Search

Organisations are adopting new IT strategies such as “Bring Your Own Device” (BYOD) and remote working. These trends are highly beneficial both for enterprise owners and employees in terms of increased productivity and reduced costs. However, security issues such as unauthorised access as well as privacy concerns pose significant obstacles. These can be overcome by adopting access control techniques and a dynamic security and privacy policy that governs these issues where they arise. Policy decision points in traditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), or relationship-based access control (ReBAC), may be limited because the status of access control can vary in response to minor changes in user and resource properties. As a result, system administrators rely on a solution for constructing complex rules with many conditions and permissions for decision control. This results in access control issues, including policy conflicts, decision-making bottlenecks, delayed access response times and mediocre performance. This paper proposes a policy decision-making and access control-based supervised learning algorithm. The algorithm enhances policy decision points (PDPs). This is achieved by transforming the PDP’s problem into a binary classification for security access control that either grants or denies access requests. Also, a vector decision classifier based on the supervised machine learning algorithm is developed to generate an accurate, effective, distributed and dynamic policy decision point (PDP). Performance was evaluated using the Kaggle-Amazon access control policy dataset, which compared the effectiveness of the proposed mechanism to previous research benchmarks in terms of performance, time and flexibility. The proposed solution obtains a high level of privacy for access control policies because the PDP does not communicate directly with the policy administration point (PAP). In conclusion, PDP-based ML generates accurate decisions and can simultaneously fulfill multiple massive policies and huge access requests with 95% Accuracy in a short response time of around 0.15 s without policy conflicts. Access control security is improved by making it dynamic, adaptable, flexible and distributed.

The vehicular network is a collection of vehicles and other components facilitated with versatile sensors to communicate, which is backboned by authentication. Present authentication protocols focus on either the lightweight feature or security. On the other hand, privacy during authentication is essential. Moreover, existing schemes are dependent on the trusted authority for checking the legitimacy of a communicating node, which suffers from the infrastructure-less scenario. Alternatively, existing protocols are dependent on a secure communication channel during the registration phase. To address these concerns, an elliptic curve cryptography-based authentication protocol has been designed. Moreover, a secure key establishment is shown in this paper. Pseudo-id-based authentication is used to provide the privacy-preservation on the scheme. On the other hand, symmetric-key cryptography with a session key is used for message encryption during communication. A security analysis on the proposed scheme using the BAN logic and AVISPA shows the protocol’s resilience capabilities on various attacks. Alternatively, the mathematical proof of the protocol represents the proof of correctness. Moreover, extensive performance analysis with the existing authentication schemes shows that the proposed algorithm is better in computation cost, communication cost, and energy cost. Lastly, the NS3 simulation shows the packet data transfer among the nodes in the network.

User activity logs can be a valuable source of information in cloud forensic investigations; hence, ensuring the reliability and integrity of such logs is crucial. Most existing solutions for secure logging are designed for conventional systems rather than the complexity of a cloud environment. In this paper, we propose the Cloud Log Assuring Soundness and Secrecy (CLASS) process as an alternative scheme for the securing of logs in a cloud environment. In CLASS, logs are encrypted using the individual user's public key so that only the user is able to decrypt the content. In order to prevent unauthorized modification of the log, we generate proof of past log (PPL) using Rabin's fingerprint and Bloom filter. Such an approach reduces verification time significantly. Findings from our experiments deploying CLASS in OpenStack demonstrate the utility of CLASS in a real-world context.

Security and privacy are among the most critical challenges on the internet of drones (IoD) network. The communication entities of the IoD network can communicate securely with the use of authenticated key agreement (AKA) based techniques. However, the design of such techniques must balance the tradeoff between security and lightweight features. Recently, Chen et al. proposed an authentication and key sharing scheme for IoD deployment. It is, however, realized after scrutiny that the proposed technique is vulnerable to security attacks under the well-accepted Canetti-Krawczyk (CK) adversary model. Moreover, the scheme applies to the IoD network with only one drones’ flying zone. To solve these challenges, this paper proposed a secure lightweight proven authenticated key agreement (SLPAKA) technique for IoD deployment. The technique is free from all the problems identified in the scheme of Chen et al . To ensure the reliability of the SLPAKA, the security of the technique has been assessed from a theoretical method and formal way using the ProVerif cryptographic protocol verification tool. Apart from comparing the performance of SLPAKA with the benchmarking schemes in terms of security, computational cost, and communication cost, the SLPAKA and the technique proposed by Chen et al. are implemented using a python programming language to evaluate and compare their performance in terms of energy consumption and computational time metrics. The results show that the SLPAKA outperforms the technique of Chen et al. and all the other benchmarking techniques in terms of security and lightweight features.

