Greece

The presentation talk about Transfer learning using Convolutional Neural Network Architectures for Brain tumors classification. First, I&#39;ll talk about why we are interested in automatic brain tumors classification Second, I’ll describe the nine pre-trained networks architectures and transfer learning setting .Then, I’ll show the dataset used, training parameters and how we calculate the classification accuracy. Next , I ‘ll also show our results which outperforms the results of the related works. finally, I’ll give you a brief conclusion and future works.

AIAI 2020

Transfer Learning using Convolutional Neural Network Architectures for Brain Tumor Classification from MRI Images

transfer learning.

magnetic resonance images

brain tumor

convolutional neural network

deep learning

classification

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

**Watch this video to hear about the AFCEA purpose.**

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NAML 2021

The presentation talk about Transfer learning using Convolutional Neural Network Architectures for Brain tumors classification. First, I'll talk about why we are interested in automatic brain tumors classification Second, I’ll describe the nine pre-trained networks architectures and transfer learning setting .Then, I’ll show the dataset used, training parameters and how we calculate the classification accuracy. Next , I ‘ll also show our results which outperforms the results of the related works. finally, I’ll give you a brief conclusion and future works.

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

The study of Self-organizing lists deals with the problem of lowering the average-case asymptotic cost of a list data structure receiving query accesses in Non-stationary Environments (NSEs) with the so-called “locality of reference” property. The de facto schemes for Adaptive lists in such Environments are the Move To Front (MTF) and Transposition (TR) rules. However, significant drawbacks exist in the asymptotic accuracy and speed of list re-organization for the MTF and TR rules. This paper improves on these schemes using the design of an Adaptive list data structure as a hierarchical data “sub”-structure. In this framework, we employ a hierarchical Singly-Linked-Lists on Singly- Linked-Lists (SLLs-on-SLLs) design, which divides the list data structure into an outer and inner list context. The inner-list context is itself a SLLs containing sub-elements of the list, while the outer-list context contains these sublist partitions as its primitive elements. The elements belong- ing to a particular sublist partition are determined using reinforcement learning schemes from the theory of Learning Automata. In this pa- per, we show that the Transitivity Pursuit-Enhanced Object Migration Automata (TPEOMA) can be used in conjunction with the hierarchical SLLs-on-SLLs as the dependence capturing mechanism to learn the probabilistic distribution of the elements in the Environment. The idea of Transitivity builds on the Pursuit concept that injects a noise filter into the EOMA to filter divergent queries from the Environment, thereby increasing the likelihood of training the Automaton to approximate the “true” distribution of the Environment. By taking advantage of the Transitivity phenomenon based on the statistical distribution of the queried elements, we can infer “dependent” query pairs from non-accessed elements in the transitivity relation. The TPEOMA-enhanced hierarchical SLLs-on-SLLs schemes results in superior performances to the MTF and TR schemes as well as to the EOMA-enhanced hierarchical SLLs- on-SLLs schemes in NSEs. However, the results are observed to have superior performances to the PEOMA-enhanced hierarchical schemes in Environments with a Periodic non-stationary distribution but were inferior in Markovian Switching Environments.

Optimizing Self-Organizing Lists-on-Lists using Transitivity and Pursuit-Enhanced Object Partitioning

Predicting the activity of chemical compounds against
cancer is a crucial task. Active chemical compounds against cancer
help pharmaceutical drugs producers in the conception of anticancer
medicines. Still the innate way of representing chemical compounds is
by graphs, the machine learning algorithms can not handle directly the
anticancer activity prediction problems. In this presentation, we investigate the representation power of Node2vec for embedding learning over
graphs, by comparing it to the theoretical framework Graph Isomorphism Network (GIN). We prove that GIN is a deep generalization of
Node2vec. We then exert the two models Node2vec and GIN to extract
regular representations from chemical compounds and make predictions
about their activity against lung and ovarian cancer

Graph Neural Networks to Advance Anticancer Drug Design

Brain-inspired Hyperdimensional (HD) computing is an emerging technique for cognitive tasks in the field of low-power design. As an energy-efficient and fast learning computational paradigm, HD computing has shown great success in many real-world applications. However, an HD model incrementally trained on multiple tasks suffers from the negative impacts of catastrophic forgetting. The model forgets the knowledge learned from previous tasks and only focuses on the current one. To the best of our knowledge, no study has been conducted to investigate the feasibility of applying multi-task learning to HD computing. In this paper, we propose Task-Projected Hyperdimensional Computing (TP-HDC) to make the HD model simultaneously support multiple tasks by exploiting the redundant dimensionality in the hyperspace. To mitigate the interferences between different tasks, we project each task into a separate subspace for learning. Compared with the baseline method, our approach efficiently utilizes the unused capacity in the hyperspace and shows a 12.8% improvement in averaged accuracy with negligible memory overhead.

