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In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed by healthcare professionals. Thus, in the study, we propose an automatic TB detection system using advanced deep learning (DL) models. A significant portion of a CXR image is dark, providing no information for diagnosis and potentially confusing DL models. Therefore, in the proposed system, we use sophisticated segmentation networks to extract the region of interest from multimedia CXRs. Then, segmented images are fed into the DL models. For the subjective assessment, we use explainable artificial intelligence to visualize TB-infected parts of the lung. We use different convolutional neural network (CNN) models in our experiments and compare their classification performance using three publicly available CXR datasets. EfficientNetB3, one of the CNN models, achieves the highest accuracy of 99.1%, with a receiver operating characteristic of 99.9%, and an average accuracy of 98.7%. Experiment results confirm that using segmented lung CXR images produces better performance than does using raw lung CXR images. [ABSTRACT FROM AUTHOR]

The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed. [ABSTRACT FROM AUTHOR]

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate. [ABSTRACT FROM AUTHOR]

A new speech feature extraction technique called moving average multi directional local features (MA-MDLF) is presented in this paper. This method is based on linear regression (LR) and moving average (MA) in the time–frequency plane. Three-point LR is taken along time axis and frequency axis, and 3 points MA is taken along 45° and 135° in the time–frequency plane. The LR captures the voice onset\offset, formant contour, while the moving average captures the dynamics on time–frequency axes which can be seen as voiceprints. The MA-MDLF performance is compared to commonly used speech features in speaker recognition. The comparison is performed in a speaker recognition system (SRS) for three different conditions, namely clean speech, mobile speech, and cross channel. MA-MDLF has shown better performance than the baseline MFCC, RASTA-PLP and LPCC. In clean and mobile speech, MA-MDLF feature performs the best and also in the cross channel task MA-MDLF performed excellent. We also evaluated the MA-MDLF using three speech databases, namely KSU, LDC Babylon and TIMITdatabases, and found that MA-MDLF outperformed the other commonly used features with speech from all the three databases. The first and second databases are for Arabic speech while third is for English speech. [ABSTRACT FROM AUTHOR]

We propose a cognitive Internet of Things (IoT)-cloud-based smart healthcare framework, which communicates with smart devices, sensors, and other stakeholders in the healthcare environment; makes an intelligent decision based on a patient's state; and provides timely, low-cost, and accessible healthcare services. As a case study, an EEG seizure detection method using deep learning is also proposed to access the feasibility of the cognitive IoT-cloud smart healthcare framework. In the proposed method, we use smart EEG sensors (apart from general healthcare smart sensors) to record and transmit EEG signals from epileptic patients. Thereafter, the cognitive framework makes a real-time decision on future activities and whether to send the data to the deep learning module. The proposed system uses the patient's movements, gestures, and facial expressions to determine the patient's state. Signal processing and seizure detection take place in the cloud, while signals are classified as seizure or non-seizure with a probability score. The results are transmitted to medical practitioners or other stakeholders who can monitor the patients and, in critical cases, make the appropriate decisions to help the patient. Experimental results show that the proposed model achieves an accuracy and sensitivity of 99.2 and 93.5%, respectively. [ABSTRACT FROM AUTHOR]

With the recent development of Cognitive Internet of Things (CIoT) and the potential of Cyber Physical System (CPS), people’s daily activities become smarter, and intelligent. The combination of CIoT and CPS can greatly enhance the quality of people’s life. To this end, this article proposes CIoT-CPS that comprises of two main models: user activity cognitive model and image authentication model.The user activity cognitive model (UACM) is a machine-learning model to have the meaningful data. The image authentication model is to verify the authenticity of images captured by various devices, such as smart phones, digital cameras, and other camera-embedded portable devices. The authenticity of an image is breached when parts of images are assembled to produce a new image (known as a splicing forgery), or a part of an image is copied or pasted into another part of the same image (known as a copy-move forgery). In the proposed verification method, an opposite color local binary pattern (OC-LBP) texture descriptor is applied to a questioned image. The image is first decomposed into an RGB (red, green, blue) and a luminance and chroma color spaces. The OC-LBP measures the interrelation between pixels of different color components. The intensive computation involving six color components and a gray version is performed in the cloud, where a server can be dedicated to doing this job. The histograms of the OC-LBP are concatenated with weights to produce a final feature vector of the image. A support vector machine is applied as a classifier, which classifies the image as authentic or forged. Several experiments were performed to verify the suitability of those models or approaches. The proposed approaches show a good accuracy compared to other competing approaches. [ABSTRACT FROM AUTHOR]