Internet of Drones (IoD) is a decentralized network and management framework that links drones’ access to the controlled airspace and provides inter-location navigation services. The interconnection of drones in the IoD network is through the Internet of Things (IoT). Hence the IoD network is vulnerable to all the security and privacy threats that affect IoT networks. It is highly required to safeguard a good atmosphere free from security and privacy threats to get the desired performance from IoD applications. Security and privacy issues have significantly restricted the overall influence of the IoD paradigm. There are existing survey studies that helped lay a vital foundation for understanding the IoD security and privacy issues. However, not all have thoroughly investigated the level of security and privacy threats associated with the various drone categories. Besides, most existing review studies do not examine secured IoD architecture. This paper aims to assess the recent trends in the security and privacy issues that affect the IoD network. We investigate the level of security and privacy threats of the various drone categories. We then highlight the need for secured IoD architecture and propose one. We also give a comprehensive taxonomy of the attacks on the IoD network. Moreover, we review the recent IoD attack mitigating techniques. We also provide the performance evaluation methods and the performance metrics employed by the techniques. Finally, we give research future direction to help researchers identify the latest opportunities in IoD research.

A feature-based retinal image registration (RIR) technique aligns multiple fundus images and composed of pre-processing, feature point extraction, feature descriptor, matching and geometrical transformation. Challenges in RIR include difference in scaling, intensity and rotation between images. The scale and intensity differences can be minimised with consistent imaging setup and image enhancement during the pre-processing, respectively. The rotation can be addressed with feature descriptor method that robust to varying rotation. Therefore, a feature descriptor method is proposed based on statistical properties (FiSP) to describe the circular region surrounding the feature point. From the experiments on public Fundus Image Registration dataset, FiSP established 99.227% average correct matches for rotations between 0° and 180°. Then, FiSP is paired with Harris corner, scale-invariant feature transform (SIFT), speeded-up robust feature (SURF), Ghassabi's and D-Saddle feature point extraction methods to assess its registration performance and compare with the existing feature-based RIR techniques, namely generalised dual-bootstrap iterative closet point (GDB-ICP), Harris-partial intensity invariant feature descriptor (PIIFD), Ghassabi's-SIFT, H-M 16, H-M 17 and D-Saddle-histogram of oriented gradients (HOG). The combination of SIFT-FiSP registered 64.179% of the image pairs and significantly outperformed other techniques with mean difference between 25.373 and 60.448% (p =

Edge computing has significantly enhanced the capabilities of cloud computing. Edge data-centres are used for storing data of the end-user devices. Secure communication between the legitimate edge data-centres during the load balancing process has attracted industrial and academic researchers. Recently, Puthal et al. have proposed a technique for authenticating edge datacenters to enable secure load balancing. However, the resource-constraint nature of the edge data-centres is ignored. The scheme is characterized by complex computation and memory intensive cryptographic protocol. It is also vulnerable to key escrow attack because the secret key used for encrypting and decrypting of the communicated messages is been created by the trusted cloud datacenter. Additionally, the key sharing phase of their algorithm is complex. Therefore, to address the highlighted challenges, this paper proposed a lightweight key escrow-less authentication algorithm that will ensure secure communication of resource-constrained edge data-centres during the load balancing process. The security capability of the proposed scheme has been formally evaluated using the automatic cryptographic analytical tool ProVerif. The relatively low computation and communication costs of the proposed scheme compared to the benchmark schemes proved that it is lightweight, thus suitable for resource-constrained edge datacenters.

Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based diagnosis enables doctors to make cancer diagnostic decisions more confidently than with mammograms. Several studies have used Hp images to classify BrC. However, the performance of classification models is compromised due to the higher misclassification rate. Therefore, this study aimed to develop a reliable, accurate, and computationally cost-effective ensembled BrC classification network (EBrC-Net) model with three misclassification algorithms to diagnose breast malignancy in early stages using Hp images. The proposed EBrC-Net model is based on the deep convolutional neural network approach. For experiments, the publicly available BreakHis dataset was used and split into training, validation, and testing sets. In addition, image augmentation was adopted for the training set only, and features were extracted through the well-trained EBrC-Net. Thereafter, the extracted features were further evaluated by six machine learning classifiers, of which two best performing classifiers (i.e., softmax and k-nearest neighbour [kNN]) were selected on the basis of five performance metric evaluation results. Furthermore, three misclassification reduction (McR) algorithms were developed and implemented in cascaded manner to reduce the false predictions of the softmax and kNN classifiers. After the implementation of the McR algorithms, experiments showed that the kNN results were much better and reliable than the softmax. The proposed BrC classification model achieved accuracy, specificity, and sensitivity rates of 97.74%, 100%, and 97.01%, respectively. Moreover, the performance of proposed BrC classification model was compared with that of state-of-the-art baseline models. Findings showed that the proposed EBrC-Net classification model, coupled with the proposed McR algorithms, achieved the best results in comparison with the baseline classification models. The proposed EBrC-Net model and the McR algorithms are a reliable source for doctors aiming for second opinion in making early diagnostic decisions for BrC using Hp images.

Upper limb angular motion measurement is particularly a subject of interest in the field of rehabilitation. It is commonly measured manually by physiotherapist via goniometer to monitor stroke patient’s progress. However, manual measurement has many inherent drawbacks such as the need to have both patient and physiotherapist to be at the same place. In this paper, an automated real-time single-camera upper limb angular motion measuring system is proposed for home-based rehabilitation to aid physiotherapist to monitor patient’s progress. The measurement of concern are angle measurement of elbow extension, elbow flexion, wrist flexion and wrist extension. To do this, we propose a method that utilized predefined coordinate points extracted from the contours of the object named as contour based motion tracking. The proposed method overcome problems of prior target tracking techniques such as Kalman filter, Optical flow and Cam-shift. The proposed method includes skin region segmentation and arm modelling for motion tracking. Prior skin segmentation techniques suffer from fixed threshold value set by the user. Therefore, we introduce dynamic threshold based on the lower and upper threshold boundary of isolated skin regions from the background. To ensure the reliability of our skin segmentation method, we compare them with four different related algorithms. The result shows that our skin segmentation method outperformed the prior method with 93% detection accuracy. Following the segmentation, we model the arm motion tracking by formulating mathematical equation of various points and motion velocities to track the arm. We then model the wrist and elbow position to estimate arm angular motion. The method is put together and tested with real human subject and other test settings. The result shows that our proposed method able to produce an accurate and reliable reading of +-1.25 average range of error from actual physiotherapist reading.

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.

Retrieving desired information from databases containing video, natural scene, and license plate images through keyword spotting is a big challenge to expert systems due to different complexities that occur because of background and foreground variations of texts in real-time environments. To reduce background complexity of input images, we introduce a new model based on fractional means that considers neighboring information of pixels to widen the gap between text and background. To do so, the process obtains text candidates with the help of k-means clustering. The proposed approach explores the combination of Radon and Fourier coefficients to define context features based on regular patterns given by coefficient distributions for foreground and background of text candidates. This process eliminates non-text candidates regardless of different font types and sizes, colors, orientations and scripts, and results in representatives of texts. The proposed approach then exploits the fact that text pixels share almost the same values to restore missing text components using Canny edge image by proposing a new idea of minimum cost path based ring growing, and then outputs keywords. Furthermore, the proposed approach extracts the same above-mentioned features locally and globally for spotting words from images. Experimental results on different benchmark databases, namely, ICDAR 2013, ICDAR 2015, YVT, NUS video data, ICDAR 2013, ICDAR 2015, SVT, MSRA, UCSC, Medialab and Uninusubria license plate data show that the proposed method is effective and useful compared to the existing methods.

Taking into consideration that the prior CMF detection methods rely on several fixed threshold values in the filtering process, we propose an efficient CMF detection method with an automatic threshold selection, named as CMF-iteMS. The CMF-iteMS recommends a PatchMatch-based CMF detection method that adapts Fourier-Mellin Transform (FMT) as the feature extraction technique while a new automatic threshold selection based on iterative means of regions size (iteMS) procedure is introduced to have flexibility in changing the threshold value for various characteristics (quality, sizes, and attacks) in each input image. To ensure the reliability of the proposed CMF-iteMS, the method is compared with four state-of-the-art CMF detection methods based on Scale Invariant Feature Transform (SIFT), patch matching, multi-scale analysis and symmetry techniques using three available datasets that cover the variety of characteristics in CMF images. The results show that the F-score of the CMF-iteMS outperformed existing CMF detection methods by exceeding an average of 90% F-score values for image-level evaluation and 82% of F-score value for pixel-level evaluation for all datasets in original size. As special attention is given to the image resizing attack, the method is able to maintain the highest performance even if the images in the datasets are resized to 0.25 parameter.

