Knowledge about infant language can go a long way in supporting parents, nurses, and care providers in improving babies' health conditions. Crying is the most effective tool through which babies convey their requirements. In this work, several studies dealing with infant cry detection and classification are contrasted. Research demonstrates that machine learning techniques can effectively categorize and classify infant needs and certain disorders. Several datasets, including Baby Chillanto, Donate A Cry Corpus and Dunstan Baby Language, are presented. After reviewing existing Datasets, preprocessing methodologies and audio feature extraction such as MFCC, RMS energy, etc., are discussed. For infant cry detection and classification, several algorithms, such as support vector machines (SVM), convolutional neural networks (CNN), k-nearest neighbors (KNN), Random Forest, etc., have been analyzed and utilized for such processes in general. Finally, the study explores various applications of infant cry analysis, highlighting its potential to improve infant care and facilitate early diagnosis. As a result of the findings, it has been observed that infant cry analysis can effectively identify different needs and potential health concerns with high accuracy. These machine learning models' classification outputs have the potential to (1) improve childcare practices, (2) detect medical issues earlier, and (3) monitor infants continuously. These features give medical professionals and caregivers useful information for prompt intervention. The implementation of these findings can be applied in hospitals, neonatal intensive care units (NICUs), smart baby monitoring systems, and research studies focused on early childhood development.

Abstract In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low‐latency healthcare decision system using Q‐learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real‐time medical data processing while enforcing robust security throughout the IoT‐based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats.

The prompt expansion of Internet of Things (IoT) devices necessitates advanced security frameworks to protect data integrity, confidentiality, and availability in resource-constrained environments. Traditional security solutions are often resource-intensive for IoT devices with limited computational power and energy resources. This study addresses these inadequacies by proposing a novel approach formulated to such constraints. This study propose the trust-enhanced lightweight security framework (TELSF), integrating two novel components: 1) the adaptive lightweight encryption algorithm (ALEA) and 2) the trust-aware data protection model (TADPM). ALEA employs dynamic key generation through a lightweight hash function, ensuring unique and regularly updated encryption keys based on device context and behavior. TADPM enhances this framework by continuously assessing device trustworthiness through direct interactions, aggregated feedback from neighboring devices, and contextual parameters, such as location and device capabilities. Performance evaluations demonstrate that TELSF significantly enhances security and operational efficiency, reducing computational overhead by 18%, improving energy efficiency by 20%, and increasing data transmission security by 10% compared to existing solutions.

Ultrasound imaging continues to play a critical role in prenatal diagnostics, but accurate interpretation remains hindered by limited labeled data, inconsistent pseudo label quality, and real-time processing constraints in Internet of Medical Things (IoMT) environments. Existing semi-supervised learning (SSL) frameworks fail to maintain reliable segmentation under these dynamic and resource-constrained conditions. This study proposes ADLIFT, a real-time SSL-based ultrasound processing framework designed to optimize diagnostic accuracy and computational efficiency. The approach integrates an Adaptive Dual-Layer Perception (ADLP) mechanism combining macro-level anatomical recognition with micro-level feature refinement, and a Dynamic Label Generation (DLG) module that iteratively improves pseudolabel reliability using confidence-driven feedback. Efficient Sparse Feature Extraction (ESFE) minimizes computational overhead by isolating high-activation regions, while the Temporal Contextualization Framework (TCF) ensures inter-frame consistency. Blockchain-enhanced edge computing supports secure and scalable IoMT deployment. Evaluations in HC18, FetalPlane18 and Kvasir-Segment datasets demonstrate precision of 93. 7%, decision stability of 92. 8%, interpretability index of 91. 5%, uncertainty handling efficiency of 89. 7%, trust reliability score of 95. 3%, and processing latency of 28.1 ms per frame.

