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Antioxidant proteins have been discovered closely associated with disease control due to its capability to eradicate excess free radicals. The accurate identification of antioxidant proteins is on the upsurge owing to their therapeutic significance. However, observing the rapid increases of this toxic disease in the human body, several machine learning algorithms have been applied and performed inadequately to identify antioxidant proteins. Therefore, measuring the effectiveness of antioxidant proteins on the human body, a reliable intelligent model is indispensable for the researchers. In this study, primary protein sequences are formulated using evolutionary and sequence-based numerical descriptors. Whereas, evolutionary features are collected using a bigram Position-specific scoring matrix, besides, K-space amino acid pair (KSAAP) and dipeptide composition are utilized to extract sequential information. Furthermore, in order to reduce the computational time and to eradicate irreverent and noisy features, the Sequential forward selection and Support vector machine (SFS-SVM) based ensemble approach is applied to select optimal features. At last, several distinct nature classification learning methods are applied to choose a suitable operational engine for our model. After evaluating the empirical results, SVM using optimal features achieved an accuracy of 97.54%, 93.71% using the training and independent dataset, respectively. It was found that our proposed model outperformed and reported the highest performance than the existing computational models. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. The source code and all datasets are publicly available at https://github.com/salman-khan-mrd/Antioxident_proteins .

Wireless communication network is the backbone of each and every organization of today modern globe. The rapid emergence of smart devices, smart homes, internet of things and clean energy become key sources in today society. In result the data traffic, emerging of new service and application are increase every year. Hence, there is a growing demand for 5G technology in all above mentioned fields. Therefore, better waveform types for 5G technology is needed, which has ability of high spectral efficiency, lower latency and less complexity. It is predicted that the data traffic will arise 1000x in near future. In this work filtered orthogonal frequency division multiplexing (F-OFDM) and universal filtered orthogonal frequency division multiplexing (UF-OFDM) techniques will be discussed to degrade key issues such as computational complexity, peak average power ratio (PAPR), lower spectral efficiency, and higher latency. The suggested work will clearly overcome the drawbacks faced by previous work like limited capacity and complexity. We also analyze algorithms and simplification for F-OFDM and UF-OFDM waveforms from an implementation perspective towards low complexity and efficient transceiver design. Moreover, F-OFDM and UF-OFDM will be mainly based on multicarrier modulation and will be enabled to reduce to out-of-band power leakage (OOBPL) of OFDM.

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to the body’s dysregulated response to infection. It is difficult to identify sepsis in its early stages, this delay in identification has a dramatic effect on mortality rate. Developing prognostic tools for sepsis prediction has been the focus of various studies over previous decades. However, most of these studies relied on tracking a limited number of features, as such, these approaches may not predict sepsis sufficiently accurately in many cases. Therefore, in this study, we concentrate on building a more accurate and medically relevant predictive model for identifying sepsis. First, both NSGA-II (a multi-objective genetic algorithm optimization approach) and artificial neural networks are used concurrently to extract the optimal feature subset from patient data. In the next stage, a deep learning model is built based on the selected optimal feature set. The proposed model has two layers. The first is a deep learning classification model used to predict sepsis. This is a stacking ensemble of neural network models that predicts which patients will develop sepsis. For patients who were predicted to have sepsis, data from their first six hours after admission to the ICU are retrieved, this data is then used for further model optimization. Optimization based on this small, recent timeframe leads to an increase in the effectiveness of our classification model compared to other models from previous works. In the second layer of our model, a multitask regression deep learning model is used to identify the onset time of sepsis and the blood pressure at that time in patients that were predicted to have sepsis by the first layer. Our study was performed using the medical information from the intensive care MIMIC III real-world dataset. The proposed classification model achieved 0.913, 0.921, 0.832, 0.906 for accuracy, specificity, sensitivity, and AUC, respectively. In addition, the multitask regression model obtained an RMSE of 10.26 and 9.22 for predicting the onset time of sepsis and the blood pressure at that time, respectively. There are no other studies in the literature that can accurately predict the status of sepsis in terms of its onset time and predict medically verifiable quantities like blood pressure to build confidence in the onset time prediction. The proposed model is medically intuitive and achieves superior performance when compared to all other current state-of-the-art approaches.