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system. [ABSTRACT FROM AUTHOR]

With the advent of future generation mobile communication technologies (5G), there is the potential to allow mobile users to have access to big data processing over different clouds and networks. The increasing numbers of mobile users come with additional expectations for personalized services (e.g., social networking, smart home, health monitoring) at any time, from anywhere, and through any means of connectivity. Because of the expected massive amount of complex data generated by such services and networks from heterogeneous multiple sources, an infrastructure is required to recognize a user's sentiments (e.g., emotion) and behavioral patterns to provide a high quality mobile user experience. To this end, this paper proposes an infrastructure that combines the potential of emotion-aware big data and cloud technology towards 5G. With this proposed infrastructure, a bimodal system of big data emotion recognition is proposed, where the modalities consist of speech and face video. Experimental results show that the proposed approach achieves 83.10 % emotion recognition accuracy using bimodal inputs. To show the suitability and validity of the proposed approach, Hadoop-based distributed processing is used to speed up the processing for heterogeneous mobile clients. [ABSTRACT FROM AUTHOR]

Cloud computing brings several advantages such as flexibility, scalability, and ubiquity in terms of data acquisition, data storage, and data transmission. This can help remote healthcare among other applications in a great deal. This paper proposes a cloud based framework for speech enabling healthcare. In the proposed framework, the patients or any healthy person seeking for some medical assistance can send his/her request by speech commands. The commands are managed and processed in the cloud server. Any doctor with proper authentication can receive the request. By analyzing the request, the doctor can assist the patient or the person. This paper also proposes a new feature extraction technique, namely, interlaced derivative pattern (IDP), to automatic speech recognition (ASR) system to be deployed into the cloud server. The IDP exploits the relative Mel-filter bank coefficients along different neighborhood directions from the speech signal. Experimental results show that the proposed IDP-based ASR system performs reasonably well even when the speech is transmitted via smart phones. [ABSTRACT FROM AUTHOR]

The increasing demand for the remote monitoring of patients combined with the promising potential of cloud computing has enabled the design and development of a number of cloud-based systems and services for healthcare. The cloud computing, in combination with the popularity of smart handheld devices, has inspired healthcare professionals to remotely monitor patients' health while the patient is at home. To this end, this paper proposes a cloud-assisted speech and face recognition framework for elderly health monitoring, where handheld devices or video cameras collect speech along with face images and deliver to the cloud server for possible analysis and classification. In the framework, a patient's state such as pain, tensed, and so forth is recognized from his or her speech and face images. The patient state recognition system extracts local features from speech, and texture descriptors from face images. Then it classifies using support vector machines. The recognized state is later sent to the remote care center, healthcare professionals and providers for necessary services in order to provide seamless health monitoring. Experiments have been performed to validate the approach and to evaluate the suitability of this framework in terms of accuracy and time requirements. The results demonstrate the effectiveness of the proposed approach with regards to face and speech processing. [ABSTRACT FROM AUTHOR]

The fast spread of the COVID-19 over the world pressured scientists to find its cures. Especially, with the disastrous results, it engendered from human life losses to long-term impacts on infected people's health and the huge financial losses. In addition to the massive efforts made by researchers and medicals on finding safe, smart, fast, and efficient methods to accurately make an early diagnosis of the COVID-19. Some researchers focused on finding drugs to treat the disease and its symptoms, others worked on creating effective vaccines, while several concentrated on finding inhibitors for the key enzymes of the virus, to reduce its spreading and reproduction inside the human body. These enzymes' inhibitors are usually found in aliments, plants, fungi, or even in some drugs. Since these inhibitors slow and halt the replication of the virus in the human body, they can help fight it at an early stage saving the patient from death risk. Moreover, if the human body's immune system gets rid of the virus at the early stage it can be spared from the disastrous sequels it may leave inside the patient's body. Our research aims to find aliments and plants that are rich in these inhibitors. In this paper, we developed a deep learning application that is trained with various aliments, plants, and drugs to detect if a component contains SARS-CoV-2 key inhibitor(s) intending to help them find more sources containing these inhibitors. The application is trained to identify various sources rich in thirteen coronavirus-2 key inhibitors. The sources are currently just aliments, plants, and seeds and the identification is done by their names. [ABSTRACT FROM AUTHOR]