Feature detection is an important concept in the area of image processing to compute image abstractions of image information, which is used for image recognition and many other applications. One of the popular algorithm used is called the Speeded-Up Robust Features (SURF), which realized the scale space pyramid to detect the features. For this reason, prior researchers concentrate on applying parallelism onto the SURF multiple layers using technology such as Field Programmable Gate Array (FPGA). However, prior FPGA-SURF implementation does not emphasis on memory access limitation that can affect the overall performance of a system. This paper proposes a study on FPGA-SURF and memory access implementation in feature detection area. We conduct a profiling test and founds that the external memory access to fetch the integral image data in SURF highly affects the overall performance. We also found that the SURF algorithm memory access has redundant repeating pattern that can be reduced. Therefore, a controller design that stores repeating data (for the subsequent process) in an on-chip memory is proposed. This method reduces the external memory access and can increase the overall performance. The result shows that our proposed method improves the existing method (i.e. without the memory access reduction) by 1.23 times when the external memory latency is 20ns.

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance.

Globally, breast cancer (BC) is the prevailing cause of unusual deaths in women. Breast tumor (BT) is a primary symptom and may lead to BC. Digital histology (DH) image modality is a gold standard medical test for a definite diagnosis of BC. Traditionally, DH images are visually examined by two or more pathologists to come up with a consensus for authentic BC detection which may cause a high error rate. Therefore, researchers had developed automated BC detection models using a machine learning (ML) based approach. Thus, this study aims to develop a BC detection model through ten feature extraction methods which extract both local and global type features from publicly available breast histology dataset. The extracted features are sorted by their weights, which are computed by the neighborhood component analysis method. A feature selection algorithm is developed to find the minimum number of discriminating features, evaluated through seven heterogeneous traditional ML classifiers. The proposed ML-based BC detection model acquired 90% accuracy for the initial testing set using 51 Harris features. Whereas, for the extended testing set, only three Harris features is shown 93% accuracy. The proposed BC detection model can assist the doctor in giving a second opinion.

Mobile communication witnesses a new generation after every 10 years. Following the same trend, the roll out of next generation called 5G network is anticipated by 2020. In this chapter, a critical review of enabling technologies and research issues of 5G network is provided. The novelty of the chapter lies in providing a holistic view of 5G networks spanning discussions on technologies and issues across all the layers of protocol stack. Specifically, the chapter talks about the higher-level research issues of 5G network. The chapter is primarily structured as follows: It starts with a brief overview of various generations of mobile communication. Then, the problems with existing generation of mobile communication are presented, thus providing the motivation for a new generation of mobile network. A survey of different enabling technologies of 5G network is provided afterwards. After having brief discussions on key enablers, the chapter presents various research issues of 5G network. The chapter concludes with highlighting current challenges and future research issues.

Worldwide, the burden of cancer is drastically increasing over the past few years. Among all types of cancers in women, breast cancer (BrC) is the main cause of unnatural deaths. For early diagnosis, histopathology (Hp) imaging is a gold standard for positive and detailed (at tissue level) diagnosis of breast tumor (BrT) compared to mammogram images. A large number of studies used BrT Hp images to solve binary or multiclassification problems using high computational resources. However, classification models' performance may be compromised due to the high correlation among various types of BrT in Hp images, which raises the misclassification rate. Thus, this paper aims to develop a tree-based BrT multiclassification model via deep learning (DL) to extract discriminative features to solve the multiclassification problem with better performance using less computational resources. The main contributions of this work are to create an ensemble, tree-based DL model that is pre-trained on the BreakHis dataset, and implementation of a misclassification reduction algorithm. The ensemble, tree-based DL model, extracts discriminative BrT features from Hp images. The target dataset (i.e., Bioimaging challenge 2015 breast histology) is small in size; thus, to avoid overfitting of the proposed model, pretraining is performed on the BreakHis dataset. Whereas, misclassification reduction algorithm is implemented to enhance the performance of the classification model. The experimental results show that the proposed model outperformed the existing state-of-the-art baseline studies. The achieved classification accuracy is ranging from 87.50 % to 100 % for four subtypes of BrT. Thus, the proposed model can assist doctors as the second opinion in any healthcare centre.

