Unattended Baggage

The magnetometric section of the marine areas geophysics is characterized by the transition to a qualitatively new level of research, pipeline tracing, search for sunken ships, aerial bombs and unexploded ordnance, study of sunken objects of cultural heritage, identification of such sunken objects (inverse magnetometry problem), etc. The identification of such objects is called the inverse problem of marine magnetometry, and it belongs to the class of incorrectly posed task. The first step in solving this task is to find solution of the direct problem. A direct problem is to determine the anomalous field created by objects of a certain shape, density under certain conditions of immersion and accumulation of information about these objects. Problems of determining the anomalous field of a thin-walled objects are of particular interest. The solution to this problem is largely determined by the existence of an effective system of programs for solving three-dimensional direct magnetometry problems – finding the fields of typical objects (by using sets of such objects you can approximate real objects). The article describes the operation of the main algorithm and subroutines that are part of the main algorithm of the developed MBEM software for modeling the magnetic fields of thin-walled structures. MBEM is based on the developed modified boundary element method, based on the classical boundary element method and the secondary source method. Numerical verification has been carried out and the efficiency of the developed algorithms and software components has been established on the problems of calculating the magnetic field of thin-walled structures for which solutions are known. The example of the software complex work is given – the calculation of the resulting field of a thin-walled pipe, namely the simulation of the tension vector in the plane that is under the object at a distance of one half meter.

Objective: To assess airborne nicotine concentrations as an indicator of second-hand smoke (SHS) exposure in public places in both urban and rural areas of China. Design: Measurement of vapour-phase nicotine...

The aim of this study is modeling ferromagnetic targets for localization and identification of such objects by a wireless sensor network (WSN). MICAz motes were used for setting up a wireless sensor network utilizing a centralized tree -based system. The detection and tracking of ferromagnetic objects is an important application of WSNs. This research focuses on analyzing the sensing limitations of magnetic sensors via tests conducted on small -scale targets which are moving within a 30 cm radius around the sensors. To detect target presence and determine direction of motion, changes in magnetic field intensity are measured by the magnetic sensors. Target detection, identification and sequential localization (DIS L) were accomplished using a minimum distance algorithm. The effect of environmental variations, such as temperature and power supply variations and magnetic noise, on DIS L performance is examined based on experimental tests.

(p,q)-strings are bound states of p F-strings and q D-strings and are\npredicted to form at the end of brane inflation. As such these cosmic\nsuperstrings should be detectable in the universe. In this paper we argue that\nthey can be detected by the way that massive and massless test particles move\nin the space-time of these cosmic superstrings, in particular we study\nsolutions to the geodesic equation in the space-time of field theoretical\n(p,q)-strings. The geodesics can be classified according to the test particle's\nenergy, angular momentum and momentum in the direction of the string axis. We\ndiscuss how the change of the magnetic fluxes, the ratio between the symmetry\nbreaking scale and the Planck mass, the Higgs to gauge boson mass ratios and\nthe binding between the F- and D-strings, respectively, influence the motion of\nthe test particles. While massless test particles can only move on escape\norbits, a new feature as compared to the infinitely thin string limit is the\nexistence of bound orbits for massive test particles. In particular, we observe\nthat - in contrast to the space-time of a single Abelian-Higgs string - bound\norbits for massive test particles in (p,q)-string space-times are possible if\nthe Higgs boson mass is larger than the gauge boson mass. We also compute the\neffect of the binding between the p- and the q-string on observables such as\nthe light deflection and the perihelion shift. While light deflection can also\nbe caused by other matter distributions, the possibility of a negative\nperihelion shift seems to be a feature of finite width cosmic strings that\ncould lead to the unmistakable identification of such objects. In Melvin\nspace-times, which are asymptotically non-conical, massive test particles have\nto move on bound orbits, while massless test particles can only escape to\ninfinity if their angular momentum vanishes.\n

BackgroundMachine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.MethodsIn our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme.ResultsML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73–0.93), boosted classification trees (AUC 0.80; 95% CI 0.68–0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67–0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75–0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78–0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance.ConclusionsUsing ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.

Background: The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning [ML] models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol- Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning [SML] models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article. Methods: The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability. Results: The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters. method: In this research work the researcher considered 9 different ML models and used them as supervised as well as semi supervised model to determine the pollution level of the certain area. In this research work the researcher also selected the most relevant features and discarded the less relevant features. In case of SML algorithm, the accuracy level of the different ML algorithm has been determined and depicted in the result analysis section. The most effective ML model has been chosen for the proposed embedded system so that accuracy could be achieved at most. In case of semi supervised algorithm the feature selection is done as per the supervised algorithm. In this case the training is done same as the SML algorithm, but the testing phase is done like unsupervised machine learning algorithm where the decision parameter is predicted and ultimately matched with the previously achieved data of SML algorithm. The reliability of this approach is much more effective than simple SML algorithm. Conclusion: This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.

In every enterprise, the customer is the focal point of the business, and it is essential to comprehend how customer satisfaction with the enterprise's product or service is evaluated. To understand how the customer is satisfied with the enterprise's business the process of carrying out customer analysis is inevitable. Moreover, due to furtherance of the Internet, CRM and technology, generation of customer data is enormous. Using these customer data efficient understanding of the customer patterns can be carried. In this analysis, ML methods are implemented to perform customer analysis. However, due to existence of irrelevant, inappropriate, & correlated data, the ML algorithm cannot help to get quality performance results for the customer analysis, and also computational time is increased with these datasets. To address these issues, the feature selection (FS) mechanism is applied. Goal of FS is to improve accuracy of the classifier and also reducing the amount of computation that the algorithm requires. This is accomplished by removing correlated, inappropriate, and irrelevant variables and focusing on selecting relevant attributes. In this study, four distinct filter FS methods are implemented: Symmetrical Uncertainty, Information Gain, Chi-square, and ReliefF. Further feature subsets captured from the FS approaches are individually applied with an ML classifier to build the model. Here, two distinct supervised ML approaches are utilized: Nave Bayes (NB) and the other one is (K-NN). The empirical methodology is conducted using a bank data collected from UCI and experiment is done in two distinct perspectives: one using full features with ML and the other one using selected features obtained from FS approaches. Empirical results observed are presented & evaluated using diverse validity scores. The comparison of the two sets of results demonstrates quite clearly that ML with feature selection performs significantly better than ML without feature selection.

