Design of load-aware resource allocation for heterogeneous fog computing systems.

The execution of delay-aware applications can be effectively handled by various computing paradigms, including the fog computing, edge computing, and cloudlets. Cloud computing offers services in a centralized way through a cloud server. On the contrary, the fog computing paradigm offers services in a dispersed manner providing services and computational facilities near the end devices. Due to the distributed provision of resources by the fog paradigm, this architecture is suitable for large-scale implementation of applications. Furthermore, fog computing offers a reduction in delay and network load as compared to cloud architecture. Resource distribution and load balancing are always important tasks in deploying efficient systems. In this research, we have proposed heuristic-based approach that achieves a reduction in network consumption and delays by efficiently utilizing fog resources according to the load generated by the clusters of edge nodes. The proposed algorithm considers the magnitude of data produced at the edge clusters while allocating the fog resources. The results of the evaluations performed on different scales confirm the efficacy of the proposed approach in achieving optimal performance.

A systematic review of robotic efficacy in coral reef monitoring techniques.

Coral reefs are home to a variety of species, and their preservation is a popular study area; however, monitoring them is a significant challenge, for which the use of robots offers a promising answer. The purpose of this study is to analyze the current techniques and tools employed in coral reef monitoring, with a focus on the role of robotics and its potential in transforming this sector. Using a systematic review methodology examining peer-reviewed literature across engineering and earth sciences from the Scopus database focusing on "robotics" and "coral reef" keywords, the article is divided into three sections: coral reef monitoring, robots in coral reef monitoring, and case studies. The initial findings indicated a variety of monitoring strategies, each with its own advantages and disadvantages. Case studies have also highlighted the global application of robotics in monitoring, emphasizing the challenges and opportunities unique to each context. Robotic interventions driven by artificial intelligence and machine learning have led to a new era in coral reef monitoring. Such developments not only improve monitoring but also support the conservation and restoration of these vulnerable ecosystems. Further research is required, particularly on robotic systems for monitoring coral nurseries and maximizing coral health in both indoor and open-sea settings.

An adapted model predictive control MPPT for validation of optimum GMPP tracking under partial shading conditions.

The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Methamphetamine-induced dental caries: a review of the literature.

Clinical picture of patients taking methamphetamine for long duration includes rampant caries of the smooth surfaces of the whole dentition. The increasing use of methamphetamine in homosexuals is leading to the spread of HIV (human immunodeficiency virus). Easy availability and rapidly spreading nature of this drug (methamphetamine) results in worldwide increase of patients with medical and dental problems. Its effect on human dentition is highly damaging as patients with a beautiful smile begin to present a horrible picture of black, broken, and painful teeth within one year of methamphetamine use. Restoration of aesthetics and function of these teeth is not an easy task, and usually the first step to deal with this condition is counselling the patient to stop using this drug. Knowledge of methamphetamine-induced undesirable effects on the human body is important for the general dental practitioner as referral to mental health services is necessary in this condition.

Development of neutrosophic cubic hesitant fuzzy exponential aggregation operators with application in environmental protection problems.

The population growth and urbanization has caused an exponential increase in waste material. The proper disposal of waste is a challenging problem nowadays. The proper disposal site selection with typical sets and operators may not yield fruitful results. To handle such problems, the exponential aggregation operators based on neutrosophic cubic hesitant fuzzy sets are proposed. For appropriate decisions in a decision-making problem, it is important to have a handy environment and aggregation operators. Many multi attribute decision making methods often ignore the uncertainty and hence yields the results which are not reliable. The neutrosophic cubic hesitant fuzzy set can efficiently handle the complex information in a decision-making problem, as it combines the advantages of neutrosophic cubic set and hesitant fuzzy set. In this paper first we establish exponential operational laws in neutrosophic cubic hesitant fuzzy sets, in which the exponents are neutrosophic cubic hesitant fuzzy numbers and bases are positive real numbers. In order to use neutrosophic cubic hesitant fuzzy sets in decision making, we are developing exponential aggregation operators and investigate their properties in the current study. In many multi expert decision-making methods there are different decision matrices but same weighting vector for attributes. The results of a multi expert decision-making problem becomes more reliable if every decision expert has its own decision matrix along with his own weighting vector for attributes. In this study, we are developing multi expert decision-making method that uses different weights for an attribute corresponding to different experts. At the end we present two applications of exponential aggregation operators in environmental protection multi attribute decision making problems.

First report of Fusarium equiseti causing leaf spots of Bitter gourd (Momordica charantia) in Pakistan.

