AI is transforming cyber security with its creative methods for identifying, thwarting, and lessening cyberthreats. The growing complexity of attacks is too much for traditional security measures to handle. The use of artificial intelligence (AI) in contemporary cybersecurity is examined in this article, with particular attention paid to AI-powered solutions for threat identification, automated incident response, and intrusion detection systems (IDS). Combining machine learning and deep learning techniques improves artificial intelligence's ability to evaluate massive datasets, spot irregularities, and predict possible security breaches. This study also discusses important issues including data privacy, explainability of AI-driven solutions, and adversarial attacks on AI systems. Problems The report highlights how AI has the potential to transform cybersecurity and how further developments are necessary to address emerging security vulnerabilities. To summarise, AI is an effective tool for protecting intelligent systems, but continual innovation is required to guarantee reliable protection against emerging threats.

In the past few years, the Universities across the world has increased their focus on teaching-learning process improvisation. In this regards, the Universities periodically collect the students’ feedback through pre-defined questionnaire using online tools such as Google forms. However, such pre-defined questionnaire does not allow the students to express their opinion outside the questions. Therefore, students unable to convey their true feedback and the overall purpose of the feedback mechanism collapses. The alternative mechanism is to allow students to write a free style feedback within certain word limits. However, it is highly challenging to summarize such freestyle feedback manually to know the overall conclusion of the entire class. In this paper, we have engaged a Bidirectional Encoder Representations from Transformers to design an extractive summarization model for unstructured student feedbacks to improve the quality of educations. The proposed model allows the students to provide feedback about the course and also allow the course faculties to be able to summarize received student feedbacks in its true spirit by extracting the key ideas from students’ feedback. The experimental results show that the if produced summaries are too small then it unable to include all the important aspects. However, when produced summaries are sufficient in size then it successfully captures all the important aspects with PrecisionBERT, RecallBERT, and FscoreBERT of 78%, 61%, and 0.61%, respectively. The proposed model is compared with three existing schemes and it provides the improved results for PrecisionBERT, RecallBERT, and FscoreBERT.

In the era of the Internet of Medical Things (IoMT), providing excellent patient care requires an understanding of patient emotions and how they are feeling. The method described in this abstract, which makes use of OpenCV algorithms, is new for examining patient emotional patterns. This technology uses IoMT devices in conjunction with physiological signals from the body and facial expressions to ascertain the emotional states of patients. OpenCV’s powerful image processing methods, such as feature extraction, emotion recognition, and facial identification, are used to do real-time facial cue analysis. In order to contextualize emotions, IoMT devices also collect data on physiological traits including skin conductance, body temperature, and heart rate variability. Machine learning models are given the processed data in order to find connections linking emotional states to bodily reactions. Convolutional neural networks and recurrent neural networks are two examples of deep learning algorithms that are used to extract complex patterns from merged data. An important asset of the system is its versatility; it can be tailored for a variety of medical situations, such as chronic pain management, mental health disorders, and post-operative care. In the end, real-time analysis improves patient well-being and the general standard of healthcare services by enabling prompt responses, such as warning healthcare personnel of distress signals.

This book offers a comprehensive overview of the theory and practical applications of swarm intelligence in fog computing, beyond 5G networks, and information security. The introduction section provides a background on swarm intelligence and its applications in real-world scenarios. The subsequent chapters focus on the practical applications of swarm intelligence in fog-edge computing, beyond 5G networks, and information security. The book explores various techniques such as computation offloading, task scheduling, resource allocation, spectrum management, radio resource management, wireless caching, joint resource optimization, energy management, path planning, UAV placement, and intelligent routing. Additionally, the book discusses the applications of swarm intelligence in optimizing parameters for information transmission, data encryption, and secure transmission in edge networks, multi-cloud systems, and 6G networks. The book is suitable for researchers, academics, and professionals interested in swarm intelligence and its applications in fog computing, beyond 5G networks, and information security. The book concludes by summarizing the key takeaways from each chapter and highlighting future research directions in these areas.