Purpose Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session, therefore, they need to be addressed in real time to keep the learner engaged in the learning process. Similarly, long-term learners’ motivation plays an equally important role to retain the learner in the long run and minimize the risk of dropout. Therefore, the purpose of this study is to incorporate a comprehensive learner motivation model that is based on short-term and long-term aspects of the learners' motivation. This approach enables Web-based educational systems to identify the real-time motivational state of the learner and provide personalized interventions to keep the learners engaged in learning process. Design/methodology/approach Recent research regarding personalized Web-based educational systems demonstrates learner’s motivation to be an essential component of the learning model. This is because of the fact that low motivation results in either students’ less engagement or complete drop out from the learning activities. A learner motivation model is considered to be a set of perceptions and beliefs that the system has developed about a learner. This includes both short-term and long-term motivations of leaners. Findings This study proposed a framework of a domain independent learners’ motivation model based on firm educational theories. The proposed framework consists of two modules. The primary module deals with real-time identification of motivation and logging off activities such as login, forum participation and adherence to assessment deadline. Secondary module maintains the profile of leaners associated with both short-term and long-term motivation. A study was conducted to verify the impact of learners’ motivation model and personalized interventional strategies based on proposed model, using Systematical Information Education Method assessment standards. The results show an increase in motivational index and the characteristics associated with motivation during the conducted study. Originality/value Motivational diagnosis is important for both traditional classrooms and Web-based education systems. It is one of the major elements that contribute in the success of the learning process. However, dropout rate among online students is very high, which leads to incorporate motivational elements in more personalized way because motivated students will retain the course until they successfully complete it. Hence, identifying learner’s motivation, updating learners’ motivation model based on this identification and providing personalized interventions are the key for the success of Web-based educational systems.

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.

Contemporary Smart Power Systems (SPNs) depend on Cyber-Physical Systems (CPSs) to connect physical devices and control tools. Developing a robust privacy-conserving intrusion detection method involves network and physical data regarding the setups, such as Supervisory Control and Data Acquisition (SCADA), for defending real data and recognizing cyber-attacks. A key issue in the implementation of SPNs is the security against cyber-attacks, targeting to interrupt SCADA operations and violate data privacy over the usage of penetration and data poisoning attacks. In this paper, a privacy-conserving framework, so-called PC-IDS, is proposed for realizing the privacy and safety features of SPNs through hybrid machine learning approach. The framework includes two key components. Primarily, a data pre-processing component is proposed for cleaning and transforming actual data into a different layout that accomplishes the aim of privacy conservation. Then, an intrusion detection component is proposed using a particle swarm optimization-based probabilistic neural network for the identification and recognition of malicious events. The performance of PC-IDS framework is evaluated by means of two commonly available datasets, i.e. the Power System and UNSW-NB15 datasets. The experimental outcomes highlight that the framework can proficiently protect data of SPNs and determine anomalous behaviours compared to numerous recent compelling state-of-the-art methods with respect to false positive rate (FPR), detection rate (DR) and computational processing time (CPT) by achieving 96.03% of DR, 0.18% FPR for Power System dataset and 95.91% of DR, 0.14% FPR for UNSW-NB15 dataset.

Alzheimer’s disease (AD) is a severe neurodegenerative disease. The identification of patients at high risk of conversion from mild cognitive impairment to AD via earlier close monitoring, targeted investigations, and appropriate management is crucial. Recently, several machine learning (ML) algorithms have been used for AD progression detection. Most of these studies only utilized neuroimaging data from baseline visits. However, AD is a complex chronic disease, and usually, a medical expert will analyze the patient’s whole history when making a progression diagnosis. Furthermore, neuroimaging data are always either limited or not available, especially in developing countries, due to their cost. In this paper, we compare the performance of five widely used ML algorithms, namely, the support vector machine, random forest, k-nearest neighbor, logistic regression, and decision tree to predict AD progression with a prediction horizon of 2.5 years. We use 1029 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI) database. In contrast to previous literature, our models are optimized using a collection of cost-effective time-series features including patient’s comorbidities, cognitive scores, medication history, and demographics. Medication and comorbidity text data are semantically prepared. Drug terms are collected and cleaned before encoding using the therapeutic chemical classification (ATC) ontology, and then semantically aggregated to the appropriate level of granularity using ATC to ensure a less sparse dataset. Our experiments assert that the early fusion of comorbidity and medication features with other features reveals significant predictive power with all models. The random forest model achieves the most accurate performance compared to other models. This study is the first of its kind to investigate the role of such multimodal time-series data on AD prediction.

The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android malware. Android malware applications from the DREBIN dataset were converted into grayscale images. In the first phase of the experiment, the proposed framework transforms Android malware into fifteen different image sections and identifies malware files by exploiting handcrafted features associated with Android malware images. The algorithms such as Gray Level Co-occurrence Matrix-based (GLCM), Global Image deScripTors (GIST), and Local Binary Pattern (LBP) are used to extract the handcrafted features from the image sections. The extracted features were further classified using machine learning algorithms like K-Nearest Neighbors, Support Vector Machines, and Random Forests. In the second phase of the experiment, handcrafted features were fused with CNN features to form the feature fusion strategy. The classification performance was evaluated against every malware image file section. The results obtained using the Feature Fusion strategy are compared with handcrafted features results. The experiment results conclude to the fact that Feature Fusion-SVM model is most suited for the identification and classification of Android malware using the certificate and Android Manifest (CR + AM) malware images. It attained an high accuracy of 93.24%.

Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protege Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.

A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail.

