iRestroom : A smart restroom cyberinfrastructure for elderly people
iRestroom: زیرساخت سایبری سرویس بهداشتی هوشمند برای افراد مسن-2022
According to a report by UN and WHO, by 2030 the number of senior people (age over 65) is projected to grow up to 1.4 billion, and which is nearly 16.5% of the global population. Seniors who live alone must have their health state closely monitored to avoid unexpected events (such as a fall). This study explains the underlying principles, methodology, and research that went into developing the concept, as well as the need for and scopes of a restroom cyberinfrastructure system, that we call as iRestroom to assess the frailty of elderly people for them to live a comfortable, independent, and secure life at home. The proposed restroom idea is based on the required situations, which are determined by user study, socio-cultural and technological trends, and user requirements. The iRestroom is designed as a multi-sensory place with interconnected devices where carriers of older persons can access interactive material and services throughout their everyday activities. The prototype is then tested at Texas A&M University-Kingsville. A Nave Bayes classifier is utilized to anticipate the locations of the sensors, which serves to provide a constantly updated reference for the data originating from numerous sensors and devices installed in different locations throughout the restroom. A small sample of pilot data was obtained, as well as pertinent web data. The Institutional Review Board (IRB) has approved all the methods.
keywords: اینترنت اشیا | حسگرها | نگهداری از سالمندان | سیستم های هوشمند | یادگیری ماشین | IoT | Sensors | Elder Care | Smart Systems | Machine Learning
Sexual-predator Detection System based on Social Behavior Biometric (SSB) Features
سیستم تشخیص جنسی-شکارچی بر اساس ویژگی های بیومتریک رفتار اجتماعی (SSB)-2021
This study designs an online sexual predator detection system using Social Behavior Biometric (SSB) features. Social biometric focuses on extracting the pattern a user exhibits while interacting and communicating through social networks. The paper addresses the online sexual predator problem by mining the vocabulary and emotional behavior, which could assist in identifying if the user is a benign or predator. The feature-set consists of vocabulary terms that appear differently in predator and victim content. In order to strengthen the detection model, the paper also focuses on distinguishing the two classes of users based on emotions reflected in their conversation. The experiments are performed on the PAN 2012 corpus. Two datasets are created with respect to vocabulary-based and emotion-based features. The results obtained on the test set have proved that by integrating the vocabulary and emotion-based attributes, the performance of the system is significantly enhanced. While comparing, the proposed approach has outperformed top existing methods by obtaining F1, F2, and F0.5values of 0.95, 0.94, and 0.96 respectively. Furthermore, we also recorded the best accuracy compared to state-of-the-art studies for our proposed SBB-based approach with 99.86%, 99.51%, and 99.88% for Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) respectively.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)Peer-review under responsibility of the scientific committee of the 5th International Conference on AI in Computational Linguistics.
Keywords: Online Sexual Predators | Emotion mining | Lexical analysis Machine Learning
Open code biometric tap pad for smartphones
باز کردن کد ضربه گیر بیومتریک برای تلفن های هوشمند-2021
Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of ‘‘open code’’ biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as(1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the ‘‘usual’’ finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
Keywords: Smartphone security | Behavioral biometrics | Touchscreen behavior | Open code | Biometric tap pad
A survey: Intelligent system for imposter detection
یک مرور: سیستم هوشمند برای تشخیص جعل کننده-2021
This study aims the impostor is a very cunning person who reaches an obsessive stage to perfection in impersonating someone in actual life, concentrates on his biometric. He analyzes the controls, restrictions, and obstacles that he will face to overcome them. The technologies biometric recognition performs a greatly important role in impostor detection. Biometrics properties refer to the automatic recognition of persons depending on their behavioral and physiological characteristics. Biometrics comprises face recognition, fingerprint, voice recognition, retinal scanning, and so on. Biometrics may increment the reliability of an ID card system. In this paper, a review of the concepts mentioned above will be provided. At first, a presentation about a procedural overview of biometric recognition technologies, ID card systems. Then dissection will be presented for the review of the most recent techniques. A description of each concept will be given and a comparison study is achieved with formal discussion and analysis for each approach result introduces in this study. Finally, a summary of the research results is given.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Face recognition | Voice recognition | Finger print | Biometric systems | ID card | Person identification | Impostor detection | Machine learning | Deep neural networks
Finger veins recognition using machine learning techniques
تشخیص رگ های انگشت با استفاده از تکنیک های یادگیری ماشین-2021
This study aims The Finger veins are a unique feature of the human, this differs from other biometric signs. That impossibility for an imposter to discovered and penetrate the system and difficult to know because the veins are under the skin, and is distinguished from the rest by its flexibility because the per- son has more than one finger to take. In this paper, we will present impostor detection using seven machine-learning techniques, preceded description of preprocessing, and features extraction. These steps implemented on two datasets. The best performance of the classifiers was naive bias followed by random forest. While the lower classifier accuracy was JRip.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.