Bitter gourd (Momordica charantia L.) is an important vegetable crop of the Cucurbitaceae family widely cultivated in Pakistan and around the world. In October 2020, a nutrition management trial of Bitter gourd cv. Seminis-200) was conducted on an area of 10,860 sq. ft. (99×110 feet) at the Agricultural Research farm of Bahauddin Zakariya University, Multan (30.2601° N, 71.5158° E), Pakistan. Symptoms of large, brown necrotic leaf spots were observed on the leaves of bitter gourd vines. The disease started from the yellowing of leaves within the reticulate venation and turned brown. Irregular brown leaf spots coalesced to form large necrotic areas followed by foliar chlorosis then wilting that occurred very late. There were no crown rot symptoms although there was slight discoloration of roots and when cut longitudinally, browning of tissues was observed. The disease was assessed visually with 37% incidence which resulted in poor quality and yield in terms of reduced size and yellowing of fruit. Infected vines along with the roots were collected for the isolation of pathogen. A total of 34 leaves and 22 root samples were collected from the field for isolation. The leaf, collar and root portions were cut into 0.5 to 1 cm in length and surface disinfected with 1% sodium hypochlorite (NaOCl) for 2-3 minutes followed by washing twice with autoclaved distilled water and after drying, placed on potato dextrose agar (PDA) medium, and incubated at 25±2 °C for one week. The fungal colonies of fluffy white growth with light orange pigment were isolated. For morphological characterization, a total of 4 pure cultures were isolated from leaves, collar region and root by single spore technique on carnation leaf agar (CLA) medium after 15 days of incubation at 25±2℃. Curved and thick-walled macroconidia with elongated or pointed apical characteristic foot-shaped basal cells were produced in sporodochia. Macroconidia with 5-7 septa measured 22.50-41.80 µm × 2.90-4.20 µm (n = 60). Thick, brown with roughened walls and subglobose ellipsoidal chlamydospores were observed in clumps or chains with the dimension of 5.8 to 10.8 µm (n = 20). On morphological characteristics, the fungus was identified as Fusarium equiseti (Corda) Sacc. according to Leslie and Summerell (2006). Two single spore isolates were used for molecular identification by amplifying ribosomal DNA of the internal transcribed spacer (ITS) region with ITS1/ITS4 primers (White et al. 1990) and for ß-tubulin gene region, primers T1/Bt-2b (O'Donnell and Cigelnik, 1997) were used. The obtained sequences were deposited in GenBank with accession numbers MW880179 and MW880198 from the ITS region and BLAST search in GenBank showed 100 and 98.11% alignment with previously published sequences of F. equiseti with accessions OM992323.1and MT558569.1 respectively. Accession number OM867571from the ß-tubulin region showed 100% sequence similarity with F. equiseti with accession MN653163.1. For pathogenicity, macroconidia from 2-week-old cultures on CLA medium were harvested to prepare spore suspension (1 × 106 conidia/ml). Koch's postulates were confirmed on nine bitter gourd plants (cv. Seminis-200) by applying spore suspension of fungal inoculum at 3-4 leaf stage separately on leaves by automizer, on collar region after making incision spore suspension was applied and in the root zone, 20ml spore suspension was added whereas distilled water was used as a control with three replications. Plants were kept under controlled conditions in the greenhouse with 65% to 75% humidity and the temperature was maintained at 32±2 °C for one week. After 7-8 days, inoculated plants began to exhibit symptoms of brown, necrotic leaf spots on the leaves of bitter gourd vines followed by yellowing of leaves that eventually turned brown. Roots showed slight discoloration and browning of vascular bundles and finally, the plants wilted after four weeks. while control plants remained symptomless. The symptoms resembled those noticed in the field. The fungus was re-isolated from leaves, collar region and roots, followed by morphological identification, and finally confirmed as F. equiseti. To the best of our knowledge, this is the first report of a leaf spot caused by F. equiseti in a bitter gourd from Pakistan. If the disease is not managed properly, it may cause a drastic effect on yield under favorable environmental conditions. The pathogen may also damage other cucurbitaceous crops cultivated in the area.

Implementation of an Intelligent Exam Supervision System Using Deep Learning Algorithms.

Examination cheating activities like whispering, head movements, hand movements, or hand contact are extensively involved, and the rectitude and worthiness of fair and unbiased examination are prohibited by such cheating activities. The aim of this research is to develop a model to supervise or control unethical activities in real-time examinations. Exam supervision is fallible due to limited human abilities and capacity to handle students in examination centers, and these errors can be reduced with the help of the Automatic Invigilation System. This work presents an automated system for exams invigilation using deep learning approaches i.e., Faster Regional Convolution Neural Network (RCNN). Faster RCNN is an object detection algorithm that is implemented to detect the suspicious activities of students during examinations based on their head movements, and for student identification, MTCNN (Multi-task Cascaded Convolutional Neural Networks) is used for face detection and recognition. The training accuracy of the proposed model is 99.5% and the testing accuracy is 98.5%. The model is fully efficient in detecting and monitoring more than 100 students in one frame during examinations. Different real-time scenarios are considered to evaluate the performance of the Automatic Invigilation System. The proposed invigilation model can be implemented in colleges, universities, and schools to detect and monitor student suspicious activities. Hopefully, through the implementation of the proposed invigilation system, we can prevent and solve the problem of cheating because it is unethical.