Topological Deep Learning (TDL) boosts the capabilities of neural networks by integrating topological features from complex data structures like graphs and simplicial complexes. However, there is a critical gap in the area of structural perturbations within a model and how these affect its stability and generalization. Overfitting and brittle predictions are concerns when dealing with topological noise. We propose a comprehensive theoretical and empirical evaluation framework of TDL robustness. We define two complementary metrics, Topological Drift which quantifies model sensitivity to structural noise using bottleneck distance in persistent homology, and Spectral Variance which measures shifts in the eigenvalues of the Hodge Laplacian. These define the degradation of learned topological representations. We conducted controlled experiments on synthetic Vietoris-Rips complexes, together with real-world data from the TDA Benchmark’s Enzyme Function Prediction dataset. These experiments support our conclusions. High levels of perturbation resulted in a drop in classification accuracy of 19.1%, and an increase in spectral variance of 4.3×, confirming theoretical expectations. Comparison with neglecting frameworks after 2022 on Simplicial Neural Networks and spectral graph models showcases the need for our rigorous dual-metric interpretable approach to stability analysis. Our persistent homology and spectral topology merger lay metric derived from persistent homology to quantify topological drift adaptable for creating TDL architectures with robustness. The results obtained in this study can be used to formulate model equipped with topological state descriptors derived from persistence diagrams and eigen-spectra methods of deep learning for advanced fields like bioinformatics, neuroscience, and structural biology, which involve data with inherent topological variations.

Chronic Kidney Disease (CKD) is a global health issue due to pre-existing conditions like anemia, diabetes, and hypertension. Early warning signals are commonly neglected, complicating diagnosis and treatment. This study uses high-performance data mining techniques to provide self-analysis and prognostic tools for medical professionals. Clinical decision support using domain experts and specialists aids CKD prognosis and rehabilitation. We use advanced data mining to evaluate demographic characteristics, test findings, and patient medical information to uncover insights and patterns. We used a modified artificial neural network to extract characteristics from a dataset and long-term and short-term memory to classify chronic kidney disease stages. LSTM algorithms improve the accuracy of CKD diagnosis and stage prediction. Initial validation tests demonstrate a 92.23% accuracy gain over traditional methods. The binary classification AUC was 99.95% and the multi-class classification was 94.13% with the proposed technique. LSTM algorithms use temporal dynamics in patient data to enhance diagnostic accuracy, early diagnosis, therapy customization, and healthcare costs. We proposed a novel CKD diagnostic and prognosis paradigm and test multiple classification algorithms for disease diagnosis and staging. Multiple methods improve reliability and account for CKD diagnostic peculiarities.

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.

Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is cancer, and among all kinds of cancers like, brain pulmonary; lung cancer is fatal. The cancers could be detected early to save lives using Computer Aided Diagnosis (CAD) systems. CT scans medical images are one the best images in detecting these tumors in lungs that are especially accepted among doctors. However, the location, random shape of tumors, and poor quality of CT scan images are among the main challenges for physicians in identifying these tumors. Therefore, deep learning algorithms have been highly regarded by researchers. This paper proposed a new model for tumors and nodules segmentation in CT scans images based on convolution neural network (CNN) algorithm. The proposed model comprises preprocessing and postprocessing for fine segmentation of nodules. Filtering is used for image enhancement in preprocessing, and morphological operators are used for fine segmentation in post-processing. Finally, the active counter algorithm implementation exhibited tumors and nodules detection precisely. The sensitivity assessment and dice similarity criteria qualitatively measure the proposed model efficiency on the benchmark dataset. The obtained results with 98.33% accuracy 99.25% validity,98.18% dice similarity criterion show superiority of the proposed model.

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

Abstract The automated behavior analysis is a significant problem in the examination. In the presence of invigilators and examiners, examinees use various unfair means to cheat the staff. Examinees may use different gestures like moving their head, eyes and using different suspicious objects (mobile phone, calculator) and so forth. This research is intended to automatically identify and distinguish examinees in live videos who cheat by their activities in examination hall during exams. Hence, we took the privilege of ensemble learning using deep learning‐based algorithms. The suspicious head movements and prohibited objects have been monitored using fine‐tuned Faster RCNN algorithm. At the same time, interactive use‐age of prohibited objects find out using Open‐Pose architecture. For this purpose, intersection over union (IOU) has been calculated between the region of interest (ROI) of detected objects and pose points of hands and face. To get the identity of a specific examinee, we stored the features of facial ROI after the last convolution layer and matched them at the time of testing for statistical analysis. We achieved state‐of‐the‐art results using standard evaluating measures. Moreover, for experiments, dataset of automatic examinee invigilation is available currently. So, this research also contributes toward the generation and annotation of the dataset.

Abstract Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning‐based solution to the agricultural community, which helped farmers to identify the diseases at the early stages. In this article, our focus is on grape diseases, and proposes a novel framework to identify and classify the selected diseases at the early stages. A deep learning‐based solution is embedded into a conventional architecture for optimal performance. Three primary steps are involved; (a) feature extraction after applying transfer learning on pre‐trained deep models, AlexNet and ResNet101, (b) selection of best features using proposed Yager Entropy along with Kurtosis (YEaK) technique, (c) fusion of strong features using proposed parallel approach and later subject to classification step using least squared support vector machine (LS‐SVM). The simulations are performed on infected grape leaves obtained from the plant village dataset to achieving an accuracy of 99%. From the simulation results, we sincerely believe that our proposed approach performed exceptionally compared to several existing methods.