Increased demand of multimedia data over heterogeneous networks has led to the requirement for increased compression and suitable security. Both of these demands require heavy computation, and are contradictory to each other, as encryption may severely affect the compression efficiency. On the other hand, handheld devices have limited energy and computational resources, making compression friendly encryption a challenging issue. One of the solutions is to encrypt a subset of data based on perceptual importance. But, that too may not be compression efficient, format compliant and secure. To address the security issue in such applications the substitution box (S-box), which is one of the most important and the only non-linear operation of the block ciphers is redesigned using chaotic equations. These equations have the characteristics that easily meet the requirements of the block ciphers. In this research work, the S-box based on the chaotic equation was designed and tested for security strength and then used for selective encryption of the multimedia (image) data. To increase the security and generate a key dependant S-box, Mackey Glass equation is used due to its inherent properties like sensitivity, pseudo random characteristics, non-uniform behaviour, ergodicity, and high confusion and diffusion. The security of the proposed S-box was tested in terms of non-linearity, Bit Independence and Strict Avalanche Criteria etc. This S-box is then used to selectively encrypt a subset of image data during the process of compression, giving an encryption-compression strategy. The subset of image data was selected such that the statistical and structural dependencies were not violated due to encryption. This resulted in compression efficient and format friendly encryption. In this regard the quantization table is selected for encryption along with a subset of bits that have already encoded and their statistical dependencies are exploited for compression. This approach not only reduced the computational burden due to the selective nature of encryption but also kept the security at the required level due to introduction of key dependent S-Box. The results of encrypted images were compared with that of the AES in terms of compression ratio, correlation and Peak Signal to Noise Ratio (PSNR), giving better results for the proposed algorithm.

The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.

Optical communication systems (OCSs) mainly represent the backbone of modern long-haul communication networks because of low loss transmission over long distances and ultra-high capacity. However high data-rate transmission through optical fiber suffers from deterioration due to nonlinear impairments, such as four-wave mixing (FWM) in particular. At high launch power levels, which are required for the long-haul transmission over hundreds of km, these nonlinear effects become more severe which imposes a challenge to achieve satisfactory transmission performance. In this paper, a theoretical model for the FWM effects and its mitigation is presented and validated through simulation results. Moreover, two other nonlinear effects, polarization mode dispersion and nonlinear dispersion variations are also investigated for various values of launch power level. The transmission performance of the proposed OCS model is evaluated on the basis of bit error rate, optical signal-to-noise ratio and quality factor using different transmission channel parameters such as effective area, nonlinear refractive index, nonlinear dispersion, and linear dispersion.

Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.

In intelligent transportation system, smart vehicles are equipped with a variety of sensing devices those offer various multimedia applications and services related to smart driving assistance, weather forecasting, traffic congestion information, road safety alarms, and many entertainment and comfort-related applications. These smart vehicles produce a massive amount of multimedia related data that required fast and real-time processing which cannot be fully handled by the standalone onboard computing devices due to their limited computational power and storage capacities. Therefore, handling such multimedia applications and services demanded changes in the underlaying networking and computing models. Recently, the integration of vehicles with cloud computing is emerged as a challenging computing paradigm. However, there are certain challenges related to multimedia contents processing, (i.e., resource cost, fast service response time, and quality of experience) that severely affect the performance of vehicular communication. Thus, in this paper, we propose an efficient resource allocation and computation framework for vehicular multimedia cloud computing to overcome the aforementioned challenges. The performance of the proposed scheme is evaluated in terms of quality of experience, service response time, and resource cost by using the Cloudsim simulator.

The load pressure on electrical power system is increased during last decade. The installation of new power generators (PGs) take huge time and cost. Therefore, to manage current power demands, the solar plants are considered a fruitful solution. However, critical caring and balance output power in solar plants are the highlighted issues. Which needs a proper procedure in order to minimize balance output power and caring issues in solar plants. This paper investigates artificial neural network (ANN) and hybrid boost converter (HBC) based MPPT for improving the output power of solar plants. The proposed model is analyzed in two steps, the offline step and the online step. Where the offline status is used for training various terms of ANNs in terms of structure and algorithm while in the online step, the online procedure is applied with optimum ANN for maximum power point tracking (MPPT) using traditional converter and hybrid converter in solar plants. Moreover, a detail analytical framework is studied for both proposed steps. The mathematical and simulation approaches show that the presented model efficiently regulate the output of solar plants. This technique is applicable for current installed solar plants which reduces the cost per generation.

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.

