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Abstract VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.

In the emerging and well-rooted field of Vehicular Adhoc-Networks (VANETs), the imperative of secure content exchange via Vehicle-to-Vehicle (V2V) communication for better safety emphasizes the importance of strong communication protocols. Implementing Information-Centric Networking (ICN) in VANETs allows for more efficient resource utilization via name-based content requests and delivery techniques. However, the dynamic and open nature of VANETs creates major security risks, necessitating rigorous defence against malicious attacks. To address these challenges, we introduce a novel trust-aware VANET framework that integrates ICN and Blockchain for content security. Our method uses authenticated vehicle data to ensure content integrity across the network, resulting in effective and efficient security measures. Through precise simulation, our technique exhibits resilience against malicious assaults by improving bandwidth utilization, throughput transactions, network content utilization, and content delivery in the face of hostile scenarios. The experimental results confirm the efficacy and feasibility of our proposed technique, highlighting its potential to improve VANET content security in real-world applications.

Abstract When the mutation affects the melanocytes of the body, a condition called melanoma results which is one of the deadliest skin cancers. Early detection of cutaneous melanoma is vital for raising the chances of survival. Melanoma can be due to inherited defective genes or due to environmental factors such as excessive sun exposure. The accuracy of the state-of-the-art computer-aided diagnosis systems is unsatisfactory. Moreover, the major drawback of medical imaging is the shortage of labeled data. Generalized classifiers are required to diagnose melanoma to avoid overfitting the dataset. To address these issues, blending ensemble-based deep learning (BEDLM-CMS) model is proposed to detect mutation of cutaneous melanoma by integrating long short-term memory (LSTM), Bi-directional LSTM (BLSTM) and gated recurrent unit (GRU) architectures. The dataset used in the proposed study contains 2608 human samples and 6778 mutations in total along with 75 types of genes. The most prominent genes that function as biomarkers for early diagnosis and prognosis are utilized. Multiple extraction techniques are used in this study to extract the most-prominent features. Afterwards, we applied different DL models optimized through grid search technique to diagnose melanoma. The validity of the results is confirmed using several techniques, including tenfold cross validation (10-FCVT), independent set (IST), and self-consistency (SCT). For validation of the results multiple metrics are used which include accuracy, specificity, sensitivity, and Matthews’s correlation coefficient. BEDLM gives the highest accuracy of 97% in the independent set test whereas in self-consistency test and tenfold cross validation test it gives 94% and 93% accuracy, respectively. Accuracy of in self-consistency test, independent set test, and tenfold cross validation test is LSTM (96%, 94%, 92%), GRU (93%, 94%, 91%), and BLSTM (99%, 98%, 93%), respectively. The findings demonstrate that the proposed BEDLM-CMS can be used effectively applied for early diagnosis and treatment efficacy evaluation of cutaneous melanoma.

Accommodating an increasing number of users in the Blockchain network has moved to the forefront of discussion. It is also evident that without jeopardizing the data security in Blockchain, it is of indispensable need to devise an appropriate method for improving the scalability trait of Blockchain. In this article, we have proposed a consensus method that is having the potential to improve the scalability of the Private Blockchain. The system, at first, mitigates latency arising from kernel schedulers, ensuring that the application consistently has access to an available core for transaction processing. Secondly, the committee system alleviates the network's workload, preventing spurious transactions from monopolizing network resources and impeding its efficiency. Extensive experimentation is made by considering various scenarios of transaction with CPU isolation and application sticking to core 2 with varied priority. Based on the number of transactions performed per second, the proposed system is compared with different existing consensus mechanisms working in various types of Blockchains. Also, a detailed discussion is presented on the critical analysis of the adopted research mechanism. Overall, the proposed systems outperforms to other systems in various parameters of blockchain network scalability.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further.

The potato plant is amongst the most significant vegetable crops farmed worldwide. The output of potato crop production is significantly reduced by various leaf diseases, which poses a danger to the world’s agricultural production in terms of both volume and quality. The two most destructive foliar infections for potato plants are early and late blight triggered by Alternaria solani and Phytophthora infestans. In actuality, farm owners predict these problems by focusing primarily on the alteration in the color of the potato leaves, which is typically problematic owing to uncertainty and significant time commitment. In these circumstances, it is vital to develop computer-aided techniques that automatically identify these disorders quickly and reliably, even in their early stages. This paper aims to provide an effective solution to recognize the various types of potato diseases by presenting a deep learning (DL) approach called EfficientPNet. More specifically, we introduce an end-to-end training-oriented approach by using the EfficientNet-V2 network to recognize various potato leaf disorders. A spatial-channel attention method is introduced to concentrate on the damaged areas and enhance the approach’s recognition ability to effectively identify numerous infections. To address the problem of class-imbalanced samples and to improve network generalization ability, the EANet model is tuned using transfer learning, and dense layers are added at the end of the model structure to enhance the feature selection power of the model. The model is tested on an open and challenging dataset called PlantVillage, containing images taken in diverse and complicated background conditions, including various lightning conditions and the different color changes in leaves. The model obtains an accuracy of 98.12% on the task of classifying various potato plant leaf diseases such as late blight, early blight, and healthy leaves in 10,800 images. We have confirmed through the performed experiments that our approach is effective for potato plant leaf disease classification and can robustly tackle distorted samples. Hence, farmers can save money and harvest by using the EfficientPNet tool.