The Internet of Things (IoT) strategy enables physical objects to easily produce, receive, and exchange data. IoT devices are getting more common in our daily lives, with diverse applications ranging from consumer sector to industrial and commercial systems. The rapid expansion and widespread use of IoT devices highlight the critical significance of solid and effective cybersecurity standards across the device development life cycle. Therefore, if vulnerability is exploited directly affects the IoT device and the applications. In this paper we investigated and assessed the various real-world critical IoT attacks/vulnerabilities that have affected IoT deployed in the commercial, industrial and consumer sectors since 2010. Subsequently, we evoke the vulnerabilities or type of attack, exploitation techniques, compromised security factors, intensity of vulnerability and impacts of the expounded real-world attacks/vulnerabilities. We first categorise how each attack affects information security parameters, and then we provide a taxonomy based on the security factors that are affected. Next, we perform a risk assessment of the security parameters that are encountered, using two well-known multi-criteria decision-making (MCDM) techniques namely Fuzzy-Analytic Hierarchy Process (F-AHP) and Fuzzy-Analytic Network Process (F-ANP) to determine the severity of severely impacted information security measures

Vehicular Ad hoc Networks (VANET) has been adopted for analysing connectivity among vehicular nodes in a mobile environment. It is assumed that the nodes travel through a multi lane and unidirectional highway path through multiple entry points without any crossing with each other. The nodes are distributed along the whole area by Poisson Distribution process. These mobile nodes communicate with each other through a specified distance and transmission range. However, since transmission is prone to interference thus a probabilistic approach has been proposed in this paper for better inter-communication between the nodes. Also, it is very essential that the knowledge of highway distribution should be pre-known before travel. This should be done in order to prevent and minimize any kind of collision or other tragic situations. Probability Density Function (PDF) has been used to extensively distribute the nodes based on better and reliable communication links through wireless channels. Since in this paper a multi lane headway path has been considered therefore the distribution of travelling nodes in each considered segment should be such that it prevents any collision or accident and also provide better interconnectivity and reliable link duration. The main contribution of this paper is to enhance the link duration performance between inter-nodal mobile communication while the vehicles move towards their exit points. The simulation results have shown 15% better improvement in keeping long link duration and better Poisson-based nodal distribution. The proposed approach will definitely help in better travel experience and road safety. The extensive simulations performed have successfully validated and confirmed the proposed approach.

Drone technology, also known as Unmanned Aerial Vehicles (UAVs), has advanced rapidly in the last decade owing to the huge number of users. This approach has an immense opportunity in areas like healthcare, agriculture, and forestry. However, the transmission of digital images via drone technology is associated with vulnerabilities and risks, due to a lack of efficient security solutions. Digital Image Watermarking (DIW) has emerged as a viable solution for digital image transmission in UAV applications. However, achieving robustness, security, and imperceptibility in a watermarking system at the same time is a difficult task. To address the aforementioned concerns, this paper proposes a secure digital image watermarking scheme in the hybrid DWT-SVD domain. The scaling factor is chosen adaptively from the hybrid Harmony-Grey Wolf (H-Grey) optimization algorithm to maintain a balance between imperceptibility and robustness. In addition, a novel symmetric cryptographic-based four-level encryption approach PSMD (Partitioning, Substituting, Merging, Division) is proposed to address UAV image security concerns by using Fibonacci and prime number series to generate two random keys. The proposed encryption scheme provides high security at low computational cost. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.27 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0008 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed encryption scheme is highly secure and the computational cost is also low. The average embedding and extraction time is 0.25 s and 0.09 s It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme’s effectiveness. The presented DIW can be used for copyright protection, UAV image transmission applications, military applications, and other purposes.

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly.

A mass of abnormal cells that form inside or outside the brain is called a brain tumor. Adults are at high risk of developing brain tumors, which can cause serious organ dysfunction and even death. Detecting tumors manually is a tedious and difficult method that might yield erroneous findings. As a result, these tumors must be meticulously classified to offer a complete medical diagnosis and design an appropriate treatment plan. Estimating the patient's chances of survival is difficult since tumors are uncommon and can vary greatly in size, location, and history. In order to address these issues, the use of two different deep learning frameworks for multi-class brain tumor classification utilizing Magnetic Resonance Imaging (MRI) data was examined in this study. Significant evaluation metrics, including F1 score, recall, accuracy, and precision, were applied to these models. Both models demonstrated significant improvements over prior brain tumor classification studies, illustrating that deep learning algorithms may be used in the future to accurately diagnose brain tumors and enable medical personnel to make well-informed judgments regarding patients' treatment courses. This study proposes two classification algorithms: ResNet50, that obtained a success rate of 99.39%, and EfficientNetB0, obtained accuracy rate of 99.75%.

