelson SRM Institute of Science and Technology Ramapuram Computer Science and Engineering Chennai India Benedict College Department of Cybersecurity Columbia United States MIT Square London United Kingdom University Silver Computer Science Department of Capitol Technology Spring United States

ISBN: (纸本)9781665462631

Crowdfunding is the method of generating money for a project or company by soliciting contributions from many individuals, typically online. For the people engaged in getting these monies, trust is crucial. Therefore, this research will aid in the analysis of how to integrate smart contracts and Blockchain technology into the prevalent crowdfunding process schemes. Because it makes it simpler for consumers to hold digital assets and data, blockchain is crucial. It is a worldwide distributed digital ledger that, in addition to using trusted and dependable technology, guarantees transparency for all network participants. Smart contracts have a number of benefits, including quicker transaction times, enhanced security, quicker money transfers, and better liquidity. The most significant benefit of the platform is the increased visibility of the project, which helps all stakeholders, including the creators, backers, and administrators of the platform. © 2023 IEEE.

关键词： Smart contract

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High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm

arXiv

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arXiv 2024年

作者： Srinivasan, Vishwak Wibisono, Andre Wilson, Ashia Department of Electrical Engineering and Computer Science MIT United States Department of Computer Science Yale University United States

In this work, we propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of Rd. Our proposed method is the result of applying a Metropolis-Hastings filter to the Markov chain formed by a single step of the preconditioned Langevin algorithm with a metric G, and is motivated by the natural gradient descent algorithm for optimisation. We derive non-asymptotic upper bounds for the mixing time of this method for sampling from target distributions whose potentials are bounded relative to G, and for exponential distributions restricted to the support. Our analysis suggests that if G satisfies stronger notions of self-concordance introduced in (Kook and Vempala, 2024), then these mixing time upper bounds have a strictly better dependence on the dimension than when is merely self-concordant. We also provide numerical experiments that demonstrates the practicality of our proposed method. Our method is a high-accuracy sampler due to the polylogarithmic dependence on the error tolerance in our mixing time upper bounds. © 2024, CC BY.

关键词： Markov chains

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Epileptic Seizure Classification Using Deep Learning Technique 2

Epileptic Seizure Classification Using Deep Learning Techniq...

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2nd International Conference on Signal and Information Processing, IConSIP 2022

作者： Dhoke, Shubham Metkar, Shilpa Patole, Rashmika Yedurkar, Dhanalekshmi P. College of Engineering Pune Department of Electronics and Telecommunications Pune411005 India MIT School of Engineering MIT Adt UniversityPune Department of Computer Science Engineering Maharashtra 411038 India

ISBN: (纸本)9781728168852

In human body, brain plays central and critical role. Any kind of problem to brain can cause great danger to whole nervous system and all together whole body. Epilepsy, disorder like parkinson's disease and Alzheimer's disease, is characterized by a rapid burst of neurochemical activity in the brain. This anomaly manifests as a seizure, and its detection is very important study issue. Electroencephalography(EEG) is one of the methods used for studying brain activity, neurological illnesses, and epileptic seizures in particular with use of Electroencephalography (EEG) Signal. Neurologists need to spend time visually inspecting epileptic abnormalities in EEG signals. This paper presented a deep learning approach for epileptic seizure classification. Data set from Bonn University is considered for the analysis. This spectral data from 128 channels is used to excite the deep learning algorithms such as CNN. We have considered Deep Learning(DL) algorithm VGG16 for the seizure classification. The results obtained shows that the CNN model performs better with Testing classification accuracy of 95% to 96%. © 2022 IEEE.

关键词： Electroencephalography

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A Novel Machine Learning Unmanned Swarm Intelligence Based Data Security in Defence System 2

A Novel Machine Learning Unmanned Swarm Intelligence Based D...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Chouhan, Nitesh Jakka, Geethamanikanta Navale, Geeta S. Verma, Garima Patil, Prerna Siddharth Dhotay, Megha Ashok Mlv Textile and Engineering College Department of Information Technology Rajasthan Bhilwara India University of the Cumberlands Department of Information Technology FremountCA United States Sinhgad Institute of Technologyand Science Department of Computer Engineering Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Dr. VishwanathKarad Mit World Peace University Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350315905

In addition to conducting research on applications in the field of civilian life, the article provides an overview of existing and anticipated possibilities for the development of artificial intelligence algorithms, particularly in military applications. The use of artificial intelligence calculations in advanced mechanics, object distinguishing proof, military coordinated factors, and cybersecurity got most of the consideration. It features the issues with the ongoing fixes and how man-made reasoning might help. Large media communications firms are at this point not only ready to offer 3G/4G administrations for remote organizations;all things considered, another time for "drones"has advanced to empower "Zero Touch Innovation,"especially in pandemic circumstances like those brought about by the Covid. Robots may now be utilized for an assortment of crisis circumstances, including food conveyance, clinical circulation, tower imaging, calamity help, and then some. These robots' data may be utilized as a hotspot for data examination. These a large number with swarm intelligence give customary frameworks - which utilize a few units to execute an errand - an upper hand. © 2023 IEEE.