Consumer knowledge of the goods produced or processed by the numerous suppliers and processors is still relatively low due to the growing complexity of the structure of pharmaceutical supply chains. Information asymmetry in the pharmaceutical sector has an effect on welfare, sustainability, and health. (1) Background: In this respect, we wanted to develop a productive structure for a pharmaceutical supply chain that satisfies the consumer information needs and fosters consumer confidence in the pharmacy goods they buy. By using blockchain technology, the main goals were to develop and implement a pharmaceutical supply chain. (2) Objectives: The main objectives of this work were to leverage an Ethereum platform for the development of a tractability system in a pharmaceutical supply chain environment and to analyze the efficiency of MSMAChain with respect to the cost and execution of transactions based on our designed smart contracts. (3) Results: This research looked into a variety of issues related to the value, viability, and effects of blockchain technology for use in supply chain applications. The methods and creations in this environment were monitored and researched. It is vital to identify a number of crucial subjects including future research areas, in order to achieve the widespread acceptance of the supply chain traceability provided by blockchain technology. (4) Conclusions: MSMAChain, an Ethereum blockchain-based approach, leverages smart contracts and decentralized off-chain storage for efficient product traceability in terms of the cost and execution of transaction for a health care supply chain.

Deep learning (DL) has emerged as a key application exploiting the increasing computational power in systems such as GPUs, multicore processors, Systems-on-Chip (SoC), and distributed clusters. It has also attracted much attention in discovering correlation patterns in data in an unsupervised manner and has been applied in various domains including speech recognition, image classification, natural language processing, and computer vision. Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. In addition, now, a number of commercial vendors also offer accelerators for deep learning systems (such as Nvidia, Intel, and Huawei).

Abstract With the evolution of fog computing, processing takes place locally in a virtual platform rather than in a centralized cloud server. Fog computing combined with cloud computing is more efficient as fog computing alone does not serve the purpose. Inefficient resource management and load balancing leads to degradation in quality of service as well as energy losses. Traffic overhead is increased because all the requests are sent to the main server causing delays which cannot be tolerated in healthcare scenarios. To overcome this problem, the authors are consolidating fog computing resources so that requests are handled by foglets and only critical requests are sent to the cloud for processing. Servers are placed locally in each city to handle the nearby requests in order to utilize the resources efficiently along with load balancing among all the servers, which leads to reduced latency and traffic overhead with the improved quality of service.

Abstract Edge-of-Things (EoT) emerged as a novel computing and storage paradigm to overcome the limitations of IoT-cloud environment by providing cloud-like services at edge of the network. EoT offers a vast area for research and development as the invention has laid out great opportunities to experiment the possibilities for handling large data sets produced by the growing Internet-of-Things (IoT). The EoT offers a framework that lies between the cloud-to-end to perform the processing and cater the storage demands of the IoT applications. However, the exponential increase in EoT infrastructure resulted into extreme energy consumption. This paper finds the opportunity to address the issue of energy consumption in IoT-EoT environment by introducing dynamic speed scaling mechanism in EoT devices. The proposed approach is rigorously evaluated, and the verification is acquired through the simulations carried out on the simulator, iFogSim. The results show significant improvement in energy conservation by dynamically scaling the processor frequency of EoT devices according to the load variations in IoT traffic.

The IoT (Internet of Things) connect systems, applications, data storage, and services that may be a new gateway for cyber-attacks as they continuously offer services in the organization. Currently, software piracy and malware attacks are high risks to compromise the security of IoT. These threats may steal important information that causes economic and reputational damages. In this paper, we have proposed a combined deep learning approach to detect the pirated software and malware-infected files across the IoT network. The TensorFlow deep neural network is proposed to identify pirated software using source code plagiarism. The tokenization and weighting feature methods are used to filter the noisy data and further, to zoom the importance of each token in terms of source code plagiarism. Then, the deep learning approach is used to detect source code plagiarism. The dataset is collected from Google Code Jam (GCJ) to investigate software piracy. Apart from this, the deep convolutional neural network is used to detect malicious infections in IoT network through color image visualization. The malware samples are obtained from Maling dataset for experimentation. The experimental results indicate that the classification performance of the proposed solution to measure the cybersecurity threats in IoT are better than the state of the art methods.