A novel image retrieval method based on colour-texture contents for radar remote sensing applications is proposed, where global properties-based colour contents are extracted from different numbers of groups of histograms of colour image planes, and local properties-based texture contents have been derived from block level GLCM of an image plane. The integration of colour-texture contents represents the low dimensional feature which reduces overall computational overhead and increases the retrieval speed. To give importance to the feature components, suitable weights are imposed to both colour-texture contents appropriately. The obtained feature information describes the radar image effectively and also similarity measures play a significant role for better performance. This work compares eight similarity metrics to select the best one in the retrieval process. To validate the suggested method, experiments on two image datasets are performed and good retrieval results have been attained with rich colour-texture contents.

At present time, the ubiquitous availability of digital devices has rendered the validity of image contents doubtful via numerous open source and commercial image altering applications. The most prevalent forgery techniques are copy-move forgery and image splicing. As a result, methods for authenticating image contents are necessary. Local Binary Patterns (LBP) and Discrete Wavelets Transform (DWT) are presented here to validate the images. The input image is first transformed into YCbCr channels. Then, chroma channels are utilized to extract features. By capturing 4 statistical moments, features are retrieved using a 2-level DWT. For training and testing, an ensemble classifier is utilized and further classifies the images in authentic or forged. The suggested model provides high detection accuracy with relatively low dimensionality and testing time which demonstrates its efficacy. © Springer Nature Switzerland AG 2024.

In the recent past, cloud supported applications are dominating the market due to the flexibility, affordability, conveniency, and its ubiquitous access. Past few years have witnessed significant focus on enabling the cities with Internet of things supported devices for efficient monitoring and management. The recent advancement in the wireless communications such as 4G have contributed a lot and there is a growing expectation in wireless communications with 5G technology. In this book chapter, 5G enabled smart city framework, possible challenges, and future work is described. The chapter also focuses on how 5G technology can be integrated with cloud environment for overall development of smart city application.

Follow the leader (FTL) algorithm is a newly developed optimization algorithm inspired by a sheep’s movement within a flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in local optima is high due to randomness in the step parameter. This paper proposes a step-size follow-the-leader (SFTL) algorithm with decreasing and increasing step-size combinations. The improved step parameter tunes the search space by generating a new solution to improve the accuracy and convergence rate of the FTL algorithm. Four different FTL variants have been presented in this paper to show the impact of the dynamic step-size parameter. FTL improvement is verified by testing SFTL over thirty-two fixed unimodal, unimodal, fixed multimodal, and multimodal benchmark functions. The computational results indicate that SFTL significantly improves the basic FTL algorithm and converges early compared to other algorithms. SFTL has also been tested on five real engineering design problems and obtained results show that SFTL outperformed FTL and other popular optimization algorithms.

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Advancements in networking technologies have enabled doctors to remotely diagnose and monitor patients using the Internet of Medical Things (IoMT), telemedicine, and edge-enabled healthcare. In e-healthcare, medical reports and patient records are typically outsourced to a server, which can make them vulnerable to unauthorized access and tampering. Therefore, it is crucial to ensure the authorization, security, confidentiality, and integrity of medical data. To address these challenges, this paper proposes a novel reversible watermarking approach with a high payload and low computational cost. First, the input medical image is divided into a Border region (BR) and a Non-Border region (NBR). The NBR region is upscaled using Neighbour Mean Interpolation (NMI) to ensure reversibility. The Electronic Patient Record (EPR) is encrypted using a pseudorandom key, which is generated adaptively from the host medical image and the Enigma machine. The encrypted EPR is then embedded in the medical image using NMI. Two levels of tamper detection (global and local) are performed at the receiver's end for higher accuracy. A Global Integrity Code is generated and embedded in BR using LSB embedding technique for global tamper detection. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.03 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0019 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed scheme is highly secure and the computational cost is also low. The average embedding and extraction time (including embedding, encryption and decryption, extraction process respectively) is 0.88 s and 0.83 s. It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme's effectiveness.