In this paper, a novel image forgery detection method is proposed based on the steerable pyramid transform (SPT) and local binary pattern (LBP). First, given a color image, we transform it in the YCbCr color space and apply the SPT transform on chrominance channels Cb and Cr, yielding a number of multi-scale and multi-oriented subbands. Then, we describe the texture in each SPT subband using LBP histograms. The histograms from each subband are concatenated to produce a feature vector. Finally, a support vector machine uses the feature vector to classify images into forged or authentic. The proposed method has been evaluated on three publicly available image databases. Our experimental results demonstrate the effectiveness of the proposed method and its superiority over some recent other methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multimodal biometric system with high security level based on finger and palm print. Verification/identification is performed by fusing features form different finger and palm prints. The features are extracted using Zernike moments (ZM) invariant algorithm. Fisher's ratio ( F-ratio) is used to select the optimum moments. The experimental results show that ring fingerprint, palm print, middle fingerprint, and thumb fingerprint give better performance than other fingerprints. Applying F-ratio on these four prints can recognize all the 50 individuals participated in the experiments. [ABSTRACT FROM AUTHOR]

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs. Deep learning techniques is a technological solution that has been used in healthcare lately. Nowadays, with the increase in chips processing capabilities, increase size of data, and the progress in deep learning research, healthcare applications have been proposed to provide citizens' health needs. In addition, a big amount of data is generated every day. Development in Internet of Things, gadgets, and phones has allowed the access to multimedia data. Data such as images, video, audio and text are used as input of applications based on deep learning methods to support healthcare system to diagnose, predict, or treat patients. This review pretends to give an overview of proposed healthcare solutions based on deep learning techniques using multimedia data. We show the use of deep learning in healthcare, explain the different types of multimedia data, show some relevant deep learning multimedia applications in healthcare, and highlight some challenges in this research area. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate. [ABSTRACT FROM AUTHOR]

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures. [ABSTRACT FROM AUTHOR]

The proposed model in "Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare", improves the medical image classification and optimization for better disease diagnosis. The manuscript entitled "Deep learning on digital mammography for expert-level diagnosis accuracy in breast cancer detection", proposes a breast cancer detection system using deep learning. The results obtained within the scope of the study indicate that diversifying X-ray images with tissue analysis methods in the diagnosis of COVID-19 and including CNN input provides significant improvements in the results. Text, radiological pictures, audio notes, video, and other types of multimedia healthcare data are all generated by today's smart healthcare system [[1]]. [Extracted from the article]

In this study, researchers examined the effect of replacing a high-volume of cement with sugarcane bagasse ash (BA) and silica fume (SF). In addition to the control, three binary and three ternary blends of concrete containing different percentages of cement/BA and cement/BA/SF were tested to determine the various mechanical and microstructural properties of concrete. For each mix, eighteen cylindrical concrete specimens were cast followed by standard curing (moist at 20 °C) to test the compressive and tensile strengths of three identical specimens at 7, 28, and 91 days. The test results indicated that the binary mix with 20% BA and ternary mix with 33% BA and 7% SF exhibited higher strengths than all the other mixes, including the control. The higher strengths of these mixes are also validated by their lower water absorption and apparent porosity than the other mixes. Following mechanical testing, the micro and pore structures of all mixes were investigated by performing scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM–EDS), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and nitrogen (N2) adsorption isotherm analysis. In SEM–EDS analysis, a dense and compact microstructure was observed for the BA20 and BA33SF7 mixtures due to the formation of high-density C–S–H and C–H phases. The formation of a large amount of C–S–H phases was observed through FTIR, where a prominent shift in peaks from 955 to 970 cm−1 was observed in the spectra of these mixes. Moreover, in N2 adsorption isotherm analysis, a decrease in the intruded pore volume and an increase in the BET surface area of the paste matrix indicate the densification of the pore structure of these mixes. As observed through TGA, a reduction in the amount of the portlandite phase in these mixes leads to the formation of their more densified micro and pore structures. The current findings indicate that BA (20%) and its blend with SF (40%) represents a potential revenue stream for the development of sustainable and high-performance concretes in the future. [ABSTRACT FROM AUTHOR]