Background: Exposure to environmental tobacco smoke (ETS) has important public health implications. The results of the first European multi-centre study that measured ETS exposure in a range of public places...

Discovery of Land Usage, also known as Land Usage Mining, essentially deals with determining the proper usage of a plot. For landform mapping to analyze locations, things, and features, aerial imagery is crucial. Remote sensing techniques are a fundamental and significant resource for detecting this. The aerial images may get captured with Modern means like satellites or drones. Additionally, the remote sensing technique gathers crucial data that may be applied to various planning tasks, including urban planning, conservation, forestry, land use, and many more. Urban regions may extract valuable information about land use and land cover from Very High Resolution (VHR) satellite pictures. Before practical usage is feasible, further research needs to be done on the many approaches that are now accessible. A machine learning technique called the ensemble technique combines many base models to create a single, ideal prediction model. In this paper, firstly, the extraction of features using TSBTC and then triangle thresholding is applied to the land usage dataset. After extracting features, a different ML algorithm is applied to compare the accuracy.Further, the triangle thresholding-based local features are ensembled with TSBTC II-ary global feature extraction technique to enhance performance. Additionally, in addition to the nine distinct ML algorithms, the recommended land use detection method uses three different ensembles of ML algorithms to evaluate performance. Results from the examination of this ensemble approach are based on the Land Use Dataset provided by UC Merced to demonstrate the benefit of using this ensemble method for classifying land use and significantly enhancing stand-alone methods.

Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime in public places has increased in the twenty first century. As a new branch of AVSS, baggage detection and classification has a broad area of security applications. Some of them are, detecting carriage of illegal materials into baggage, detecting unclaimed baggage in public space that can be placed by terrorists for violence, detecting baggage in baggage restricted super shop etc. However, in this paper, a detection & classification framework of baggage is proposed using dynamic human body parameter with boosting strategy. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with uneven illumination condition. Then, to overcome the shadow effect a model is introduced. Extraction of rotational signal descriptor (RSD_HOG) from Region of Interest (ROI) added efficiency in HOG. Finally, dynamic approach in human body parameter setting enabled the system to detect & classify single or multiple carried baggages although some portions of human are absent. In baggage detection, boosting of similarity measure based cascade multilayer SVMs into HOG based SVM generated a strong classifier. This scheme has used to deal with various texture patterns of baggages. Experimental results discovered the system satisfactorily accurate and faster comparative to other alternatives.

Biodetection dogs for COVID-19: an alternative diagnostic screening strategy

Notice board is the most consistent and main appliances in every university, school, or public place location such as bus stations, train stations, parks. But fixing and updating specific instructions on every day is a difficult operation. Wireless electronic notice boards are a faster alternative to conventional pin-up type notice boards. The main goal of this project is to design a wireless notice board to displays notifications that are received from a smartphone user. In this paper, the development of a simple and low-cost Android-based wireless notice board is presented. A prism-shaped device, having a portable circuit inside and an LCD outside is a solution for displaying any messages or notices immediately. A user can type a message in the app and the message will be displayed on the notice board instantly. It is a convenient substitute for a traditional noticeboard where written papers are pinned on the board. Other than institutional purposes, the same technology can be used in traffic signals, railway stations, bus stands, shopping malls, and in every public place where people can get messages or emergency alerts. The wireless notice board display is configured using NodeMCU, 16 × 02 LCD Display, I2C Adapter for 16 × 02 LCD Display, Buzzer, Mini Breadboard, Jumper Wires, and PVC Boards. As the electronic notice board is wireless so it minimizes the hassles of wires as well as it is very easy to operate and consumes less power.

This paper presents the motivation, design and evaluation of CountMeIn, a mobile collaborative pervasive memory game to revive social interactions in public places (e.g. a train station or bus stop). Two versions of CountMeIn were tested; an NFC-based and a touchscreen version. In a 2×1 within-subject (NFC vs. Touch) experiment (N = 20), postexperiment group interviews and findings indicate the NFC version led to increased perception of social presence while participants were more aware of others' actions and intentions (mode of co-presence). However, we did not find quantitative evidence that attributes of social presence were higher from the Social Presence Game Questionnaire. Together, our findings suggest that placement of a physical NFC interface does not necessarily increase perceived social presence when users play collaboratively. However, social expansion in mobile collaborative pervasive games can greatly benefit from people's mutual awareness from such an interface. This mutual awareness has the potential to both attract users and spectators, and reduce anxiety of users to invite spectators, or accept an invite from users.

Nowadays, great attention is paid to solving environmental issues and ensuring environmental safety of the territory. One of these issues is the disposal of consumer waste, the formation of which is growing exponentially. The problem of environmental pollution with glass waste is described in the article. It is proposed to dispose glass waste in special devices to solve this problem. The authors present the development of the machine for recycling glass bottles. The process of its work is described. This machine can be located in public places (shopping centers, hospitals, train stations, airports), where there may be accumulations of glass garbage.