With the rapid progress of cloud computing, there has been a marked improvement in the development of smart healthcare applications such as Internet of Medical Things (IoMT), Telemedicine, etc. Cloud-based healthcare systems can efficiently store and communicate patient electronic healthcare records (EHR) while allowing for quick growth and flexibility. Despite the potential benefits, identity violation, copyright infringement, illegal re-distribution, and unauthorized access have all been significant. To address all these breaches, in this paper, a reversible medical image watermarking scheme using interpolation is proposed. The medical image is partitioned into Border Region (BR), Region of Interest (ROI), and Region of Non-interest (RONI) regions. BR is used for embedding integrity checksum code generated from ROI for tamper detection. RONI is used for embedding watermark. To ensure complete recovery of ROI and high embedding capacity, ROI is compressed before embedding. To ensure high-security compressed ROI, hospital emblem and EHR merged and then encrypted using a random key generated from Polybius magic square to get higher security. The proposed scheme is proved to take less computational time as there are no complex functions used in the embedding. The experiments performed on the proposed scheme is proved to have high imperceptibility, robustness, embedding capacity, security, and less computational time. All these confirm that the proposed approach is a potential candidate for suitable in smart healthcare environment.

In the past decade, the world has seen rapid advancements in the field of healthcare services due to the state of the arts in technologies. Several real-time health monitoring applications and products are designed to assist the human to take the timely precautionary measures to avoid the unseen abnormalities. However, current healthcare monitoring infrastructures are not ready to provide efficient health services during the sudden and unknown pandemic situations such as COVID-19. The COVID-19 started in the later part of 2019, rapidly spread across the countries and labeled as a pandemic in the very early part of the 2020. Several people died due to the lack of the healthcare infrastructure and lack of access to health facilities. This book chapter explores the various technologies such as augmented reality, connected e-health along with the time series analysis of COVID-19 waves in India to know the implication of COVID-19 on society for a social good.

Widespread transmission of digital image in social media has come up with security, confidentiality and authentication issues. Ensuring copyright protection of digital images shared through social media has become inevitable. To address these issues, a robust and secure digital image watermarking scheme using Redundant discrete wavelet transform (RDWT) - Singular value decomposition (SVD) hybrid transform is proposed in this paper. In the proposed scheme, digital image is divided into 4 × 4 non-overlapping blocks, and low information blocks are selected for embedding to ensure higher imperceptibility. For watermark embedding 1-level RDWT is applied on the selected blocks followed by SVD decomposition to make the proposed scheme highly robust against common attacks. One watermark bit is embedded in each left and right singular SVD matrices by adjusting the coefficients. This makes the proposed scheme free from false positive error and achieve high embedding capacity. Before embedding, watermark encryption is done by using a pseudo random key. The pseudo random key is generated adaptively from the cover image by using discrete wavelet transform saliency map, block mean approach and cosine functions. High imperceptibility and robustness is indispensable for the digital images shared through social media. But, these watermarking characteristics are in trade-off. In the proposed scheme, the trade-off is balanced by using optimized scaling factor (embedding strength). Scaling factor is optimized by using the proposed JAYA-Firefly (Ja-Fi) optimization. Experimental results demonstrate that the proposed scheme provides high imperceptibility, robustness, embedding capacity and security. Furthermore, performance comparison with the recent state-of-the-art schemes affirms that the proposed scheme has superior performance.

Classification is the process of systematically arranging the data into multiple groups, which gives a better understanding of large datasets. Considerable research has been carried out on data classification to develop a predictive model with high accuracy. This paper introduces chaos in the Follow the leader (FTL) optimization algorithm to achieve global optima for imbalanced data classification problems. In the FTL algorithm, the random parameter ? may not give enough exploration to the particles, which may lead to local stagnation problems. One of the solutions to this problem can be the replacement of the random parameter ? by a chaotic variable. For a detailed study on the chaotic behavior of ? in FTL, the algorithm is implemented with ten different chaotic maps and tested on twenty-four standard benchmark functions. Thereafter, the best chaos FTL (cFTL) algorithm is tested on six different complex real engineering problems and compared with ten meta-heuristic algorithms. Moreover, to further validate the performance of cFT, it has been applied to ten standard classification datasets collected from UCI and Kaggle data repositories. The obtained results show that cFTL outperforms FTL and other meta-heuristic algorithms.

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.

DevOps (development and operations) is a collective and multidisciplinary organizational effort used by many software development organizations to build high-quality software on schedule and within budget. Implementing DevOps is challenging to implement in software organizations. The DevOps literature is far away from providing a guideline for effectively implementing DevOps in software organizations. This study is conducted with the aim to develop a readiness model by investigating the DevOps-related factors that could positively or negatively impact DevOps activities in the software industry. The identified factors are further categorized based on the internal and external aspects of the organization, using the SWOT (strengths, weaknesses, opportunities, threats) framework. This research work is conducted in three different phases: (1) investigating the factors, (2) categorizing the factors using the SWOT framework, and finally, (3) developing an analytic hierarchy process (AHP)-based readiness model of DevOps factors for use in software organizations. The findings would provide a readiness model based on the SWOT framework. The proposed framework could provide a roadmap for organizations in the software development industry to evaluate and improve their implementation approaches to implement a DevOps process.

