https://cronfa.swan.ac.uk/Record/cronfa44873/Download/0044873-26102018132818.pdf

Exploring barriers of m-commerce adoption in SMEs in the UK: Developing a framework using ISM

With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

/pdf/response-based-methods-to-measure-road-surface-irregularity-n2p196ci94.pdf

Response-based methods to measure road surface irregularity: a state-of-the-art review

Extending TAM for Online Learning Systems： An Intrinsic Motivation Perspective

Crime in the telecommunications sector increasingly, especially in the mobile security system found several security flaws of data outside of the network. Clone SIM card is a major problem in the SIM card device. Research cloning SIM card can be presented in the form of analysis algorithms A3 SRES, and A8 RAND to get Ki AUC for the investigation process digital forensic cloning SIM card, testing scheme SIM card cloning used parameter ＂Due Under Test＂ (DUT) and ＂Trial and Error＂ with the following phases ; identification, preservation, collection, examination, anally and presentation. Conclusion SIM card cloning and analysis in the form of percentage of success then conducted a forensic investigation to cloning SIM card with the matching algorithm A8 (RAND) contained in each SIM card which produces authentication Ki as contained in the investigation file structure SIM card. Memory capacity has advantages and disadvantages, which is 32kb SIM card Ki produced a success rate of 100% success, 64kb SIM card cloning success rate of 25% to 50%. Research cloning SIM card with forensic investigations have been successfully cloned.

Forensic SIM Card Cloning Using Authentication Algorithm

Automatic systems for pavement inspection can significantly enhance the performance of the Pavement Management Systems (PMSs). Cracking is the most current distress in any type of pavement. Progress of various technologies leads to a lot of effort in developing an automatic system for pavement cracking inspection. In the early image-based systems, the feature extraction process for crack classification must be done by using various image processing techniques in an expert-based system. In recent years, the new machine learning techniques such as a deep convolutional neural network (DCNN) provide more efficient models with the ability of automatic feature extracting, but these models need a lot of labeled data for training. Transfer learning is a technique that solves this problem using pre-trained models. In this research, several pre-trained models (AlexNet, GoogleNet, SqueezNet, ResNet-18, ResNet-50, ResNet-101, DenseNet-201, and Inception-v3) have been used to retrain based on pavement images using transfer learning. This study aims to evaluate the efficiency of retrained DCNNs in the detection and classification of the pavement cracking. Also, it presents a more effective algorithm based on a developed wavelet transform module with more regulizer parameters for crack segmentation. The result indicated that retrained classifier models provide reliable outputs with a range of 0.94 to 0.99 in confusion matrix-based performance, but the speed of some models is significantly higher than others. Also, the results clarified that the developed wavelet module could segment crack pixels with a high level of clarity.

An image-based system for pavement crack evaluation using transfer learning and wavelet transform

Mobile computing and Mobile Commerce is most popular now a days because of the service offered during
the mobility. Mobile computing has become the reality today rather than the luxury.Mobile wireless market
is increasing by leaps and bounds. The quality and speeds available in the mobile environment must
match the fixed networks if the convergence of the mobile wireless and fixed communication network is to happen in the real sense. The challenge for mobile network lies in providing very large footprint of mobile services with high speed and security. Online transactions using mobile devices must ensure high security for user credentials and it should not be possible for misuse. M-Commerce is the electronic commerce performed using mobile devices. Since user credentials to be kept secret, a high level of security should be ensured