In extremely cold environments, living organisms like plants, animals, fishes, and microbes can die due to the intracellular ice formation in their bodies. To sustain life in such cold environments, some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry. Different AFPs exhibit high heterogeneity in their structures and sequences. Keeping the significance of AFPs, several machine-learning-based models have been developed by scientists for the prediction of AFPs. However, due to the complex and diverse nature of AFPs, the prediction performance of the existing methods is limited. Therefore, it is highly indispensable for researchers to develop a reliable computational model that can accurately predict AFPs. In this connection, this study presents a novel predictor for AFPs, named AFP-CMBPred. The sequences of AFPs are formulated via four different feature representation methods, such as Amphiphilic pseudo amino acid composition (Amp-PseAAC), Dipeptide Deviation from Expected Mean (DDE), Multi-Blocks Position Specific Scoring Matrix (MB-PSSM), and Consensus Sequence-based on Multi-Blocks Position Specific Scoring Matrix (CS-MB-PSSM) to collect local and global descriptors. In the next step, the extracted feature vectors are evaluated via Support Vector Machine (SVM) and Random Forest (RF) based classification learners. The prediction performance of both classifiers is further assessed using three validation methods i.e., jackknife test, 10-fold cross-validation test, and independent test. After examining the prediction rates of all validation tests, it was found that our proposed model achieved the higher prediction accuracies of ∼2.65%, ∼2.84%, and ∼3.37% using jackknife, K-fold, and independent test, respectively. The experimental outcomes validate that our proposed “AFP-CMBPred” predictor secured the highest prediction results than the existing models for the identification of AFPs. It is further anticipated that our proposed AFP-CMBPred model will be considered a valuable tool in the research academia and drug development.

Requirement gathering is considered as one of the most important activity is software development lifecycle (SDLC). If requirements are not gathered properly then there is huge expectation gap between what the customer desire and what the developer built. Currently, there are various techniques used to elicit software requirements such as interview, questionaries, and prototyping etc. However, identify the common risks arise and adopting the best methodology in requirement gathering is still a topic of debate. In this paper, we performed a survey-based study featuring 21 participants aims to identify various aspects in requirement gathering process such as best approaches, techniques, problems occurred, risks involved, and benefits. The participants involved in this study are from diverse industrial roles (i.e., business analyst, software developer and technical lead etc.) and are working in requirement gathering process of real software projects. The main findings of this survey study shows that many participants (16/21, 76.2%) use interview approach followed by prototyping approach (13/21, 61.9%) to elicit the requirements. Moreover, related to potential risks, (9/21, 42.9%) participants responds insufficient user involvement and ambiguous requirements as most common risks experienced by business analyst, whereas (1/21, 4.8%) participant select gold plating as less frequent risk arise during requirement gathering.

Enhancers are short DNA cis-elements that can be bound by proteins (activators) to increase the possibility that transcription of a particular gene will occur. The Enhancers perform a significant role in the formation of proteins and regulating the gene transcription process. Human diseases such as cancer, inflammatory bowel disease, Parkinson’s, addiction, and schizophrenia are due to genetic variation in enhancers. In the current study, we have made an effort by building, a more robust and novel computational a bi-layered model. The representative feature vector was constructed over a linear combination of six features. The optimum Hybrid feature vector was obtained via the Novel Cascade Multi-Level Subset Feature selection (CM-SFS) algorithm. The first layer predicts the enhancer, and the secondary layer carries the prediction of their subtypes. The baseline model obtained 87.88% of accuracy, 95.29% of sensitivity, 80.47% of specificity, 0.766 of MCC, and 0.9603 of a roc value on Layer-1. Similarly, the model obtained 68.24%, 65.54%, 70.95%, 0.3654, and 0.7568 as an Accuracy, sensitivity, specificity, MCC, and ROC values on layer-2 respectively. Over an independent dataset on layer-1, the piEnPred secured 80.4% accuracy, 82.5% of sensitivity, 78.4% of specificity, and 0.6099 as MCC, respectively. Subsequently, the proposed predictor obtained 72.5% of accuracy, 70.0% of sensitivity, 75% of specificity, and 0.4506 of MCC on layer-2, respectively. The proposed method remarkably performed in contrast to other state-of-the-art predictors. For the convenience of most experimental scientists, a user-friendly and publicly freely accessible web server @/bienhancer dot pythonanywhere dot com/has been developed.

Tuberculosis (TB) is a worldwide illness caused by the bacteria Mycobacterium tuberculosis. Owing to the high prevalence of multidrug-resistant tuberculosis, numerous traditional strategies for developing novel alternative therapies have been presented. The effectiveness and dependability of these procedures are not always consistent. Peptide-based therapy has recently been regarded as a preferable alternative due to its excellent selectivity in targeting specific cells without affecting the normal cells. However, due to the rapid growth of the peptide samples, predicting TB accurately has become a challenging task. To effectively identify antitubercular peptides, an intelligent and reliable prediction model is indispensable. An ensemble learning approach was used in this study to improve expected results by compensating for the shortcomings of individual classification algorithms. Initially, three distinct representation approaches were used to formulate the training samples: k-space amino acid composition, composite physiochemical properties, and one-hot encoding. The feature vectors of the applied feature extraction methods are then combined to generate a heterogeneous vector. Finally, utilizing individual and heterogeneous vectors, five distinct nature classification models were used to evaluate prediction rates. In addition, a genetic algorithm-based ensemble model was used to improve the suggested model's prediction and training capabilities. Using Training and independent datasets, the proposed ensemble model achieved an accuracy of 94.47% and 92.68%, respectively. It was observed that our proposed "iAtbP-Hyb-EnC" model outperformed and reported ~10% highest training accuracy than existing predictors. The "iAtbP-Hyb-EnC" model is suggested to be a reliable tool for scientists and might play a valuable role in academic research and drug discovery. The source code and all datasets are publicly available at https://github.com/Farman335/iAtbP-Hyb-EnC.

