High occupancy vehicle lane as per the buses flow rate and passenger trips

Mobility Demand Management initiatives are seen globally as a solution to the elimination of pollution and daily effects in off-road regions. In order to allow improvements on attainable comfort, such steps must have an effect. The High Occupancy Driver Carpool Lane is one of the TDM steps. HOV lanes are not utilized, with 81% of HOV locaters resulting streams under1400 vehicles per track during the PM peak hour strategy. This research is fitting in the Pune and Mumbai districts of India. HOV channels bear a 20 percent cap levy, reaching the highest advancement of 1600 Vphpl at 45 mph over the most drastic stream over 2000 Vphpl at 60 mph as a general justification for GP routes. HOV lanes deliver no investment funds for driving time. In general, HOV lanes decrease considerably as the usually helpful pathways are enabled to be clogged. In spite of these discoveries, HOV offices can take on a valuable job in the framework of all-around supervision of the expressway in India. Basically, where there is a meaning, they will be useful

Revealing the Feature Influence in HTTP Botnet Detection

Botnet are identified as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users’ network of computers as their target. In conjunction to that, many researchers has conduct a lot of study regarding on the botnets and ways to detect botnet in network traffic. Most of them only used the feature inside the system without mentioning the feature influence in botnet detection. Selecting a significant feature are important in botnet detection as it can increase the accuracy of detection. Besides, existing research focusses more on the technique of recognition rather than uncovering the purpose behind the selection. Therefore, this paper will reveal the influence feature in botnet detection using statistical method. The result obtained showed the accuracy is about 91% which is approximately acceptable to use the influence feature in detecting botnet activity

Revealing Influenced Selected Feature for P2P Botnet Detection

P2P botnet has become a serious security threat for computer networking systems. Botnet attack causes a great financial loss and badly impact the information and communication technology (ICT) system. Current botnet detection mechanisms have limitations and flaws to deal with P2P botnets which famously known for their complexity and scalable attack. Studies show that botnets behavior can be detected based on several detection features. However, some of the feature parameters may not represent botnet behavior and may lead to higher false alarm detection rate. In this paper, we reveal selected feature that influences P2P botnets detection. The result obtained by selecting features shows detection attack rate of 99.74%

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0

MANET performance optimization using network-based criteria and unmanned aerial vehicles

In this contribution we consider the problem of optimal drone positioning for improving the 1 operation of a mobile ad hoc network. We build upon our previous results devoted to the application 2 of game-theoretic methods for computing optimal strategies. One specific problem that arises in this 3 context is that the optimal solution cannot be uniquely determined. In this case, one has to use some 4 other criteria to choose the best (in some sense) of all optimal solutions. It is argued that centrality 5 measures as well as node ranking can provide a good criterion for the selection of a unique solution. 6 We showed that for two specific networks most criteria yielded the same solution thus demonstrating 7 good coherence in their predictions.

Recognition of Activities of Daily Living Based on Environmental Analyses Using Audio Fingerprinting Techniques: A Systematic Review

An increase in the accuracy of identification of Activities of Daily Living (ADL) is very important for different goals of Enhanced Living Environments and for Ambient Assisted Living (AAL) tasks. This increase may be achieved through identification of the surrounding environment. Although this is usually used to identify the location, ADL recognition can be improved with the identification of the sound in that particular environment. This paper reviews audio fingerprinting techniques that can be used with the acoustic data acquired from mobile devices. A comprehensive literature search was conducted in order to identify relevant English language works aimed at the identification of the environment of ADLs using data acquired with mobile devices, published between 2002 and 2017. In total, 40 studies were analyzed and selected from 115 citations. The results highlight several audio fingerprinting techniques, including Modified discrete cosine transform (MDCT), Mel-frequency cepstrum coefficients (MFCC), Principal Component Analysis (PCA), Fast Fourier Transform (FFT), Gaussian mixture models (GMM), likelihood estimation, logarithmic moduled complex lapped transform (LMCLT), support vector machine (SVM), constant Q transform (CQT), symmetric pairwise boosting (SPB), Philips robust hash (PRH), linear discriminant analysis (LDA) and discrete cosine transform (DCT).This work was supported by FCT project UID/EEA/50008/2013 (Este trabalho foi suportado pelo projecto FCT UID/EEA/50008/2013). The authors would also like to acknowledge the contribution of the COST Action IC1303—AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments

Examination of Urwin’s Framework in Information Technology Project Management Practices

Information Technology (IT) projects are organizational investments that require time, money, and other resources such as people and technology. In order to ensure the success of these projects, organizations are adopting project management approaches and setting up project management offices. In spite of this, projects still ended in partial or total failure. Despite the availability of frameworks in IT project management organizations still practice informal and ad hoc development in their project management causing additional costs and project delays or even failures. This study was done to examine the practice of Information Technology Project Management (ITPM) in organizations. The objectives of the study was first to review the existing frameworks in ITPM and then to propose a framework that is appropriate and that could be easily adopted by organizations. The study found that although there are many frameworks that can be adopted these existing frameworks are heavy in documentation and to adopt them required skilled or certified project managers which are not within the means of small and medium size organizations. Based on the review, the study had proposed Urwin’s framework as the most appropriate and practical project management approach for this study. This is because Urwin had created his framework of 12 themes after a long and intense study of information systems, information technology and project management literature. The 12 themes are strategy, leadership, scope, participation and commitment, project planning, project team, communication, risk management, training and resources, test management, organization structure and data. Urwin’s framework seemed most appropriate and practical because no certification is required and it can be implemented throughout the project life cycle. Also Urwin had showed how to implement each theme by providing a checklist under each theme. To examine Urwin’s framework interviews were conducted in two large organizations with two project managers of 15 and 17 years of experience each. Also a survey was conducted in 104 organizations. The respondents are project managers, IT executives, senior level employees and middle level employees with range of experience between two to 15 years. The results showed that all the 12 themes are well implemented in the two large organizations. Results of the survey showed that only three out of the 12 themes are well implemented. Findings from the study suggest that all 12 themes from Urwin’s framework must be well implemented to effectively managed ITPM. The study also put forth four recommendations to be practiced together with Urwin’s framework. They are executive focus and commitment; effective staffing; learning incrementally from experience; and baseline management. (Abstract by Author

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others