This contribution applies tools from the information theory and soft computing (SC) paradigms to the embedding and extraction of watermarks in aerial remote sensing (RS) images to protect copyright. By the time 5G came along, Internet usage had already grown exponentially. Regarding copyright protection, the most important responsibility of the digital image watermarking (DIW) approach is to provide authentication and security for digital content. In this paper, our main goal is to provide authentication and security to aerial RS images transmitted over the Internet by the proposal of a hybrid approach using both the redundant discrete wavelet transform (RDWT) and the singular value decomposition (SVD) schemes for DIW. Specifically, SC is adopted in this work for the numerical optimization of critical parameters. Moreover, 1-level RDWT and SVD are applied on digital cover image and singular matrices of LH and HL sub-bands are selected for watermark embedding. Further selected singular matrices (Formula presented.) and (Formula presented.) are split into (Formula presented.) non-overlapping blocks, and diagonal positions are used for watermark embedding. Three-level symmetric encryption with low computational cost is used to ensure higher watermark security. A hybrid grasshopper–BAT (G-BAT) SC-based optimization algorithm is also proposed in order to achieve high quality DIW outcomes, and a broad comparison against other methods in the state-of-the-art is provided. The experimental results have demonstrated that our proposal provides high levels of imperceptibility, robustness, embedding capacity and security when dealing with DIW of aerial RS images, even higher than the state-of-the-art methods.

In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patient's health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys. Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data. However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient's E-Healthcare Records Management System (PRMS) that focuses on latency and throughput. A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively. The experimental results confirm the superiority of the PRMS under the high workload scenario over SRHB and its applicability in cloud data centers.

In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop different global and low-level texture features to build efficient CBIR systems. However, designing texture features that are suitable for distance-based retrieval is always a challenging task. Recently, Convolution Neural Networks have shown promising results for object detection and classification. CNNs are also applied to build classifier-based retrieval systems. However, the classifier-based retrieval methods can retrieve images only from the predicted class. Therefore, the performance of such system greatly depends on classification performance of the classifier. This paper proposes a method that exploits the strength of the Convolutional Neural Networks for predicting the class membership of the query image for all output classes and retrieve images using a modified distance function in the wavelet feature space. The performance of the proposed method is evaluated using three popular texture datasets of varying complexity and found to be superior to all competing methods considered.

Energy management is essential for both economy and environment, and proper management needs an efficient forecasting tool. Energy forecasting plays a significant role in balancing the generator-distributor loads. In this paper, a hybrid model has been proposed for accurate and effective electricity price forecasting. To improve the forecasting results of vanilla artificial neural network (ANN), it has been combined with different meta-heuristic algorithms to train its network parameters. The meta-heuristic algorithms evaluated here include follow the leader (FTL), cuckoo search optimization (COA), fruit fly optimization (FFOA), and particle swarm optimization (PSO). The performance of the hybrid models is evaluated on the New Pool, England dataset to predict the short-term electricity price. Results show that the FTL-ANN algorithm outperforms the other algorithms, including traditional ANN architecture.

Swarm-based models mimic the collective behavior shown in insects or animals. To date, several algorithms have been proposed by researchers to solve a wide range of complex optimization problems. This paper presents an improved version of follow the leader (iFTL) algorithm that imitates the behavioral movement of a sheep within the flock. The work presented in this paper mathematically models this foraging behavior to realize the process of optimization. The COmparing Continuous Optimisers (COCO) experimental framework is used for performance evaluation with twenty-four noiseless and thirty noisy benchmark functions. After that, it has been compared with fourteen well-presented optimization algorithms in different dimensions. The results generated show that iFTL outperformed all compared optimization algorithms and outranked in all dimensions. The iFTL algorithm is also tested on twenty-four standard benchmark function and compared with eight well-known optimization algorithms to benchmark its performance. Further, the efficacy of the proposed algorithm is tested on 10, 37, 52, 72, 120, 200, 224, and 942 bar truss design problems. Finally, the results generated by truss design problems are compared with other meta-heuristics algorithms to validate the performance of the proposed algorithm. The obtained results reveal that iFTL is efficient and stable than other state-of-the-art algorithms.