Optical communication systems represent the backbone of modern long-haul communication networks. Nonlinear impairments such as the Kerr effect pose threat and deteriorate transmission in long-haul optical communication systems. At high launch powers, nonlinear effects become more important and their compensation is entailed to improve the transmission performance. In this paper, one of the major nonlinear effects of four waves mixing (FWM) is addressed using a theoretical model and validated through simulation in Optisystem. Two mechanisms with and without dispersion variation are investigated. The impact of FWM is investigated in terms of various values of launch power. The transmission performances of the proposed model are evaluated on the basis of bit error rate, optical signal to noise ratio, and quality factor using key parameters of effective area, nonlinear refractive index, nonlinear dispersion, and linear dispersion.

Social networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent observations to support intelligent transportation systems (ITSs) in examining traffic control and management systems. However, sentiment analysis faces technical challenges: extracting meaningful information from social network platforms, and the transformation of extracted data into valuable information. In addition, accurate topic modeling and document representation are other challenging tasks in sentiment analysis. We propose an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification. The proposed system retrieves transportation content from social networks, removes irrelevant content to extract meaningful information, and generates topics and features from extracted data using OLDA. It also represents documents using word embedding techniques, and then employs lexicon-based approaches to enhance the accuracy of the word embedding model. The proposed ontology and the intelligent model are developed using Web Ontology Language and Java, respectively. Machine learning classifiers are used to evaluate the proposed word embedding system. The method achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment classification.

Meiotic recombination plays an important role in the process of genetic diversity generation. “Hotspots” are regions that show a higher rate of meiotic recombination, while the regions with a lower rate are called “cold spots”. It has a great effect on the genome evolution via gene conversion or mutagenesis. According to recent research, recombination is present in uneven distribution across the genome. Many computational methods have been developed using secondary sequence information or physiochemical properties of nucleotide descriptor for the prediction of hotspots and cold spots, which are computationally cheap and fast in performance rather than web-lab experiments, but the correlations between nucleotides pairs at different positions along DNA sequence is often ignored, which conceal a very important predictive information. In this study, we have proposed a deep neural network to predict recombination spots by fusing both the secondary sequence information and physio-chemical derived features. Our deep learning algorithm leverage's deep dense architecture by showing its effectiveness over the state-of-the-art methods with a classification accuracy of 90.04%, sensitivity of 92.21%, specificity of 92.11% and area under the curve of 0.9801. Moreover, it is anticipated, that our model will provide novel insight into basic research, drug designing, academic research and recombination spots studies particularly. All the methodology and python-based source code is publicly available for the users at https://github.com/zaheerkhancs/irSpot_SPI along with publicly accessible web server using the proposed predictor.

DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structure known chromatin while some of these proteins regulate the chromosome packaging and transcription process. In the pharmaceutical industry, DBPs are used as a key component of antibiotics, steroids, and cancer drugs. These proteins also involve in biophysical, biological, and biochemical studies of DNA. Due to the crucial role in various biological activities, identification of DBPs is a hot issue in protein science. A series of experimental and computational methods have been proposed, however, some methods didn't achieve the desired results while some are inadequate in its accuracy and authenticity. Still, it is highly desired to present more intelligent computational predictors. In this work, we introduce an innovative computational method namely DP-BINDER based on physicochemical and evolutionary information. We captured local highly decisive features from physicochemical properties of primary protein sequences via normalized Moreau-Broto autocorrelation (NMBAC) and evolutionary information by position specific scoring matrix-transition probability composition (PSSM-TPC) and pseudo-position specific scoring matrix (PsePSSM) using training and independent datasets. The optimal features were selected by the support vector machine-recursive feature elimination and correlation bias reduction (SVM-RFE + CBR) from fused features and were fed into random forest (RF) and support vector machine (SVM). Our method attained 92.46% and 89.58% accuracy with jackknife and ten-fold cross-validation, respectively on the training dataset, while 81.17% accuracy on the independent dataset for prediction of DBPs. These results demonstrate that our method attained the highest success rate in the literature. The superiority of DP-BINDER over existing approaches due to several reasons including abstraction of local dominant features via effective feature descriptors, utilization of appropriate feature selection algorithms and effective classifier.

Fiber-optic communication systems predominantly when employ in the nonlinear regime, generally do not perform truly as theory would speculate. This paper researched the theoretical model, power budget and transmission power penalties of the high capacity long haul optical network under the influence of nonlinear impairments. The presume model is affirmed using important nonlinear factors like effective area, launch power, received power, refractive index and length of the fiber. The power budget and transmission performance of the high capacity long haul optical network are analyzed on the basis of bit error rate and optical signal noise to ratio against launch power, length of the fiber, effective area and nonlinear refractive index. The simulation results manifest significant impact of nonlinear impairments on power budget and transmission power penalties of the high capacity long haul optical networks.