The electron transport layer (ETL) is critical in perovskite solar cells (PSCs) as it controls the optics of the complete solar cell. This study uses an industrially viable RF magnetron sputtering technique to prepare the tungsten oxide (WOx) ETL for PSCs. Necessary morphological and optoelectronic investigations were carried out to ensure the high-quality WOx thin-film. The influence of the deposition power on the ETL thickness and PSC optics were systematically investigated. A three-dimensional (3D) finite-difference time-domain (FDTD) approach analyses the optics and optimization of the complete solar cell. The investigations allow the optimized planar PSC to determine the JSC of > 21 mA/cm2. The optical performance of the planar device is limited due to higher optical losses; hence, the current study proposes a PSC design embedded with Ag nanoparticles. The proposed PSC can improve the JSC by ~ 17% (up to 24.5 mA/cm2) than the planar device owing to improved light trapping, further boosting the PSC's energy conversion efficiency (ECE). A detailed discussion on film realization and solar cell optics is provided. [ABSTRACT FROM AUTHOR]

A new feature called multi-directional local feature (MDLF) is proposed and applied in automatic speaker recognition. In order to extract the MDLF, a windowed speech signal is processed by fast Fourier transform and passed through 24 mel-scaled Filter Bank, followed by log compression. A three-point linear regression is then applied in four different directions, which are horizontal (time axis), vertical (frequency axis), 45 (time-frequency) and 135 (time-frequency). MDLF holds the characteristics of the speaker in time spectrum and results in better performance. In the experiments conducted, a Gaussian mixture model (GMM) with a different number of mixtures is used as the classifier. Experimental results show that the proposed MDLF has better recognition accuracy than the traditional MFCC features. The MDLF achieves excellent results both in text-dependent and text-independent speaker recognition, and in Arabic and English speech. The proposed technique is also language independent. [ABSTRACT FROM AUTHOR]

Outlier detection is critical for many applications such as healthcare, health insurance, medical diagnosis, predictive analytics, pattern recognition, intrusion detection, anomaly or defect detection, video surveillance, credit card fraud detection and text mining. Outlier detection techniques could be statistics, distance- or model based. Techniques, which are based on a single method for outlier detection usually have weaknesses and strengths and are mostly unstable. Outlier detection ensembles harness the strengths of individual detectors and result in stable performance. This paper presents a new parameter based growing self-organizing maps ensemble (GSOME) for outlier detection in multivariate patterns. For outlier detection, the proposed GSOME transforms non-linear relationships between high dimensional patterns into a simple 1D geometric relationship. Whatever the pattern dimensionality is, it is mapped to a single point of a line. The dispersion of mapped points will be used to locate the outliers and measure the degree of outlyingness. Several experiments on both real and synthetic data sets show the promising performance of the proposed GSOME. [ABSTRACT FROM AUTHOR]

With the development of modern medical technology, the emerging 5G, tactile Internet, robot, and artificial intelligence technology have enabled the interdisciplinary innovations facilitating the development of the surgical treatment technology, and enhancing the treatment efficiency of various diseases. In the medical field, the introduction of robot technology has contributed to the telesurgery. Moreover, the telesurgery robot allocated with the 5G tactile Internet as infrastructure, and AI technology as core competitiveness can promote the audio, visual and tactile perceptions of a doctor during the surgery process and solve the problems of resource scheduling; accordingly, it has become the research hotspot. Therefore, this paper introduces a telesurgery robot based on the 5G tactile Internet and artificial intelligence technology. The architecture, composition, characteristics, and advantages of telesurgery are explained in detail from two aspects, the intelligent tactile feedback, and human-machine interaction data. On this basis, a human-machine interaction optimization scheme during the telesurgery process is presented from four aspects, i.e., Edge-Cloud Integration, network slice, and intelligent edge-cloud. Finally, this paper discusses the open issues of the presented telesurgery system regarding the ultra-high reliability, AI-enabled surgery robot, communication, and security, to provide the reference for the promotion of the telesurgery robot performance. [ABSTRACT FROM AUTHOR]

Voice disorders are associated with irregular vibrations of vocal folds. Based on the source filter theory of speech production, these irregular vibrations can be detected in a non-invasive way by analyzing the speech signal. In this paper we present a multiband approach for the detection of voice disorders given that the voice source generally interacts with the vocal tract in a non-linear way. In normal phonation, and assuming sustained phonation of a vowel, the lower frequencies of speech are heavily source dependent due to the low frequency glottal formant, while the higher frequencies are less dependent on the source signal. During abnormal phonation, this is still a valid, but turbulent noise of source, because of the irregular vibration, affects also higher frequencies. Motivated by such a model, we suggest a multiband approach based on a three-level discrete wavelet transformation (DWT) and in each band the fractal dimension (FD) of the estimated power spectrum is estimated. The experiments suggest that frequency band 1-1562 Hz, lower frequencies after level 3, exhibits a significant difference in the spectrum of a normal and pathological subject. With this band, a detection rate of 91.28 % is obtained with one feature, and the obtained result is higher than all other frequency bands. Moreover, an accuracy of 92.45 % and an area under receiver operating characteristic curve (AUC) of 95.06 % is acquired when the FD of all levels is fused. Likewise, when the FD of all levels is combined with 22 Multi-Dimensional Voice Program (MDVP) parameters, an improvement of 2.26 % in accuracy and 1.45 % in AUC is observed. [ABSTRACT FROM AUTHOR]