Direct histogram to histogram matching in content-based image retrieval is not proficient due to its large number of bins. The total number of bins of an original histogram represents the large dimensional feature descriptor which requires high computational overhead during the retrieval process. To address this issue, in the proposed scheme image histogram is quantized into a different number of bins which represents the low dimensional feature descriptor effectively. Since, the global and local features play an important role in image retrieval, therefore, considering any single feature for image retrieval is not adequate, so in this paper, a quantized histogram-based global and local features have been considered for feature representation. To avoid variations among the feature components, suitable weights are assigned to the local and global features effectively. To check the efficacy of the proposed method, performance analysis using a different number of bins has been evaluated based on two standard similarity distances for corel-1 K image dataset. The presented work has achieved satisfactory retrieval results in terms of precision, recall, and F-score metrics.

Advanced predictive analytics coupled with an effective attribute selection method plays a pivotal role in the precise assessment of chronic disorder risks in patients. Traditional attribute selection approaches suffer from premature convergence, high complexity, and computational cost. On the contrary, heuristic-based optimization to supervised methods minimizes the computational cost by eliminating outlier attributes. In this study, a novel buffer-enabled heuristic, a memory-based metaheuristic attribute selection (MMAS) model, is proposed, which performs a local neighborhood search for optimizing chronic disorders data. It is further filtered with unsupervised K-means clustering to remove outliers. The resultant data are input to the Naive Bayes classifier to determine chronic disease risks' presence. Heart disease, breast cancer, diabetes, and hepatitis are the datasets used in the research. Upon implementation of the model, a mean accuracy of 94.5% using MMAS was recorded and it dropped to 93.5% if clustering was not used. The average precision, recall, and F-score metric computed were 96.05%, 94.07%, and 95.06%, respectively. The model also has a least latency of 0.8 sec. Thus, it is demonstrated that chronic disease diagnosis can be significantly improved by heuristic-based attribute selection coupled with clustering followed by classification. It can be used to develop a decision support system to assist medical experts in the effective analysis of chronic diseases in a cost-effective manner.

The Internet of Medical Things (IoMT) plays a big role in today’s healthcare industry. Smart healthcare has been a great use in sharing all kinds of medical data from Electronic Health Records (EHR) to hospital management. This advancement in healthcare made the diagnosis easy for the doctors and patients by saving their time. But in recent years there are a lot of threats in IoT and IoMT frameworks which motivated many researchers to find a way to overcome these challenges. In this paper, reversible data hiding technique using a linear and quadratic difference expansion to embed the EHR on the image is proposed. The medical image is divided into Border Region (BR) and Non Border Region (NBR). The hospital logo is watermarking in the BR of the image. Using LSB (Least Significant Bit) method hospital logo is embedded to ensure autauthentication. LSB approach is an easy and fast way to perform. To ensure confidentiality NBR is used for EPR embedding. Selecting a pair of pixels in NBR for embedding 2 bits of EPR pixels at each time with the linear and quadratic difference expansion. For ensuring dual security, EPR is encrypted using a Pseudo random key. Then encrypted EPR is partitioned into Odd and Even position pixels parts. Further, Odd and Even position pixels are watermarking in NBR using linear and quadratic difference expansion. Pseudo random key is generated adaptively from the mean of Medical image and Divide and Conquer algorithm to provide higher security. This proposed method is proved to take less computational time as there are no complex functions used in the algorithm. The test performed on this technique is proved to have high imperceptibility, robustness, security, and less computational time. All this confirms that proposed approach is a potential candidate for the security of data in the IoMT frameworks.

Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5-10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier's accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.

In the recent past, Artificial intelligence and its sister technology such as Machine Learning, Deep Learning, and Reinforcement learning have grown rapidly in several applications. The self-driving car is one of the applications, which is the need of the hour. In this paper, we describe the trends in autonomous vehicle technology for the self-driving car. There are many different approaches to mathematically formulate a design for the self-driving car such as deep Q-learning, Q-learning, and machine learning. However, in this paper, we propose a very basic and less compute-intensive simplistic self-driving car model called “ReLearner” using the Gym environment. To simulate the self-driving car model, we preferred to create a simple small environment OpenAi gym which is a deterministic environment. The OpenAi gym provides the virtual simulation environment and parameter tuning to train and test the model. We have focused on two methods to test our model. The basic approach is to compare the performance of the car when tested using Q-Learning and another using a random action agent, i.e., No reinforcement learning. We have derived a theoretical model and analyzed how to use Q-learning to train cars to drive. We have carried out a simulation and on evaluating the performance and found that Q-learning is a more optimal approach to solve the issue of a self-driving car.

