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Lung cancer emerges as a major factor in cancer-related fatalities in the current generation, and it is predicted to continue having a long-term impact. Detecting symptoms early becomes crucial for effective treatment, underscoring innovative therapy's necessity. Many researchers have conducted extensive work in this area, yet challenges such as high false-positive rates and achieving high accuracy in detection continue to complicate accurate diagnosis. In this research, we aim to develop an ecologically considerate lung cancer therapy prototype model that maximizes resource utilization by leveraging recent advancements in computational intelligence. We also propose an Internet of Medical Things (IoMT)-based, consumer-focused integrated framework to implement the suggested approach, providing patients with appropriate care. Our proposed method employs Logistic Regression, MLP Classifier, Gaussian NB Classifier, and Intelligent Feature Selection using K-Means and Fuzzy Logic to enhance detection procedures in lung cancer dataset. Additionally, ensemble learning is incorporated through a voting classifier. The proposed model's effectiveness is improved through hyperparameter tuning via grid search. The proposed model's performance is demonstrated through comparative analysis with existing NB, J48, and SVM approaches, achieving a 98.50% accuracy rate. The efficiency gains from this approach have the potential to save a significant amount of time and cost. This study underscores the potential of computational intelligence and IoMT in developing effective, resource-efficient lung cancer therapies. [ABSTRACT FROM AUTHOR]

Simple Summary: Skin diseases can be serious, and early detection is key to effective treatment. Unfortunately, the quality of images used to diagnose these diseases often suffers due to interference from hair, making accurate diagnosis challenging. This research introduces a novel technology, the DM–AHR, a self-supervised conditional diffusion model designed specifically to generate clear, hairless images for better skin disease diagnosis. Our work not only presents a new, advanced model that expertly identifies and removes hair from dermoscopic images but also introduces a specialized dataset, DERMAHAIR, to further research and improve diagnostic processes. The enhancements in image quality provided by DM–AHR significantly improve the accuracy of skin disease diagnoses, and it promises to be a valuable tool in medical imaging. Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM–AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM–AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM–AHR holds considerable promise for broader application in medical image enhancement. [ABSTRACT FROM AUTHOR]

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks. [ABSTRACT FROM AUTHOR]

In transcranial magnetic stimulation (TMS) and electroencephalography (EEG) experiments, two researchers typically collaborate in the lab. This study addresses the challenge a single researcher faces in managing the TMS experiment's timing while operating the TMS coil. It introduces the Arduino Trigger Generator (ArTGen) to remotely control the timing of TMS experiments using a footswitch pedal. Moreover, a bespoke printed circuit board (PCB) is designed to interface the eegoMylab amplifier with off‐the‐shelf EEG caps. The ArTGen facilitates accurate timing of the TMS stimulator's inter‐pulse intervals (IPIs) through a footswitch pedal, enhancing researchers' control over TMS‐EEG experiments. The PCB interface provides a cost‐effective tool to extend the functionality of the eegoMylab amplifier. The integration of our PCB interface has been validated in a custom TMS‐EEG setup by analyzing TMS‐evoked potentials (TEPs), global mean field power (GMFP), butterfly plots, and event‐related spectral potentials (ERSPs). The PCB reliably preserved EEG signal integrity, ensuring accurate data acquisition. Thorough channel‐wise consistency checks across components confirmed data accuracy. ArTGen's portability and footswitch feature streamline experimental control, aiding TMS‐EEG research and clinical applications. Moreover, our PCB resolves compatibility between the eegoMylab amplifier and the Waveguard EEG cap by extending the amplifier to connect to off‐the‐shelf EEG caps. The ArTGen serves as a robust remote control tool for TMS stimulators, while our PCB interface presents a solution for integrating a customized TMS‐EEG setup. This study addresses the gap in existing TMS‐EEG research by introducing innovative technological enhancements that not only augment experimental flexibility but also streamline procedural workflows. [ABSTRACT FROM AUTHOR]

Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats. [ABSTRACT FROM AUTHOR]

This paper presents a computational approach for quantifying soybean defects through seed classification using deep learning techniques. To differentiate between good and defective soybean seeds quickly and accurately, we introduce a lightweight soybean seed defect identification network (SSDINet). Initially, the labeled soybean seed dataset is developed and processed through the proposed seed contour detection (SCD) algorithm, which enhances the quality of soybean seed images and performs segmentation, followed by SSDINet. The classification network, SSDINet, consists of a convolutional neural network, depthwise convolution blocks, and squeeze-and-excitation blocks, making the network lightweight, faster, and more accurate than other state-of-the-art approaches. Experimental results demonstrate that SSDINet achieved the highest accuracy, of 98.64%, with 1.15 M parameters in 4.70 ms, surpassing existing state-of-the-art models. This research contributes to advancing deep learning techniques in agricultural applications and offers insights into the practical implementation of seed classification systems for quality control in the soybean industry. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio. [ABSTRACT FROM AUTHOR]

This paper introduces a novel semicircular distribution obtained by applying the quadratic rank transmutation map to the stereographic semicircular exponential distribution, referred to as the transmuted stereographic semicircular exponential distribution (TSSCED). This newly proposed distribution exhibits enhanced flexibility compared to the baseline stereographic semicircular exponential distribution (SSCEXP). We conduct a comprehensive analysis of the model's properties and demonstrate its efficacy in data modeling through the application to a real dataset. [ABSTRACT FROM AUTHOR]

Intrusion detection systems, also known as IDSs, are widely regarded as one of the most essential components of an organization's network security. This is because IDSs serve as the organization's first line of defense against several cyberattacks and are accountable for accurately detecting any possible network intrusions. Several implementations of IDSs accomplish the detection of potential threats throughout flow-based network traffic analysis. Traditional IDSs frequently struggle to provide accurate real-time intrusion detection while keeping up with the changing landscape of threat. Innovative methods used to improve IDSs' performance in network traffic analysis are urgently needed to overcome these drawbacks. In this study, we introduced a model called a deep neural decision forest (DNDF), which allows the enhancement of classification trees with the power of deep networks to learn data representations. We essentially utilized the CICIDS 2017 dataset for network traffic analysis and extended our experiments to evaluate the DNDF model's performance on two additional datasets: CICIDS 2018 and a custom network traffic dataset. Our findings showed that DNDF, a combination of deep neural networks and decision forests, outperformed reference approaches with a remarkable precision of 99.96% by using the CICIDS 2017 dataset while creating latent representations in deep layers. This success can be attributed to improved feature representation, model optimization, and resilience to noisy and unbalanced input data, emphasizing DNDF's capabilities in intrusion detection and network security solutions. [ABSTRACT FROM AUTHOR]

This work presents a highly accurate method for the numerical solution of the advection–diffusion equation of fractional order. In our proposed method, we apply the Laplace transform to handle the time-fractional derivative and utilize the Chebyshev spectral collocation method for spatial discretization. The primary motivation for using the Laplace transform is its ability to avoid the classical time-stepping scheme and overcome the adverse effects of time steps on numerical accuracy and stability. Our method comprises three primary steps: (i) reducing the time-dependent equation to a time-independent equation via the Laplace transform, (ii) employing the Chebyshev spectral collocation method to approximate the solution of the transformed equation, and (iii) numerically inverting the Laplace transform. We discuss the convergence and stability of the method and assess its accuracy and efficiency by solving various problems in two dimensions. [ABSTRACT FROM AUTHOR]

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N ×M ×3 to P×Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption. [ABSTRACT FROM AUTHOR]

The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models' performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models' performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and γ for SVM and α and β for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%. [ABSTRACT FROM AUTHOR]

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