Abstract Automated skin lesion diagnosis from dermoscopic images is a difficult process due to several notable problems such as artefacts (hairs), irregularity, lesion shape, and irrelevant features extraction. These problems make the segmentation and classification process difficult. In this research, we proposed an optimized colour feature (OCF) of lesion segmentation and deep convolutional neural network (DCNN)‐based skin lesion classification. A hybrid technique is proposed to remove the artefacts and improve the lesion contrast. Then, colour segmentation technique is presented known as OCFs. The OCF approach is further improved by an existing saliency approach, which is fused by a novel pixel‐based method. A DCNN‐9 model is implemented to extract deep features and fused with OCFs by a novel parallel fusion approach. After this, a normal distribution‐based high‐ranking feature selection technique is utilized to select the most robust features for classification. The suggested method is evaluated on ISBI series (2016, 2017, and 2018) datasets. The experiments are performed in two steps and achieved average segmentation accuracy of more than 90% on selected datasets. Moreover, the achieve classification accuracy of 92.1%, 96.5%, and 85.1%, respectively, on all three datasets shows that the presented method has remarkable performance.

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.

At present, governments and private business operations are highly dependent on relational data applications such as bank accounts, citizen registration, etc. These relational data dependent operations require reliable integrity protection while utilising the cloud computing storage infrastructure. Identification and recovery of stolen bits are a major assistance to the reliable integrity protection services for the sensitive relational data applications. To deal with the problems of detecting and recovering tampering in large relational data at minimum computational complexity, in this paper, N8WA (briefed in Section 2.1) coding-based scheme is presented. Overall the scheme is comprised of two cross functional modules. The first module is labelled as compact code generation using N8WA coding and code registration at registration module (RM). In the second module which is called accurate locating/restoring tampering, utilising the mismatching of different compact codes based on N8WA from RM, the major/minor tampered data is accurately located and restored. Investigational outcome indicates that the scheme ensures the computational complexity of O(n2) while minimum to maximum alterations is accurately localised and restored successfully.

In federation of cloud, multiple cloud service providers share their resources based on certain assumptions and trust. Most of the times, identity of a user and permission to give access are shared using service access requirements. In intercloud security, building mutual trust relationship between multiple cloud federated identities is the main goal. Securing the components involved in cloud federation can be helpful in building mutual trust relationship between federated identities. Intrusion Detection and Prevention techniques helped a lot in detecting the malicious activities performed by the intruders. In this domain of securing data, a lot of research is being done. Recently, artificial intelligence and machine learning have greatly attracted the attention of researchers to integrate the concepts of network security with artificial intelligence. In this research, we have studied artificial intelligence techniques and finalized artificial neural network (ANN) model to detect the intrusions based on anomalies. In previous studies, it was discussed that major challenges in anomaly based intrusion detection systems is to lessen the false positive rate (FPR). So, in this research the main emphasis is done on reducing the failure rate of FPR of the intrusions done to the system while maintaining the overall perfromance and accuracy of the proposed model.

The idea of Internet of Medical Things (IOMT) is used as health intelligence in the smart hospital to assist medical staff for diagnosis and patient care. Smart hospitals play an essential role in digitizing healthcare facilities that could enhance a smart city project's scalability and efficiency. However, in the smart healthcare environment, IoMT devices face high vulnerability. Cyber-security is an essential aspect of a smart city that could achieve a secure environment for smart healthcare. Thus, the Intrusion Detection System (IDS) is used as a protection layer of communication towards cybersecurity for the latest devices and networks systems. In this paper, principal component analysis (PCA) is used for feature reduction and ensemble-based classifiers are used to predict intrusion attacks on the networks. KDDCup-'99' dataset has been employed and performance is evaluated in terms of accuracy, precision, recall and F-score.