Source Code Authorship Attribution (SCAA) is to find the real author of source code in a corpus. Though, it is a privacy threat to open-source programmers, but, it may be significantly helpful to develop forensic based applications. Such as, ghostwriting detection, copyright dispute settlements, and other code analysis applications. The efficient features extraction is the key challenge for classifying real authors of specific source codes. In this paper, the Program Dependence Graph with Deep Learning (PDGDL) methodology is proposed to identify authors from different programming source codes. First, the PDG is implemented to extract control and data dependencies from source codes. Second, the preprocessing technique is applied to convert PDG features into small instances with frequency details. Third, the Term Frequency Inverse Document Frequency (TFIDF) technique is used to zoom the importance of each PDG feature in source code. Fourth, Synthetic Minority Over-sampling Technique (SMOTE) is applied to tackle the class imbalance problem. Finally, the deep learning algorithm is applied to extract coding styles' features for each programmer and to attribute the real authors. The deep learning algorithm is further fine-tuned with drop out layer, learning error rate, loss and activation function, and dense layers for better accuracy of results. The proposed work is analyzed on 1000 programmers' data, collected from Google Code Jam (GCJ). The dataset contains three different programming languages, i.e., C++, Java, C#. The results are appreciable in outperforming the existing techniques from the perspective of classification accuracy, precision, recall, and f-measure metrics.

Over the past decade, the digitization of services transformed the healthcare sector leading to a sharp rise in cybersecurity threats. Poor cybersecurity in the healthcare sector, coupled with high value of patient records attracted the attention of hackers. Sophisticated advanced persistent threats and malware have significantly contributed to increasing risks to the health sector. Many recent attacks are attributed to the spread of malicious software, e.g., ransomware or bot malware. Machines infected with bot malware can be used as tools for remote attack or even cryptomining. This paper presents a novel approach, called BotDet, for botnet Command and Control (C&C) traffic detection to defend against malware attacks in critical ultrastructure systems. There are two stages in the development of the proposed system: 1) we have developed four detection modules to detect different possible techniques used in botnet C&C communications and 2) we have designed a correlation framework to reduce the rate of false alarms raised by individual detection modules. Evaluation results show that BotDet balances the true positive rate and the false positive rate with 82.3% and 13.6%, respectively. Furthermore, it proves BotDet capability of real time detection.

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper.

Smart devices, the carriers of a huge amount of private, sensitive and confidential data are pervasive in today's world with innovative and enhanced functionalities. Smartphones have brought tremendous change in people's lives with the launch of a new platform of communication and an ease of access to a wide range of applications. Due to the swift increase in the users of Android smartphones and the increasing demands based on advanced ease and features, developers are working hard to achieve the needful. Easy access to certain features and applications gave rise to the powerfulness and an efficacy of various threats, risks and vulnerabilities that can victimize users' private data residing in smartphone paradigm. With the developments and enhancements in malware, for Android-based smartphones, attacks continue to occur. In this paper, we investigate one of the possibly most destructive attacks for Android, that is, screenshot attack. We have developed “ScreenStealer”application and explored the vulnerabilities which make Android more inclined to risks and threats. Furthermore, we evaluated capture ratio of screenshots, resources consumption and execution time to determine effectiveness, efficiency and stealthiness of such a malicious application.

The application of wireless body area network (WBAN) in health sector is crucial. It prolongs the life expectancy and improves the quality of life by enabling remote health monitoring. Despite many advantages, it may result and exacerbates various extant health issues especially in the context of mobile multimedia. Due to high data rate of mobile multimedia applications, heat generated from sensor devices is considerably high and results in thermal dissipation. The thermal impact of sensors on human body has largely been a neglected topic. The paper rigorously analyzes the specific absorption rate (SAR) impact on human body. Moreover, the paper largely contributes by comprehensively evaluating SAR impact under various circumstances and dependency factors. The analysis and evaluation show promising results that can significantly help the successful design and deployment of WBAN strategies. The paper also provides great guidance to numerous practitioners of this domain.

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

The Bag-of-Visual-Words (BoVW) model is widely used for image classification, object recognition and image retrieval problems. In BoVW model, the local features are quantized and 2-D image space is represented in the form of order-less histogram of visual words. The image classification performance suffers due to the order-less representation of image. This paper presents a novel image representation that incorporates the spatial information to the inverted index of BoVW model. The spatial information is added by calculating the global relative spatial orientation of visual words in a rotation invariant manner. For this, we computed the geometric relationship between triplets of identical visual words by calculating an orthogonal vector relative to each point in the triplets of identical visual words. The histogram of visual words is calculated on the basis of the magnitude of these orthogonal vectors. This calculation provides the unique information regarding the relative position of visual words when they are collinear. The proposed image representation is evaluated by using four standard image benchmarks. The experimental results and quantitative comparisons demonstrate that the proposed image representation outperforms the existing state-of-the-art in terms of classification accuracy.