Abstract The velocity parameter in mobile Wireless Sensor Networks (WSNs) is a critical factor in anchor nodes distribution. However, most of the previous schemes use the random velocity to transmit anchor nodes as in the waypoint mobility model, which produces a considerable overlap between anchor nodes without improving the localization accuracy. In this paper, we improve such model by controlling the anchor node velocity. In the proposed scheme (EDAL), the anchor node velocity is a function of the overlap degree between anchor nodes and number of anchor node in the neighbor. Thus, EDAL can distribute the anchor nodes efficiently to improve the localization accuracy and expand the coverage area simultaneously. We evaluate the EDAL performance through extensive simulation experiments. توزيع نقاط الربط (المرساة) في شبكات الاستشعار اللاسلكية المتنقلة من اجل توطين دقيق (EDAL) ملخص: تعد معلمة السرعة في شبكات الاستشعار اللاسلكية المتنقلة (WSNs) عاملاً هامًا في توزيع نقاط الربط (المرساة)، معظم المخططات السابقة تستخدم السرعة العشوائية لإرسال نقاط الربط كما هو الحال في نموذج التنقل بنقطة الطريق (Waypoint)، والذي ينتج تداخلًا كبيرًا بين عقد الربط دون تحسين دقة التعريب. في هذه الورقة، نقوم بتحسين هذا النموذج من خلال التحكم في سرعة عقد الربط، في المخطط المقترح (EDAL)، تعد سرعة عقد الربط مهمة لحساب درجة التداخل بين عقد الربط وعدد العقد في الجوار، وبالتالي، يمكن لـ(EDAL)توزيع نقاط الربط بكفاءة لتحسين دقة التموضع وتوسيع مساحة التغطية في وقت واحد. نقيم أداء(EDAL)من خلال تجار بمحاكاة واسعة النطاق. الكلمات المفتاحية: شبكات الاستشعار اللاسلكية المتنقلة، نموذج التنقل بنقطة الطريق. &nbsp

The complex nature of SDN coupled with the huge number of services they provide; makes the system as a whole prone to some malicious attacks. An attack such as Link Fabrication Attack (LFA) is becoming more and more common with a high degree of sophistication. Different techniques exist which examine the occurrence of LFA in SDN. To ensure that SDN does not suffer from the same attack from the same origin, we have to consider in general the entire system liability, service information, dependencies, and switch as well as host vulnerabilities. However, due to the complex nature and scalability property of SDN, determining the origin of the LFA is not a straight forward. Although other solutions have been proposed to address this problem in traditional networks, a formal method to security in SDN is lacking. In this paper, we discuss a formal method for SDN using Higher-Order Logic (HOL) and as a case study used to examine the LFA in SDN.

This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Audio streams, such as news broadcasting, meeting rooms, and special video comprise sound from an extensive variety of sources. The detection of audio events including speech, coughing, gunshots, etc. leads to intelligent audio event detection (AED). With substantial attention geared to AED for various types of applications, such as security, speech recognition, speaker recognition, home care, and health monitoring, scientists are now more motivated to perform extensive research on AED. The deployment of AED is actually a more complicated task when going beyond exclusively highlighting audio events in terms of feature extraction and classification in order to select the best features with high detection accuracy. To date, a wide range of different detection systems based on intelligent techniques have been utilized to create machine learning-based audio event detection schemes. Nevertheless, the preview study does not encompass any state-of-the-art reviews of the proficiency and significances of s...

The widespread use of digital images has led to a new challenge in digital image forensics. These images can be used in court as evidence of criminal cases. However, digital images are easily manipulated which brings up the need of a method to verify the authenticity of the image. One of the methods is by identifying the source camera. In spite of that, it takes a large amount of time to be completed by using traditional desktop computers. To tackle the problem, we aim to increase the performance of the process by implementing it in a distributed computing environment. We evaluate the camera identification process using conditional probability features and Apache Hadoop. The evaluation process used 6000 images from six different mobile phones of the different models and classified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop. We ran the source camera identification process in a cluster of up to 19 computing nodes. The experimental results demonstrate exponential decrease in processing times and slight decrease in accuracies as the processes are distributed across the cluster. Our prediction accuracies are recorded between 85 to 95% across varying number of mappers.