Skin cancers primarily malignant melanoma is mortal and tough to recognize in the final stages. To minimize the increasing death rate it is a most essential goal to recognize the skin cancer at its first stage. Skin lesion classification is becoming challenging more and more due to low contrast images. In this research, we propose an intelligent method by implementing the histogram decision to separate the low contrast images into a large amount of dataset. This decision is helpful in the pre-processing stage for the enhancements just in low contrast image either applied into all dataset by avoiding the time complexity. The saliency-based method is applied for lesion segmentation and achieved 95.8 % accuracy. Feature selection is performed by the entropy method after the extraction of deep color and PHOG features. In this research, the SVM classifier is applied on three benchmark datasets ISIB 2016, ISIB 2017 and PH2. Through our proposed fusion feature vector, the best classification results in achieved are 99.5% accuracy on the dataset ISIB2017.

Rendering atmospheric phenomena is known to have its basis in the fields of atmospheric optics and meteorology and is increasingly used in games and movies. Although many researchers have focused on generating and enhancing realistic light shafts, there is still room for improvement in terms of both qualification and quantification. In this paper, a new technique, called ReLiShaft, is presented to generate realistic light shafts for outdoor rendering. In the first step, a realistic light shaft with respect to the sun position and sky colour in any specific location, date and time is constructed in real-time. Then, Hemicube visibility-test radiosity is employed to reveal the effect of a generated sky colour on environments. Two different methods are considered for indoor and outdoor rendering, ray marching based on epipolar sampling for indoor environments, and filtering on regular epipolar of z-partitioning for outdoor environments. Shadow maps and shadow volumes are integrated to consider the computational costs. Through this technique, the light shaft colour is adjusted according to the sky colour in any specific location, date and time. The results show different light shaft colours in different times of day in real-time.

License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi‐views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE‐Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy‐based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.

Mobile technology is very fast growing and incredible, yet there are not much technology development and improvement for Deaf‐mute peoples. Existing mobile applications use sign language as the only option for communication with them. Before our article, no such application (app) that uses the disrupted speech of Deaf‐mutes for the purpose of social connectivity exists in the mobile market. The proposed application, named as vocalizer to mute (V2M), uses automatic speech recognition (ASR) methodology to recognize the speech of Deaf‐mute and convert it into a recognizable form of speech for a normal person. In this work mel frequency cepstral coefficients (MFCC) based features are extracted for each training and testing sample of Deaf‐mute speech. The hidden Markov model toolkit (HTK) is used for the process of speech recognition. The application is also integrated with a 3D avatar for providing visualization support. The avatar is responsible for performing the sign language on behalf of a person with no awareness of Deaf‐mute culture. The prototype application was piloted in social welfare institute for Deaf‐mute children. Participants were 15 children aged between 7 and 13 years. The experimental results show the accuracy of the proposed application as 97.9%. The quantitative and qualitative analysis of results also revealed that face‐to‐face socialization of Deaf‐mute is improved by the intervention of mobile technology. The participants also suggested that the proposed mobile application can act as a voice for them and they can socialize with friends and family by using this app.

Abstract With ceaseless utilization of web and other online advances, it has turned out to be amazingly simple to imitate, discuss, and convey digital material. Subsequently, confirmation and copyright assurance issues have been emerged. Text is the most widely used media of communication on the web as compared with images and videos. The significant part of books, daily papers, websites, commercial, research papers, reports, and numerous different archives are just the plain text. Therefore, copyrights protection of plain text is a critical issue that could not be accepted at all. This paper presents a concise analysis on information hiding techniques and their consequences. Various watermarking systems properties are highlighted along with different types of attacks and their possible defenses. Additionally, the current applications for text document watermarking and the reasons for the problems of text document watermarking are described. Finally, attacks characteristics are discussed including types of attacks, volumes, and nature on text. Current watermarking approaches are reviewed in depth, and consequently, their advantages and weaknesses are highlighted. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract Software security is an essential requirement for software systems. However, recent investigation indicates that many software development methodologies do not explicitly include methods for incorporating information security into the software development life cycles (SDLC). This research investigates, using case study, the methodologies being used in software development in Saudi Arabia and describes a model for integrating security into the SDLC. The aim is to identify the appropriate means of introducing security measures much earlier in the SDLC. This model is designed to be an extension to the existing SDLC. For achieving the research objectives and answering the research questions, the research followed a case study research design in an information‐based organization. The research identified various important elements as security standards, policies, processes being practiced, and tools used within SDLC projects. In this regard, recommendations and verification were gathered to elicit the actual activities that are appropriate to be conducted at each phase of SDLC. The non‐functional security requirements were also found, to the use of fortify and hp alm for source code review and web application testing. Copyright © 2016 John Wiley & Sons, Ltd.