Network lifetime and throughput are one of the prime concerns while designing routing protocols for wireless sensor networks (WSNs). However, most of the existing schemes are either geared towards prolonging network lifetime or improving throughput. This paper presents an energy efficient routing scheme for throughput improvement in WSN. The proposed scheme exploits multilayer cluster design for energy efficient forwarding node selection, cluster heads rotation and both inter- and intra-cluster routing. To improve throughput, we rotate the role of cluster head among various nodes based on two threshold levels which reduces the number of dropped packets. We conducted simulations in the NS2 simulator to validate the performance of the proposed scheme. Simulation results demonstrate the performance efficiency of the proposed scheme in terms of various metrics compared to similar approaches published in the literature.

In 6LoWPAN networks, several mobility management schemes have been proposed based on Mobile IPv6 (MIPv6) and Proxy Mobile IPv6 (PMIPv6). However, the existing schemes are centralized, and thus they have many serious drawbacks such as nonoptimal data route, injection of unwanted data traffics into core networks, increased cost of network engineering, and large registration and handover delays. To overcome these limitations, we propose new distributed mobility management schemes for 6LoWPAN networks. In the proposed schemes, Home Agent (HA), Local Mobility Anchor (LMA), and Mobile Access Gateway (MAG) functions are implemented in 6LoWPAN gateways, and the handover operations are performed directly between two neighboring 6LoWPAN gateways. By numerical analysis, we show that the proposed distributed schemes can significantly reduce the registration and handover delays, compared to the existing centralized mobility schemes.

The proposed idea in this paper is the constructive ramification of cluster designing mechanism floated in recently published work, multilayer cluster designing algorithm (MCDA) to ameliorate the performance in network lifetime. Novel algorithms for time slot allocation, minimizing the cluster head completion candidates, and cluster member selection∖node affiliation to cluster head play underpinning roles to achieve the target. These incorporations in MCDA result in minimizing transmissions, suppressing unfavorable response of transmissions and near-equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, number of designed clusters, number of nodes per cluster, and unclustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCD and EADUC.

In this paper, we introduce a mobility aware and energy efficient congestion control protocol “time sharing energy efficient congestion control (TSEEC) for mobile wireless sensor network.” TSEEC is based on hybrid scheme of time division multiple access protocol (TDMA) and statistical time division multiple access (STDMA) protocol that inform the sensor nodes when to wake up and to go to listening state so as to save energy. This management helps in minimizing congestion and improving network energy conservation through its load based allocation (LBA) and time allocation leister (TAL) techniques. LBA is typically based on STDMA that uses sensor node information for assignment of dynamic timeslots to the sensor, nodes, whereas TAL targets the mobility management of sensor nodes that further comprises three main strategies of cluster member node that is, joining cluster, leaving cluster, and absence of data/redundant data with extricated time allocation (ETA), shift back time allocation (SBTA), and eScaped Time Allocation (STA) subtechniques. In addition, TSEEC protocol introduces the mobility pattern to control the mobile sensor node (MSN) movement to enable the protocol to effectively adapt itself to change the traffic environments and mobility. Mathematical analysis and NS2 simulation show that TSEEC outperforms SMAC in terms of energy consumption and packet deliver ratio. Furthermore, a comparative analysis of TSEEC with various well-known related MAC protocols is also given.

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

This survey article is a comprehensive discussion on Intelligent Optimization of Wireless Sensor Networks through Bio-Inspired Computing. The marvelous perfection of biological systems and its different aspects for optimized solutions for non-biological problems is presented here in detail. In the current research inclination, hiring of biological solutions to solve and optimize different aspects of artificial systems' problems has been shaped into an important field with the name of bio-inspired computing. We have tabulated the exploitation of key constituents of biological system for developing bio-inspired systems to represent its importance and emergence in problem solving trends. We have presented how the metaphoric relationship is developed between the two biological and non-biological systems by quoting an example of relationship between prevailing wireless system and the natural system. Interdisciplinary research is playing a splendid contribution for various problems' solving. The process of combining the individuals' output to form a single problem solving solution is depicted in three-stage ensemble design. Also the hybrid solutions from computational intelligence-based optimization are elongated to demonstrate the emergent involvement of these inspired systems with rich references for the interested readers. It is concluded that these perfect creations have remedies for most of the problems in non-biological system.