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General General

Deep feature selection using local search embedded social ski-driver optimization algorithm for breast cancer detection in mammograms.

In Neural computing & applications

Breast cancer has become a common malignancy in women. However, early detection and identification of this disease can save many lives. As computer-aided detection helps radiologists in detecting abnormalities efficiently, researchers across the world are striving to develop reliable models to deal with. One of the common approaches to identifying breast cancer is through breast mammograms. However, the identification of malignant breasts from mass lesions is a challenging research problem. In the current work, we propose a method for the classification of breast mass using mammograms which consists of two main stages. At first, we extract deep features from the input mammograms using the well-known VGG16 model while incorporating an attention mechanism into this model. Next, we apply a meta-heuristic called Social Ski-Driver (SSD) algorithm embedded with Adaptive Beta Hill Climbing based local search to obtain an optimal features subset. The optimal features subset is fed to the K-nearest neighbors (KNN) classifier for the classification. The proposed model is demonstrated to be very useful for identifying and differentiating malignant and healthy breasts successfully. For experimentation, we evaluate our model on the digital database for screening mammography (DDSM) database and achieve 96.07% accuracy using only 25% of features extracted by the attention-aided VGG16 model. The Python code of our research work is publicly available at: https://github.com/Ppayel/BreastLocalSearchSSD.

Pramanik Payel, Mukhopadhyay Souradeep, Mirjalili Seyedali, Sarkar Ram

2022-Nov-05

Algorithm, Breast cancer, Deep learning, Local search, Mammogram images, Optimization, Social ski-driver

General General

Air Pollution Hotspot Detection and Source Feature Analysis using Cross-domain Urban Data

ACM SIGSPATIAL 2021

Air pollution is a major global environmental health threat, in particular for people who live or work near pollution sources. Areas adjacent to pollution sources often have high ambient pollution concentrations, and those areas are commonly referred to as air pollution hotspots. Detecting and characterizing pollution hotspots are of great importance for air quality management, but are challenging due to the high spatial and temporal variability of air pollutants. In this work, we explore the use of mobile sensing data (i.e., air quality sensors installed on vehicles) to detect pollution hotspots. One major challenge with mobile sensing data is uneven sampling, i.e., data collection can vary by both space and time. To address this challenge, we propose a two-step approach to detect hotspots from mobile sensing data, which includes local spike detection and sample-weighted clustering. Essentially, this approach tackles the uneven sampling issue by weighting samples based on their spatial frequency and temporal hit rate, so as to identify robust and persistent hotspots. To contextualize the hotspots and discover potential pollution source characteristics, we explore a variety of cross-domain urban data and extract features from them. As a soft-validation of the extracted features, we build hotspot inference models for cities with and without mobile sensing data. Evaluation results using real-world mobile sensing air quality data as well as cross-domain urban data demonstrate the effectiveness of our approach in detecting and inferring pollution hotspots. Furthermore, the empirical analysis of hotspots and source features yields useful insights regarding neighborhood pollution sources.

Yawen Zhang, Michael Hannigan, Qin Lv

2022-11-15

General General

Intrusion Detection System Based on Pattern Recognition.

In Arabian journal for science and engineering

Artificial intelligence has been developed to be able to solve difficult problems that involve huge amounts of data and that require rapid decision-making in most branches of science and business. Machine learning is one of the most prominent areas of artificial intelligence, which has been used heavily in the last two decades in the field of network security, especially in Intrusion Detection Systems (IDS). Pattern recognition is a machine learning method applied in medical applications, image processing, and video processing. In this article, two layers' IDS is proposed. The first layer classifies the network connection according to the used service. Then, a minimum number of features that optimize the detection accuracy of malicious activities on that service are identified. Using those features, the second layer classifies each network connection as an attack or normal activity based on the pattern recognition method. In the training phase, two multivariate normal statistical models are created: the normal behavior model and the attack behavior model. In the testing and running phases, a maximum likelihood estimation function is used to classify a network connection into attack or normal activity using the two multivariate normal statistical models. The experimental results prove that the proposed IDS has superiority over related IDSs for network intrusion detection. Using only four features, it successfully achieves DR of 97.5%, 0.001 FAR, MCC 95.7%, and 99.8% overall accuracy.