This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR systems for MR images is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional feature extraction methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap. It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features by encoding/combining low-level and high-level features. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction in self-learning. Therefore, we propose a deep convolutional neural network VGG19-based novel feature extraction framework and apply closed-form metric learning to measure the similarity between the query image and database images. Furthermore, we adopt transfer learning and propose a block-wise fine-tuning strategy to enhance the retrieval performance. The extensive experiments are performed on a publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Our method is more generic, as we do not use any handcrafted features; it requires minimal preprocessing, tested as robust on fivefold cross-validation, can achieve a fivefold mean average precision of 96.13%, and outperforms the state-of-the-art CBIR systems on the CE-MRI dataset.

DNA-binding proteins play a crucial role in various biological processes such as regulation of DNA modification, repair, replication, and transcription. These proteins widely participate in the production of drugs, antibiotics, and steroids. Many computational approaches have been developed to identify DNA-binding proteins, but some methods are time-consuming and expensive while some are laborious. Still, it is a challenging task for the researchers to develop highly promising computational methods to identify DNA-binding proteins with high precision. In our work, we developed a new computational approach named as DBPPred-PDSD which has more promising prediction power for DNA-binding proteins. We employed two datasets, extracted features via Split Amino Acid Composition (SAAC) and Position Specific Scoring Matrix (PSSM). Further, we applied the Discrete Wavelet Transform (DWT) on PSSM to extract dominant features. From these features space, optimal features are generated by Maximum Relevance and Minimum Redundancy (mRMR) and fused. To obtain highly informative features, we used Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and provided to well-known classifiers namely Support Vector Machine (SVM) and Random Forest (RF). Our model with the SVM classifier on three tests i.e. Jackknife cross-validation, 10-fold cross-validation and Independent tests achieved the highest success rate than other existing methods in the literature.

Radio-over-fiber (RoF) links successfully provide high data rates and bandwidth capacity with a low complexity system architecture, as compared to its counterpart digital-RoF. In addition, the compound of quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexed (OFDM) modulation schemes further enhance the process of these achievements. However, high data rates and bandwidth-capacity-supported RoF links face nonlinearities (NLs), linear distortions (LDs), and phase noise challenges that degrade the reliability of communication networks (CNs). Therefore, in this paper, to suppress NLs, LDs, and phase noise, next generation cloud radio access networks (CRANs) are investigated using RoF links and wavelength division multiplexing (WDM) methodology based on 16, 32, and 64 QAM-OFDM modulation schemes. The receiver of the proposed framework is designed, applying an improved digital signal processing (DSP) system that includes overlap frequency domain equalization (OFDE), a synchronization process, and time domain equalization (TDE). Theoretical and simulation models are organized for estimating the proposed RoF link with the aid of different values of transmission ranges, input power, output power, bit rate, bits per symbol, channel spacing, and the number of users. The fitness of the model matches that of existing approaches.

Nonlinear effects in the optical transmission systems (OTSs) are considered as the major performance limiting factor to provide high transmission rates over ultra-long distances. As the demands for system capacity, transmission range and the number of users is increasing exponentially with the development of mobile broadband, new challenges are being faced by the backbone optical networks. Mainly, the refractive index related non-linearities (RIrNLs) need to be characterized to design an optimal OTS for error-free transmission with provision of wavelength division multiplexing (WDM) to support for multiple channels. This paper provides an estimation technique of RIrNLs for long-haul transmission and their treatment for different channel spacing and the number of channels in a WDM system operating frequency domain multiple in multiple out (FD-MIMO) equalizer based digital signal processing (DSP) receiver and microstrip Chebyshev low pass filter. The main focus of this work is to utilize the existing structure of OTS for RIrNLs treatment with a low cost solution. Thus, by varying the parameters of the third order dispersion parameters, group velocity dispersion parameters, phase modulation dispersion and nonlinear refractive index, the impact of RIrNLs is investigated in detail to enhance the transmission range and capacity of the current OTS. The proposed system is analyzed in terms of range of input power, fiber length and received power for OTS figure of merits including bit error rate (BER) and optical signal-to-noise ratio (OSNR). Using duo-binary modulation, the BER achieved in this work is &lt, 10&minus, 5 till 500 km range, for maximum number of 32 channels, with 100 Gbps aggregate data rate, which shows the feasibility and effectiveness of our proposed model.