Keywords: Machine learning | Veins | Imposter | Naïve Bayes | k-Nearest Neighbor | Random forest
Feature based classification of voice based biometric data through Machine learning algorithm
طبقه بندی مبتنی بر ویژگی داده های بیومتریک مبتنی بر صدا از طریق الگوریتم یادگیری ماشین-2021
In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected bio- metric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us ben- efits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Con- ference on Computations in Materials and Applied Engineering – 2021.
Keywords: Prediction | Biometric data | Voice samples | Male | Female | Cost complexity pruning (CCP) | Dimension reduction
Generative Deep Learning in Digital Pathology Workflows
یادگیری عمیق مولد در گردش کار آسیب شناسی دیجیتال-2021
Many modern histopathology laboratories are in the process of digitizing their workflows. Digitization of tissue images has made it feasible to research the augmentation or automation of clinical reporting and diagnosis. The application of modern computer vision techniques, based on deep learning, promises systems that can identify pathologies in slide images with a high degree of accuracy. Generative modeling is an approach to machine learning and deep learning that can be used to transform and generate data. It can be applied to a broad range of tasks within digital pathology, including the removal of color and intensity artifacts, the adaption of images in one domain into those of another, and the generation of synthetic digital tissue samples. This review provides an introduction to the topic, considers these applications, and discusses future directions for generative models within histopathology.
Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021
While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work.
Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron
Machine learning: Best way to sustain the supply chain in the era of industry 4:0
یادگیری ماشین: بهترین راه برای حفظ زنجیره تأمین در عصر صنعت 4:0-2021
With the rapidly growing importance in the industries on the adaptation of advanced technologies, the involvement of IT-enabled systems has increased in developing the pathway for the future industry. The learning’s from these technologies becomes paramount for the present industries which gives a sense of belongingness and significance of the industry towards the market. The digital revolution world-wide affected the physical happenings of the events in the manufacturing industries such as the procurement, manufacturing/assembling & distribution of goods. This digital reformation is known as Industry 4.0 which generally means the advancement in the existing business models where all the business operations are interconnected with each other by digital mode (virtual representation based on operations). In this kind of environment, it is being necessary to map all the operations digitally in such a manner so that the physical flows of resources/goods will not suffer at any stage. Machine learning in the present scenario is one of the thrust areas for the researchers and the practitioners. The output in the machine learning process is having many dependencies on the input data such as the functions and characteristics imparted to the machine at the earlier stage. The present paper aptly reflects the thoughts and reflections of present-day industries and the opportunities to express feelings, thoughts, and contribute towards the future industries.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International Conference on Computational and Experimental Methods in Mechanical Engineering.
Keywords: Machine Learning (ML) | Supply Chain (SC) | Industry 4.0 | Resources utilization | Digital transformation
Towards a pragmatic detection of unreliable accounts on social networks
به سوی تشخیص عملی حسابهای غیر قابل اعتماد در شبکه های اجتماعی-2021
In recent years, the problem of unreliable content in social networks has become a major threat, with a proven real-world impact in events like elections and pandemics, undermining democracy and trust in science, respectively. Research in this domain has focused not only on the content but also on the accounts that propagate it, with the bot detection task having been thoroughly studied. However, not all bot accounts work as unreliable content spreaders (p.e. bot for news aggregation), and not all human accounts are necessarily reliable. In this study, we try to distinguish unreliable from reliable accounts, independently of how they are operated. In addition, we work towards providing a methodology capable of coping with real-world situations by introducing the content available (restricting it by volume- and time-based batches) as a parameter of the methodology. Experiments conducted on a validation set with a different number of tweets per account provide evidence that our proposed solution produces an increase of up to 20% in performance when compared with traditional (individual) models and with cross-batch models (which perform better with different batches of tweets).
Keywords: Unreliable accounts detection | Social networks | Machine learning | Data mining | Volume and time adaptive methodology