Security issues and challenges in mobile Computing and m-commerce

Tuberculosis (TB) is an airborne disease caused by Mycobacterium tuberculosis. It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased in mid to low-income countries due to the rise of automation in healthcare sectors. The already limited resources are being heavily allocated towards controlling other dangerous diseases. Modern digital radiography (DR) machines, used for screening chest X-rays of potential TB victims are very practical. Coupled with computer-aided detection (CAD) with the aid of artificial intelligence, radiologists working in this field can really help potential patients. In this study, progressive resizing is introduced for training models to perform automatic inference of TB using chest X-ray images. ImageNet fine-tuned Normalization-Free Networks (NFNets) are trained for classification and the Score-Cam algorithm is utilized to highlight the regions in the chest X-Rays for detailed inference on the diagnosis. The proposed method is engineered to provide accurate diagnostics for both binary and multiclass classification. The models trained with this method have achieved 96.91% accuracy, 99.38% AUC, 91.81% sensitivity, and 98.42% specificity on a multiclass classification dataset. Moreover, models have also achieved top-1 inference metrics of 96% accuracy and 98% AUC for binary classification. The results obtained demonstrate that the proposed method can be used as a secondary decision tool in a clinical setting for assisting radiologists.

/pdf/ai-assisted-tuberculosis-detection-and-classification-from-1m4gmtx6.pdf

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Identification and authentication play a significant role in security controls. The manual process of user identification and authentication is time-consuming and tedious. The proposed research work aims to overcome this drawback and achieve user identification and authentication through an automated method using the Aadhar card. Aadhar project is developed by the Unique Identification Authority of India by merging biometrics and digitization. The details are stored in the form of a quick response code along with a 12-digit Aadhar card number. A model is built to capture the information from the Aadhar card. The mobile phones are used to obtain the data from the Aadhar card. The user validation is through two-step process using One Time Password and email address. The wireless public key infrastructure issues a digital certificate to the valid user. The proposed authentication and symmetric key exchange algorithm are formally verified and analyzed using automated validation of Internet security protocols and applications. The result of simulation proves that the proposed scheme is secure and safe. The experimental results show that time consumption for user authentication is acceptable. The acceptance of the proposed automated user identification and authentication model is authorized using a modified technology acceptance model. The result proves that there exists a strong relationship between the perceived usefulness of items and the Attribute Of Use (AOU). It also exhibits that the association between the AOU and user satisfaction is influential. The model demonstrates that the association between the Perceived Ease Of Use and attribute towards use is weak. The influence of exogenous variables over endogenous variables exhibits that the proposed model outperforms the traditional manual system and the target users are inclined towards it. The proposed model also finds its application in services which requires user authentication.

/pdf/automated-user-authentication-in-wireless-public-key-2p4tzsgfao.pdf

Automated User Authentication in Wireless Public Key Infrastructure for Mobile Devices Using Aadhar Card

Potholes are a major nuisance on city roads leads to several problems and losses in productivity. Local authorities have cited a lack of geographic localization of these potholes as one of the rate limiting factors for repairs. This study proposes a

Pothole Detection and Analysis System (PoDAS) for Real Time Data Using Sensor Networks

Public key cryptography is widely used for secure data transfer in various applications. The Public key infrastructure provides a foundation that supports public key cryptography, and it is required to deliver the public keys to existing systems or users securely. The public key is exchanged digitally in the form of digital certificates having a certain period of validity. Circumstances may
arise under which the validity of the certificates should be ceased, and they need to be revoked. The fast growth of e-commerce demands verification and data transfers to be done in minimum time to maintain high efficiency of communication. In this paper, a new method of developing
certificate revocation lists is proposed. The formal model of enhanced PKI for mobile commerce security is put forward. The performance of NTRU is compared with the RSA algorithm and the ElGamal algorithm. The paper also sets forth a comparative analysis of the proposed method with
other existing techniques. The results show that the proposed technology has the upper hand over the current methods.

K Krishna Prakasha

Papers

Security issues and challenges in mobile Computing and m-commerce

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model

Automated User Authentication in Wireless Public Key Infrastructure for Mobile Devices Using Aadhar Card

Pothole Detection and Analysis System (PoDAS) for Real Time Data Using Sensor Networks

Efficient digital certificate verification in wireless public key infrastructure using enhanced certificate revocation list