Functional materials are a group of engineered and advanced materials designed and synthesized for some specific function with proper surface morphology and tailored properties. We present a comprehensive overview of the recent research and development of functional materials. The challenges and research opportunities in the fabrication process of materials, and the functional fabrics selection are discussed. Decisions are required in the selection of materials such as polymers as organic components and SiO2, Fe3O4 as inorganic components of functionalized nanomaterials. Furthermore, manufacturing processes in terms of self-assembly, wrapping, grafting from, grafting onto, and one-pot synthesis are discussed in detail. In addition, the characterization, followed by modeling and analysis of functional materials are described with the importance of searching and identifying the relationship in fundamental structural properties. Challenges in the fabrication process of functional materials are addressed in their future prospects in different technical and scientific fields.

The generation of an enormous quantity of municipal solid waste (MSW) poses a potential risk to the environment and socioeconomics, globally. The rapid increase in the generation of MSW is even exacerbating the situation on a daily basis. Appropriate treatment of the prevailing MSW and eliminating/minimizing the generation of new MSW are essential to improve the quality of the environment and socioeconomic conditions. Herein, we present an overview of numerous approaches with emphasis on their challenges and opportunities for sustainable management of MSW. Following a brief instruction, a special focus is given to the 3R approach, which has not been reviewed before from the MSW perspective. Thus, to cover this literature gap, herein, we discussed 3R approach to present its potential to minimize the generation and/or elimination of MSW. For the treatment of the existing MSW, three different approaches including biological, thermal, and hydrothermal approach along with their challenges and opportunities are thoroughly discussed with suitable examples. Various MSW breakdown processes involved and their related products are also given with schemes. Moreover, the factors affecting the composition and implementation of a comprehensive MSW management program are briefly discussed. Finally, the economic feasibility of the MSW treatment methods, sociocultural status, policy, and political aspects are also highlighted for a better tomorrow.

The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.

Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas.

Next-generation cloud radio access networks (C-RANs) are anticipated to provide multi-Gbps data rate transmission and ultra-high bandwidth capacity, which is one of the key performance indicators for future mobile networks. The integral layout of fiber optics and radio network manages the capabilities of the C-RAN, but needs to be optimized in terms of cost, reliability and further scalibility. For C-RAN architectures, Radio over Fiber (RoF) transport-based fronthaul is a promising candidate but the associated issues of distortions due to nonlinear impairments (NLIs) from power amplifier, linear distortions (LDs) due to modulating lasers and high peak to average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals need to be addressed. This work investigates these performance limiting factors and presents a DSP receiver-based solution to mitigate the effects of NLIs, LDs and high PAPR. Simulations are performed by applying a various range of transmission input powers, different quadrature amplitude modulation (QAM) formats for the OFDM signal, optimized filtering at the receiver end and varying channel spacing among the optical WDM channels to analyze the performance of the proposed receiver under different conditions. The simulations and theoretical model of the proposed case studies verify that the presented solution for the RoF transport utilize less power, performs better for longer transmission distances, supports higher modulation formats and transports large number of WDM channels in the presence of NLIs and DLs as compared to the conventional RoF approach. With compensation of NLIs and LDs, transmission distance up to 10 km is investigated using 16 WDM channels with aggregate data rate of 100 Gb/s which shows that the proposed receiver can be used for future C-RAN fronthaul networks.

Intelligent Transport Systems (ITS) are anticipated to be one of the key technologies for the next decade and their deployment can benefit from the recent developments in the domain of Visible Light Communication (VLC). Light Emitting Diode (LED)-based low-cost VLC is considered in this work to provide a practical approach towards the implementation of an ITS by addressing the major issues of channel noise, free-space optical multipath reflections and interference from light sources. An analytical model is presented for the proposed Multiple-Input–Single-Output (MISO)-based VLC, and simulations are performed to analyze the performance of the system for various transmission distances. Results show that the proposed optimal receiver for 4 × 1 MISO can provide considerable improvement in the bit error rate for the forward error correction (FEC) threshold of 3.8 × 10−3 in the presence of optical interference, and is suitable to support an ITS with an inter-vehicle transmission approach. The comparison of achieved performance with existing solutions for VLC-based ITS depicts that the proposed framework provides much higher data rates, three times longer transmission distance and improved receiver sensitivity.

Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network-based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user's opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.

High capacity long haul communication and cost-effective solutions for low loss transmission are the major advantages of optical fibers, which makes them a promising solution to be used for backhaul network transportation. A distortion-tolerant 100 Gbps framework that consists of long haul and high capacity transport based wavelength division multiplexed (WDM) system is investigated in this paper, with an analysis on different design parameters to mitigate the amplified spontaneous emission (ASE) noise and nonlinear effects due to the fiber transmission. The performance degradation in the presence of non-linear effects is evaluated and a digital signal processing (DSP) assisted receiver is proposed in order to achieve bit error rate (BER) of 1.56 &times, 10&minus, 6 and quality factor (Q-factor) of 5, using 25 and 50 GHz channel spacing with 90 &mu, m2 effective area of the optical fiber. Analytical calculations of the proposed WDM system are presented and the simulation results verify the effectiveness of the proposed approach in order to mitigate non-linear effects for up to 300 km length of optical fiber transmission.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem’s constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