Abdeldayem Mohamed M

2022-Nov-07

Intrusion detection system (IDS), Machine learning techniques, Network security, Pattern recognition

General General

CardiacGen: A Hierarchical Deep Generative Model for Cardiac Signals

ArXiv Preprint

We present CardiacGen, a Deep Learning framework for generating synthetic but physiologically plausible cardiac signals like ECG. Based on the physiology of cardiovascular system function, we propose a modular hierarchical generative model and impose explicit regularizing constraints for training each module using multi-objective loss functions. The model comprises 2 modules, an HRV module focused on producing realistic Heart-Rate-Variability characteristics and a Morphology module focused on generating realistic signal morphologies for different modalities. We empirically show that in addition to having realistic physiological features, the synthetic data from CardiacGen can be used for data augmentation to improve the performance of Deep Learning based classifiers. CardiacGen code is available at https://github.com/SENSE-Lab-OSU/cardiac_gen_model.

Tushar Agarwal, Emre Ertin

2022-11-15

oncology Oncology

Feasibility of Establishing an Artificial Intelligence Based Head and Neck Cancer Registry: Experience from a Tertiary Care Hospital.

In Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India

** : Introduction Establishing and maintaining a cancer registry in a tertiary high volume centre is fraught with difficulty, inaccuracy and missed data entry. Further, the raw unstructured data must be converted into a structured digital data, so that scientists can identify trends in cancer diagnoses and treatment responses. Objective We test the feasibility of establishing a cancer registry of Head and Neck malignancy patients through a research oriented artificial intelligence (AI) enabled data collection platform, using its smartphone application version. Materials and Methods This prospective observational study was conducted in the Department of Otolaryngology & Head and Neck Surgery, Post Graduate Institute of Medical Education And Research, Chandigarh in collaboration with Departments of Radiotherapy and Community Medicine. After taking due clearance from the Institute ethical committee, HNC patients, who were biopsy proven, were enrolled from October 2019 up to March 2021. The obtained data was entered, followed up and analysed through Jiyyo Research application which is a commercially available dedicated research oriented AI enabled data collection platform. Results The Jiyyo Research site was browsed and after proper registration, the patient data was entered into a proforma/questionnaire. The entered patient details were browsed for review, follow up and addition of new information. The whole process of data capture for each patient, took approximately 5-8 min, while any updates or review for the same patient required less than a minute. Search and data retrieval was very quick, and can be done in 1-2 min. Through this platform, a total of 1214 HNC patients were collected, followed and analysed during the study period. Conclusion It was feasible to establish a Head and Neck Cancer Registry using an AI based smartphone app. This AI based tumor registry could benefit in further studies with longer follow up of 5 and 10 years and in future AI studies.

Supplementary Information : The online version contains supplementary material available at 10.1007/s12070-022-03173-3.

Gautamjit R K, Gupta Rijuneeta, Singh Amarjeet, Panda Naresh Kumar, Ghoshal Sushmita, Bakshi Jaimanti B, Verma Roshan Kumar

2022-Nov-05

App. based cancer registry, Artificial intelligence, Head and neck cancer registry, Indian cancer registry, Oral cancer

General General

Identification of medical devices using machine learning on distribution feeder data for informing power outage response

ArXiv Preprint

Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study proposes a load disaggregation model to predict the number of medical devices behind an electric distribution feeder. This data can be used to inform planning and response. The proposed solution serves as a measure for climate change adaptation.

Paraskevi Kourtza, Maitreyee Marathe, Anuj Shetty, Diego Kiedanski

2022-11-15