Copy-move image forgery detection has recently become a very active research topic in blind image forensics. In copy-move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy-move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy-move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using 'dense' MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy-move image forgery with higher accuracy, especially when the size of the duplicated region is small. [ABSTRACT FROM AUTHOR]

Gastrointestinal nematode (GIN) infections are a common constraint to small ruminant industry throughout the world, and among those, haemonchosis has its own significance. Control of GIN primarily relies on the use of anthelmintics, but this approach has become less reliable due to the development of resistance in GINs against commonly used anthelmintics and an increased consumer demand for environmentally friendly animal products. These issues have stimulated investigations to find alternative sustainable control strategies, which are less reliant on anthelmintic input. One of such strategies is breeding of small ruminants for their resistance to the GINs. The susceptibility and resistance of animals to GIN infections varies within and between breeds. Various parasitological, biochemical and immunological parameters are employed to evaluate natural resistance status of animals both in natural pasture and artificial infections. The immune mechanisms responsible for resistance are not completely understood, but it has a significant effect in inherited resistance. Relatively resistant or tolerant animals show better local and generalised immune response as compared to susceptible. Immune response against GINs is influenced by many physiological factors. Determination of specific genes linked with host resistance will provide a valuable approach to find out the molecular mechanism of host resistance to GINs. Resistance has been reported to reduce pasture contamination, which in turn reduces re-infection and thus the requirement of the frequent anthelmintic treatments. The efficiency of control can be increased through objective and accurate identification of genetically tolerant individuals by natural and artificial infections with GINs. Complete resistance is the ultimate solution, but this has generally been ignored as a commercial reality. This paper reviews the published reports on natural resistance in small ruminants and discusses the prospects of developing small ruminants, which could be resistant to GINs. [ABSTRACT FROM AUTHOR]

total of 800 goats of various breeds, age, and sex were randomly selected from Muzaffargarh (M. garh) and Layyah districts of lower Punjab, Pakistan. The selected goats were visited twice a month to collect information about determinants influencing goat tick infestation prevalence. For acaricidal efficacy, 360 tick-infested adult goats were subjected to an acaricidal treatment and post-treatment quantitative assessment of tick burden. Quantification of adult tick detachment 24 h post-treatment and the duration of treatment efficacy were calculated. Overall prevalence of goat tick infestation in both study districts was 60.1% (481/800). The prevalence was higher in district M. garh than in district Layyah. Tehsil-wise prevalence in district Layyah was highest in tehsil Layyah followed in order by Chaubara and Karor. In district M. garh, highest prevalence was found in tehsil M. garh followed by Kot Addu, Alipur, and Jatoi. Hyalomma a. anatolicum (75.9%; 365/481) and Rhipicephalus sanguineus (24.1%; 116/481) were the predominant species in both the districts. The highest month-wise prevalence was 56.9% and 62.7% in Layyah and M. garh districts, respectively, during July 2008, and the minimum (0%) prevalence was reported in November and December, respectively. Regarding host determinants, female goats were more heavily infested (72.8%) than males (47.5%), and younger animals were (63.5%) more burdened than older ones (56.7%). Teddy goats were the most susceptible breeds followed in order by Beetal, cross-bred, Nachi, and Dera Din Pannah. The preferred sites of attachment were inside and outside of the ear. Both the ivermectin (IVM)- and cypermethrin (CYM)-treated groups resulted in significantly lower ( P < 0.05) tick counts relative to controls on all post-treatment counting days. The lowest tick burden in the IVM-treated group was significantly higher ( P < 0.05) as compared to the CYM-treated group, the latter being close to zero. Hence, the in vivo efficacy trials of injectable IVM vs CYM pour-on revealed better results for the latter. These observations provided the first insights into what determinants impact goat tick infestation, and laid a foundation for planning of future control programs in the lower Punjab, Pakistan. [ABSTRACT FROM AUTHOR]