Fiber optic communication systems (FOCSs) have attained a lot of attention by revolutionizing the telecommunication industry and offering new possibilities with the technical advancements in state-of-the-art high speed digital electronics. Advanced modulation formats make use of the phase, amplitude, and polarization of the optical signals at the same time to provide high spectral efficiency as compared with 1 bit/s/Hz for the intensity modulation direct detection system (IMDD), but are highly prone to transmission impairments. Thus, the effects that add up to the optical fiber impairments such as optical fiber chromatic dispersion (OFCD), polarization model dispersion (PMD), and phase offset and noise (POaN) need to be addressed at the receiver side. The development of components and algorithms to minimize these effects in next generation FOCSs with 100 Gbps data rate and beyond with long-haul transmission is still a challenging issue. In this paper, digital signal processing- (DSP-) assisted dispersion and nonlinear compensation techniques are presented to compensate for physical layer impairments including OFCD, PMD, and POaN. The simulations are performed considering Dual Polarization- (DP-) QPSK modulation format to achieve two-fold data rate to achieve spectral efficiency of 3.28 bits/s/Hz by making use of the polarization diversity and system performance is investigated in terms of bit error rate (BER), constellation diagrams, and quality factor (Q-factor) for different values of optical signal-to-noise ratio (OSNR), launch power (PL), and fiber length.

Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT. Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at . The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.

Cell-penetrating peptides (CPPs) are short length permeable proteins have emerged as drugs delivery tool of therapeutic agents including genetic materials and macromolecules into cells. Recently, CPP has become a hotspot avenue for life science research and paved a new way of disease treatment without harmful impact on cell viability due to nontoxic characteristic. Therefore, the correct identification of CPPs will provide hints for medical applications. Considering the shortcomings of traditional experimental CPPs identification, it is urgently needed to design intelligent predictor for accurate identification of CPPs for the large scale uncharacterized sequences. We develop a novel computational method, called TargetCPP, to discriminate CPPs from Non-CPPs with improved accuracy. In TargetCPP, first the peptide sequences are formulated with four distinct encoding methods i.e., composite protein sequence representation, composition transition and distribution, split amino acid composition, and information theory features. These dominant feature vectors were fused and applied intelligent minimum redundancy and maximum relevancy feature selection method to choose an optimal subset of features. Finally, the predictive model is learned through different classification algorithms on the optimized features. Among these classifiers, gradient boost decision tree algorithm achieved excellent performance throughout the experiments. Notably, the TargetCPP tool attained high prediction Accuracy of 93.54% and 88.28% using jackknife and independent test, respectively. Empirical outcomes prove the superiority and potency of proposed bioinformatics method over state-of-the-art methods. It is highly anticipated that the outcomes of this study will provide a strong background for large scale prediction of CPPs and instructive guidance in clinical therapy and medical applications.

Optical communication networks (OCNs) provide promising and cost-effective support for the ultra-fast broadband solutions, thus enabling them to address the ever growing demands of telecommunication industry such as high capacity and end users&rsquo, data rate. OCNs are used in both wired and wireless access networks as they offer many advantages over conventional copper wire transmission such as low power consumption, low cost, ultra-high bandwidth, and high transmission rates. Channel effects caused by light propagation through the fiber limits the performance, hence the data rate of the overall transmission. To achieve the maximum performance gain in terms of transmission rate through the OCN, an optical downlink system is investigated in this paper using feed forward equalizer (FFE) along with decision feedback equalizer (DFE). The simulation results show that the proposed technique plays a key role in dispersion mitigation in multi-channel optical transmission to uphold multi-Gb/s transmission. Moreover, bit error rate (BER) and quality factor (Q-factor) below 10 &minus, 5 and above 5, respectively, are achieved with electrical domain equalizers for the OCN in the presence of multiple distortion effects showing the effectiveness of the proposed adaptive equalization techniques.

High-capacity and long-haul transmission gained great significance in modern communication networks. Optical fiber communication system is good enough to face the high demand of current telecom terrific. This paper will propound the theoretical model showing nonlinear factors which degrade the transmission performances of high-capacity long-haul network. The postulatory model will be validated using simulation of key nonlinear factors such as effective area, launch power, refractive index and fiber length. The transmission performance of the high-capacity long-haul optical network would be analyzed on the basis of some key parameters such as bit error rate and signal-to-noise ratio. Mitigation of nonlinear impairments shows significant impact on transmission performances of high-capacity long-haul optical networks.

In this paper, we propose a hybrid nano communication (HNC) system that integrates three possible modes of nano communications: terahertz communication (TC), molecular communication (MC), and neural communication (NC). The paper provides some insights into how the required four key building blocks, namely, terahertz to molecular (T2M) relay interface, molecular to terahertz (M2T) interface, molecular to neural (M2N) relay, and neural to molecular (N2M) interface can be designed to implement the HNC system. In addition, the capacity analysis for the hybrid channel is provided. Sub-channels’ capacities are also calculated. Finally, numerical results are presented to comprehend the data transmission capacity of the proposed HNC system.