Programmatic buying popularly known as real-time bidding (RTB) is a key ascendancy in online advertising. The Ad Tech industry is experiencing sustained growth, especially due to the increased use of mobile devices. While data has become essential for targeting and ad performance, data businesses have become difficult to differentiate due to their proliferation, as well as limitations of attribution. This provides an opportunity for Big Data practitioners to leverage this data and use machine learning to improve efficiency and make more profits. Taking such an opportunity we came up with an application of a machine learning algorithm, distributed back propagation neural network (d-bpnn) to predict bid prices in a real-time bidding system. This paper depicts how d-bpnn is used to achieve less eCPM (effective Cost Per Mille) for advertisers while preserving win rate and budget utilization.

With the growth in Internet and digital technology, Internet of Medical Things (IoMT) and Telemedicine have become buzzwords in healthcare. A large number of medical images and information is shared through a public network in these applications. This paper proposes a region-based hybrid Medical Image Watermarking (MIW) scheme to ensure the authenticity, authorization, integrity, and confidentiality of the medical images transmitted through a public network in IoMT. In the proposed scheme, medical images are partitioned into Region of Interest (RoI) and Region of Non-Interest (RoNI). To ascertain integrity of RoI, tamper detection and recovery bits are embedded in RoI in the medical image. RoI is watermarked using adaptive Least Significant Bit (LSB) substitution with respect to the hiding capacity map for higher RoI imperceptibility and accuracy in tamper detection and recovery. Electronic Patient Record (EPR) is compressed using Huffman coding and encrypted using a pseudo-random key (secret key) to provide higher confidentiality and payload. QR code of hospital logo, Encrypted EPR, and RoI recovery bits are interleaved in RoNI using Discrete Wavelet Transform-Singular Value Decomposition (DWT-SVD) hybrid transforms to achieve a robust watermark. The proposed scheme is tested under various geometric and non-geometric attacks such as filtering, compression, rotation, salt and pepper noise and shearing. The evaluation results demonstrate that the proposed scheme has high imperceptibility, robustness, security, payload, tamper detection, and recovery accuracy under image processing attacks. Therefore, the proposed scheme can be used in the transmission of medical images and EPR in IoMT. Relevance of the proposed scheme is established by its superior performance in comparison to some of the popular existing schemes.

Due to the tremendous increase in the digital image data, the efficient and effective image content-based search scheme for retrieving desired images from a large image repository is highly required. The biggest challenge in image retrieval scheme is to retrieve the desired multimedia images from the digital image repository with minimum time. Extracting significant image features with low dimensional feature descriptor play a significant role in improving retrieval outcomes. In the presented paper, an image retrieval scheme is proposed using fused low dimensional feature descriptor which is obtained by fusion of probability histogram-based HSV color moments and multiresolution based shape moments. The color moments and shape moments are extracted from the Laplacian filter based preprocessed image. The suggested scheme is implemented on a standard Corel-1K image dataset and the retrieval accuracy is measured using precision, recall, and F-score metrics. The experimental outcomes are also validated and compared with some existing state of the art image retrieval schemes and it outperforms over the existing ones.

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We intend to propose an ensemble optimization algorithm based on Follow The Leader (FTL), Multi-verse Optimizer (MVO), and Salp Swarm Algorithm (SSA) to solve constrained optimization problems. The FTL, MVO, and SSA are swarm-based algorithms that update their particle position using a selection approach. Less number of control parameters and a common selection approach make these algorithms suitable for hybridization. In this work, combinations of FTL, MVO, and SSA algorithms such as FTL_MVO, FTL_SSA, MVO_SSA, and FTL_MVO_SSA have been proposed to solve different optimization problems. The proposed ensemble optimization algorithms have been compared with base optimization algorithms on forty-eight unimodal and multimodal benchmark functions. The ensemble model has achieved significant performance improvement over base FTL, MVO, and SSA. Moreover, these algorithms have been tested on six well-known constrained optimization problems to benchmark their performance over real-world applications. Finally, the comparison with classical optimization algorithms reveals the efficacy of the proposed models.