Task-Projected Hyperdimensional Computing for Multi-Task Learning

Twitter is generating a large amount of real-time data in the form of microblogs that has potential knowledge for various applications like traffic incident analysis and urban planning. Social media data represents the unbiased actual insights of citizens concerns that
may be mined in making cities smarter. In this study, a computational framework has been proposed using word embedding and machine learning model to detect traffic incidents using social media data. The study includes the feasibility of using machine learning algorithms with different feature extraction and representation models for the identification of traffic incidents from the Twitter interactions. The comprehensive proposed approach is the combination of following four steps. In the first phase, a dictionary of traffic-related keywords is formed. Secondly, realtime Twitter data has been collected using the dictionary of identified traffic related keywords. In the third step, collected tweets have been pre-processed, and the feature generation model is applied to convert
the dataset eligible for a machine learning classifier. Further, a machine
learning model is trained and tested to identify the tweets containing traffic incidents. The results of the study show that machine learning
models built on top of right feature extraction strategy is very promising to identify the tweets containing traffic incidents from micro-blogs.

Harnessing Social Interactions on Twitter for Smart Transportation Using Machine Learning

We present acoustic resonance testing on glass intravenous (IV) bottles. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. 
Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods. 
The results from the binary classification experiments are presented and compared in terms of Balanced Accuracy Rate, F1-score, Area Under the Receiver Operating Characteristic Curve, and Matthews Correlation Coefficient metrics.
The presented feature analysis and the employed classifiers achieved solid results, despite the rather small and imbalanced dataset with a highly inconsistent class population.

Acoustic Resonance Testing of Glass IV Bottles

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Au- tomated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interest- ing predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.

Chemical Laboratories 4.0: A Two-stage Machine Learning System for Predicting the Arrival of Samples

Surfing is currently one of the most popular water sports in the world, both for recreational and competitive level surfers. Surf
session analysis is often performed with commercially available devices.
However, most of them seem insufcient considering the surfers' needs,
by displaying a low number of features, being inaccurate, invasive or not adequate for all surfer levels. Despite the fact that performing manoeuvres is the ultimate goal of surng, there are no available solutions that enable the identication and characterization of such events. In this work, we propose a novel method to detect manoeuvre events during wave riding periods resorting solely to the inertial sensors embedded in smartphones. The proposed method was able to correctly identify over
95% of all the manoeuvres in the dataset (172 annotated manoeuvres), while achieving a precision of up to 80%, using a session-independent validation approach. These ndings demonstrate the suitability and validity
of the proposed solution for identication of manoeuvre events in real-world conditions, evidencing a high market potential.

Real-time Surf Manoeuvres' Detection Using Smartphones' Inertial Sensors

Internet of Things (IoT) devices are inherently vulnerable due to insecure design, implementation, and configuration. Aggressive behavior change, due to increased attacker's sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. To detect intensive attacks and increase device uptime, we propose a novel ensemble learning model for IoT anomaly detection using software-dened networks (SDN). We use a deep auto-encoder to
extract handy features for stacking into an ensemble learning model. The learned model is deployed in the SDN controller to detect anomalies or dynamic attacks in IoT by addressing the class imbalance problem. We validate the model with real-time testbed and benchmark datasets. The initial results show that our model has a better and more reliable performance than the competing models showcased in the relevant related work.

SDN-enabled IoT Anomaly Detection using Ensemble Learning

This paper presents a novel Machine Learning (ML) approach to support the creation of woven fabrics. Using data from a textile company, two CRoss-Industry Standard Process for Data Mining (CRISP-DM) iterations were executed, aiming to compare three input feature representation strategies related with fabric design and finishing processes. During the modeling stage of CRISP-DM, an Automated ML (AutoML) procedure was used to select the best regression model among six distinct state-of-the-art ML algorithms. A total of nine textile physical properties were modeled (e.g., abrasion, elasticity, pilling). Overall, the simpler yarn representation strategy obtained better predictive results. Moreover, for eight fabric properties (e.g., elasticity, pilling), the addition of finishing features improved the quality of the predictions. The best ML models obtained low predictive errors (from 2% to 7%) and are potentially valuable for the textile company, since they can be used to reduce the number of production attempts (saving time and costs).

Predicting Physical Properties of Woven Fabrics via Automated Machine Learning and Textile Design and Finishing Features

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.

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