关键词： Machine learning

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Identifying Cyber Extremism Sentiments using ROBERTA

Identifying Cyber Extremism Sentiments using ROBERTA

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

作者： Patel, Sudhanshu Raja, K. Duela, J. Shiny Chen, Thomas M Sathiyanarayanan, mithileysh SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Science and Technology City Department of Electrical and Electronic Engineering University of London United Kingdom Research & Innovation MIT Square University of London London United Kingdom

ISBN: (纸本)9781665462631

It is critical to identify and mitigate cyber extremist sentiments in order to prevent terrorist acts and maintain national security. The use of online platforms to promote extremist ideologies and incite violence is referred to as cyber extremism. To detect and classify extremist content on the internet, machine learning models have been used, making the process more efficient and scalable. To identify cyber extremism sentiments using machine learning models, the model is typically trained on a large dataset of extremist and non-extremist materials. The model then learns to recognize patterns and features associated with extremism, such as the use of specific words and phrases, as well as image and video-based features like facial expressions and gestures. We use ROBERTA for this purpose in this study and compare its results to those of BERT and ALBERT. The use of machine learning to detect cyber extremism sentiments can improve law enforcement and other organizations' ability to detect and respond to potential threats, ultimately helping to keep communities safe. © 2023 IEEE.

关键词： National security

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General and Scalable Optimization for Robust AI 37

General and Scalable Optimization for Robust AI

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Liu, Sijia Department of Computer Science & Engineering Michigan State University MI United States MIT-IBM Watson AI Lab IBM Research United States

ISBN: (纸本)9781577358800

Deep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as lack of optimization generality and lack of optimization scalability. My research highlights will offer a holistic understanding of optimization foundations for robust AI, peer into their emerging challenges, and present recent solutions developed by my research group. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Resource Allocation and Energy Efficient Dynamic Particle Swarm Optimisation in Heterogeneous Wireless Communication Networks 4

Resource Allocation and Energy Efficient Dynamic Particle Sw...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Pawar, Anuradha Bhalerao Kannan, G. Anuradha, T. Vignesh, V. Kesavan, R. Jose Anand, A. Mit Academy of Engineering Department of Electronics Engineering Alandi Maharastra Pune412105 India Amrita College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu 629 901 India Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu 600097 India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu 641032 India Simts Institute of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu 602105 India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu 600097 India

ISBN: (纸本)9798350354218

This paper investigates the difficult of beam policy in a multi-user peer-to-peer web linkage setting as part of our research. Using a network simulator, the dynamic particle swarm optimization is suggested for this. The challenge is to limit the maximum power consumption per relay, according to minimal Signal to Noise Ratio (SNR) guarantees, after a complete Channel State Information (CSI) is established. It has been established that the non-convex problem satisfies a potent duality. The twofold problem with the highest polynomial complexity was given as Semi Definite Programming (SDP). On the basis of the standards of the optimal dual variables, we looked at the best beam forming vectors for the initial problems in 3 different scenarios. This demonstrated whether the minimum Signal-to-Noise Ratio (SNR) prerequisite for each S-D pair has been met as well as whether a relay's electricity consumption varies among relays. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Advancing Alzheimer's Diagnosis: Performance Analysis of Deep Learning Ensembles in Medical Imaging

Advancing Alzheimer's Diagnosis: Performance Analysis of Dee...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Sonali Deshpande Nilima Kulkarni Computer Science & Engineering Department MIT SoC MIT Art Design and Technology University Pune India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Neuroimaging and deep learning have become the focus of much research for diagnosing Alzheimer's disease (AD) in recent years. Nevertheless, the limited availability of neuroimaging training data has resulted in significant overfitting issues for several deep learning models. However, the dataset was very imbalanced. To address this issue, we employed several augmentation techniques such as flipping, rotating, and zooming to balance the dataset. The ADNI dataset consisted of four classes: mild demented, moderate demented, very mild demented, and non-demented. In this work, we have proposed an ensemble of EfficientNetB2, DenseNet121, VGG16, and Xception for the diagnosis of Alzheimer at early stage. Experiment results shows that EfficientNetB2 ensemble with Xception and VGG16 performed extremely well with 99.1% and 99.4% respectively.

关键词： Deep learning Neuroimaging Industries Accuracy Training data Medical services Performance analysis Ensemble learning Alzheimer's disease Overfitting

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