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges.

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

Identification and primary distribution of Citrus viroid V in citrus in Punjab, Pakistan.

BACKGROUND: Citrus plants are prone to infection by different viroids which deteriorate their vigor and production. Citrus viroid V (CVd-V) is among the six citrus viroids, belongs to genus Apscaviroid (family Pospiviroidae) which induces symptoms of mild necrotic lesions on branches and cracks on trunk portion. METHODS AND RESULTS: A survey was conducted to evaluate the prevalence of CVd-V in core and non-core citrus cultivated areas of Punjab, Pakistan. A total of 154 samples from different citrus cultivars were tested for CVd-V infection by RT-PCR. The results revealed 66.66% disease incidence of CVd-V. Citrus cultivars Palestinia Sweet lime, Roy Ruby, Olinda Valencia, Kaghzi lime, and Dancy were identified as new citrus hosts of CVd-V for the first time from Pakistan. The viroid infection was confirmed by biological indexing on indicator host Etrog citron. The reported primers used for the detection of CVd-V did not amplify, rather showed non-specific amplification, which led to the designing of new primers. Whereas, new back-to-back designed primers (CVd-V AF1/CVd-V AR1) detected CVd-V successfully and obtained an expected amplified product of CVd-V with 294 bp. Sequencing analysis confirmed the new host of CVd-V showing 98-100% nucleotide sequence homology with those reported previously from other countries while 100% sequence homology to the isolates reported from Pakistan. Based on phylogenetic analysis using all CVd-V sequences in GenBank, two main CVd-V groups (I and II) were identified, and newly identified isolates during this study fall in the group I. CONCLUSION: The study revealed that there are some changes in the nucleotide sequences of CVd-V which made difficult for their detection using reported primers. All isolates of Pakistan showed high sequence homology with other isolates of CVd-V from Iran and USA whereas; the isolates from China, Japan, Tunisia, and Africa are distantly related. It is evident that CVd-V is spreading in all citrus cultivars in Pakistan.

Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.

Artificial intelligence is serving as an impetus in digital health, clinical support, and health informatics for an informed patient's outcome. Previous studies only consider classification accuracies of cardiotocographic (CTG) datasets and disregard computational time, which is a relevant parameter in a clinical environment. This paper proposes a modified deep neural algorithm to classify untapped pathological and suspicious CTG recordings with the desired time complexity. In our newly developed classification algorithm, AlexNet architecture is merged with support vector machines (SVMs) at the fully connected layers to reduce time complexity. We used an open-source UCI (Machine Learning Repository) dataset of cardiotocographic (CTG) recordings. We divided 2126 CTG recordings into 3 classes (Normal, Pathological, and Suspected), including 23 attributes that were dynamically programmed and fed to our algorithm. We employed a deep transfer learning (TL) mechanism to transfer prelearned features to our model. To reduce time complexity, we implemented a strategy wherein layers in the convolutional base were partially trained to leave others in the frozen states. We used an ADAM optimizer for the optimization of hyperparameters. The presented algorithm also outperforms the leading architectures (RCNNs, ResNet, DenseNet, and GoogleNet) with respect to real-time accuracies, sensitivities, and specificities of 99.72%, 96.67%, and 99.6%, respectively, making it a viable candidate for clinical settings after real-time validation.

Early-Stage Alzheimer's Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains.

Alzheimer's Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

Fabrication of Recycled Polycarbonate Fibre for Thermal Signature Reduction in Camouflage Textiles.

Thermal signature reduction in camouflage textiles is a vital requirement to protect soldiers from detection by thermal imaging equipment in low-light conditions. Thermal signature reduction can be achieved by decreasing the surface temperature of the subject by using a low thermally conductive material, such as polycarbonate, which contains bisphenol A. Polycarbonate is a hard type of plastic that generally ends up in dumps and landfills. Accordingly, there is a large amount of polycarbonate waste that needs to be managed to reduce its drawbacks to the environment. Polycarbonate waste has great potential to be used as a material for recycled fibre by the melt spinning method. In this research, polycarbonate roofing-sheet waste was extruded using a 2 mm diameter of spinnerette and a 14 mm barrel diameter in a 265 °C temperature process by using a lab-scale melt spinning machine at various plunger and take-up speeds. The fibres were then inserted into 1 × 1 rib-stitch knitted fabric made by Nm 15 polyacrylic commercial yarns, which were manufactured by a flat knitting machine. The results showed that applying recycled polycarbonate fibre as a fibre insertion in polyacrylic knitted fabric reduced the emitted infrared and thermal signature of the fabric.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier.

Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger-knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

An Easy-to-Fabricate Microfluidic Shallow Trench Induced Three-Dimensional Cell Culturing and Imaging (STICI3D) Platform.

Compared to the established monolayer approach of two-dimensional cell cultures, three-dimensional (3D) cultures more closely resemble in vivo models; that is, the cells interact and form clusters mimicking their organization in native tissue. Therefore, the cellular microenvironment of these 3D cultures proves to be more clinically relevant. In this study, we present a novel easy-to-fabricate microfluidic shallow trench induced 3D cell culturing and imaging (STICI3D) platform, suitable for rapid fabrication as well as mass manufacturing. Our design consists of a shallow trench, within which various hydrogels can be formed in situ via capillary action, between and fully in contact with two side channels that allow cell seeding and media replenishment, as well as forming concentration gradients of various molecules. Compared to a micropillar-based burst valve design, which requires sophisticated microfabrication facilities, our capillary-based STICI3D can be fabricated using molds prepared with simple adhesive tapes and razors alone. The simple design supports the easy applicability of mass-production methods such as hot embossing and injection molding as well. To optimize the STICI3D design, we investigated the effect of individual design parameters such as corner radii, trench height, and surface wettability under various inlet pressures on the confinement of a hydrogel solution within the shallow trench using Computational Fluid Dynamics simulations supported with experimental validation. We identified ideal design values that improved the robustness of hydrogel confinement and reduced the effect of end-user dependent factors such as hydrogel solution loading pressure. Finally, we demonstrated cultures of human mesenchymal stem cells and human umbilical cord endothelial cells in the STICI3D to show that it supports 3D cell cultures and enables precise control of cellular microenvironment and real-time microscopic imaging. The easy-to-fabricate and highly adaptable nature of the STICI3D platform makes it suitable for researchers interested in fabricating custom polydimethylsiloxane devices as well as those who are in need of ready-to-use plastic platforms. As such, STICI3Ds can be used in imaging cell-cell interactions, angiogenesis, semiquantitative analysis of drug response in cells, and measurement of transport through cell sheet barriers.

Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions.

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework.

Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI systems with small and ample samples of motor and mental imagery EEG training data. The framework is explored by investigating the implications of different limiting factors, such as learning rates and optimizers, processed versus unprocessed scalograms, and features derived from untuned pretrained models in small, medium, and large pretrained CNN models. The experiments were performed on three public datasets obtained from BCI Competition III. The datasets were denoised with multiscale principal component analysis, and time-frequency scalograms were obtained by employing a continuous wavelet transform. The scalograms were fed into several variants of ten pretrained models for feature extraction and identification of different EEG tasks. The experimental results showed that ShuffleNet yielded the maximum average classification accuracy of 99.52% using an RMSProp optimizer with a learning rate of 0.000 1. It was observed that low learning rates converge to more optimal performances compared to high learning rates. Moreover, noisy scalograms and features extracted from untuned networks resulted in slightly lower performance than denoised scalograms and tuned networks, respectively. The overall results suggest that pretrained models are robust when identifying EEG signals because of their ability to preserve the time-frequency structure of EEG signals and promising classification outcomes.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

A novel computer-aided diagnosis framework for EEG-based identification of neural diseases.

Recent advances in electroencephalogram (EEG) signal classification have primarily focused on domain-specific approaches, which impede algorithm cross-discipline capability. This study introduces a new computer-aided diagnosis (CAD) system for the classification of two distinct EEG domains under a unified sequential framework. The key motivation to consider two neural diseases by one framework is to develop a unified algorithm for EEG classification. The main contributions of this study are five-fold. First, EEG signals are decomposed into 10 intrinsic mode functions (IMFs) with the help of empirical wavelet transform. Second, a novel two-dimensional (2D) modeling of IMFs is plotted to visualize the complexity of EEG signals. Third, several new geometrical features are extracted to analyze the dynamic and chaotic essence. Fourth, significant features are selected by binary particle swarm optimization algorithm (B-PSO). Fifth, selected features are fed to the k-nearest neighbor classifier for EEG signal classification purposes. All the experiments are executed on one depression and two epileptic EEG datasets in a leave one out cross-validation strategy. The proposed CAD system provides an average classification accuracy of 93.35% in depression detection, 99.33% for regular against ictal, and 97.33% for interictal versus ictal respectively. The overall empirical analysis authenticates that the proposed CAD outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus, can be endorsed as an effective automated neural rehabilitation system.