In the past decade, rapid development in digital communication has led to prevalent use of digital images. More importantly, confidentiality issues have also come up recently due to the increase in digital image transmission across the Internet. Therefore, it is necessary to provide high imperceptibility and security to digitally transmitted images. In this paper, a novel blind digital image watermarking scheme is introduced tackling secured transmission of digital images, which provides a higher quality regarding both imperceptibility and robustness parameters. A block based hybrid IWT-SVD transform is implemented for robust transmission of digital images. To ensure high watermark security, the watermark is encrypted using a Pseudo random key which is generated adaptively from cover and watermark images. An encrypted watermark is embedded in randomly selected low entropy blocks to increase the security as well as imperceptibility. Embedding positions within the block are identified adaptively using a Blum–Blum–Shub Pseudo random generator. To ensure higher visual quality, Initial Scaling Factor (ISF) is chosen adaptively from a cover image using image range characteristics. ISF can be optimized using Nature Inspired Optimization (NIO) techniques for higher imperceptibility and robustness. Specifically, the ISF parameter is optimized by using three well-known and novel NIO-based algorithms such as Genetic Algorithms (GA), Artificial Bee Colony (ABC), and Firefly Optimization algorithm. Experiments were conducted for the proposed scheme in terms of imperceptibility, robustness, security, embedding rate, and computational time. Experimental results support higher effectiveness of the proposed scheme. Furthermore, performance comparison has been done with some of the existing state-of-the-art schemes which substantiates the improved performance of the proposed scheme.

Eigenvector-based moments are proposed for offline signature validation. Here, principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for dimension reduction and generated eigenvector which is calculated using Euclidean distance. It measured the distance between two vectors having an equal size in 2-D space. A newly suggested approach to generate the eigenvector from training and testing samples of signatures, which is calculated through Euclidean distance as a classifier. In which, it has shown high verification accuracy of 91.07% on the MCYT-75 corpus and GPDS synthetic signature database.

This paper presents a new hybrid image watermarking method based on IWT, DCT, and SVD domains, to solve the problem of false-positive detection and scale down the impact of geometric attacks. Properties of IWT, DCT, and SVD enable in achieving higher imperceptibility and robustness. However, SVD-based watermarking method suffers from a major flaw of false-positive detection. Principal component of watermark is embedding in the cover image to overcome this problem. Attacker cannot extract watermark without the key (eigenvector) of the embedded watermark. To recover geometrical attacks, we use a synchronization technique based on corner detection of the image. Computer simulations show that the novel method has improved performance. A comparison with well-known schemes has been performed to show the leverage of the proposed method.

We are proposing a novel Integer Wavelet Transform(IWT)-Singular Value Decomposition(SVD) blind digital image watermarking scheme using hybrid transform to achieve higher imperceptibility, robustness, security and authenticity. In the proposed scheme, pseudo-random Latin square sequence is used for watermark encryption, encrypted watermark is divided and cross implant technique is used for embedding to overcome the problem of False Positive Problem (FPP). Further watermarking strengthening parameter is optimized using nature-inspired Artificial Bee Colony(ABC) algorithm, Simulation results shows that the proposed scheme has higher imperceptibility and robustness with different image modalities(gray-scale and colored). Performance comparison with some popular schemes shows that the proposed scheme surpass them in terms of robustness, imperceptibility and confidentiality.

Smart healthcare monitoring systems provide better healthcare service by improving the availability and transparency of health data. However, it also posses serious threats to data security and privacy. As medical internet of things (IoT) are connected to other devices through various networks that provide a suitable attack surface for the intruders. Further, the health data are sensitive, and any breach in security may lead to wrong treatment or compromising the privacy of the patients. In this regard, a secure IoT frame is desirable, which is capable of preserving the integrity and confidentiality of the medical data. In this paper, we have proposed a novel architecture which leverages the blockchain technology to enhance the security and privacy of IoT for healthcare applications. In the proposed architecture called smart healthcare system for patients in ICU (SHPI), critical data is processed in edge computing which is located inside the hospital to reduce the communication latency. In order to provide tramper-proof medical records and data confidentiality SHPI uses blockchain technology and cryptographic methods respectively. Also, a data accessing token system is introduced to separate the group of users based on their roles. This system utilizes smart contracts to record every event for providing transparency in medical activities. In order to describe the working principles a logical analysis is carried out, that shows the system is capable of providing the desired security and privacy.