Recent Developments in AI and ML for IoT: A Systematic Literature Review on LoRaWAN Energy Efficiency and Performance Optimization.

The field of the Internet of Things (IoT) is dominating various areas of technology. As the number of devices has increased, there is a need for efficient communication with low resource consumption and energy efficiency. Low Power Wide Area Networks (LPWANs) have emerged as a transformative technology for the IoT as they provide long-range communication capabilities with low power consumption. Among the various LPWAN technologies, Long Range Wide Area Networks (LoRaWAN) are widely adopted due to their open standard architecture, which supports secure, bi-directional communication and is particularly effective in outdoor and complex urban environments. This technology is helpful in enabling a variety of IoT applications that require wide coverage and long battery life, such as smart cities, industrial IoT, and environmental monitoring. The integration of Machine Leaning (ML) and Artificial Intelligence (AI) into LoRaWAN operations has further enhanced its capability and particularly optimized resource allocation and energy efficiency. This systematic literature review provides a comprehensive examination of the integration of ML and AI technologies in the optimization of LPWANs, with a specific focus on LoRaWAN. This review follows the PRISMA model and systematically synthesizes current research to highlight how ML and AI enhance operational efficiency, particularly in terms of energy consumption, resource management, and network stability. The SLR aims to review the key methods and techniques that are used in state-of-the-art LoRaWAN to enhance the overall network performance. We identified 25 relevant primary studies. The study provides an analysis of key findings based on research questions on how various LoRaWAN parameters are optimized through advanced ML, DL, and RL techniques to achieve optimized performance.

Statistical analysis in cellular systems for channel capacity improvement with dynamic pilots across different angles users.

Accurate channel state information (CSI) is crucial for optimizing wireless communication systems. In scenarios with varying user-to-base station angles, the angle-dependent coherence time impacts conventional pilot strategies. Due to small angles, the coherence time of the user decreases dramatically because of doppler shift, which causes an increase in the number of pilots. We introduces an innovative sub-block design approach for systems with different user angles. This method harmonizes coherence time of high and low-angle users, while maintaining a constant pilot count. This not only improves spectral efficiency but also ensures accurate channel estimation. Through simulations, we demonstrate the effectiveness of our approach in enhancing both spectral efficiency upt to 10 % and CSI precision. This breakthrough contributes to the advancement of channel estimation techniques in scenarios with angle-dependent coherence time, offering practical benefits to wireless communication systems.

A novel simulation-based analysis of a stochastic HIV model with the time delay using high order spectral collocation technique.

The economic impact of Human Immunodeficiency Virus (HIV) goes beyond individual levels and it has a significant influence on communities and nations worldwide. Studying the transmission patterns in HIV dynamics is crucial for understanding the tracking behavior and informing policymakers about the possible control of this viral infection. Various approaches have been adopted to explore how the virus interacts with the immune system. Models involving differential equations with delays have become prevalent across various scientific and technical domains over the past few decades. In this study, we present a novel mathematical model comprising a system of delay differential equations to describe the dynamics of intramural HIV infection. The model characterizes three distinct cell sub-populations and the HIV virus. By incorporating time delay between the viral entry into target cells and the subsequent production of new virions, our model provides a comprehensive understanding of the infection process. Our study focuses on investigating the stability of two crucial equilibrium states the infection-free and endemic equilibriums. To analyze the infection-free equilibrium, we utilize the LaSalle invariance principle. Further, we prove that if reproduction is less than unity, the disease free equilibrium is locally and globally asymptotically stable. To ensure numerical accuracy and preservation of essential properties from the continuous mathematical model, we use a spectral scheme having a higher-order accuracy. This scheme effectively captures the underlying dynamics and enables efficient numerical simulations.

Brain Tumor Segmentation Using Deep Learning on MRI Images.

Brain tumor (BT) diagnosis is a lengthy process, and great skill and expertise are required from radiologists. As the number of patients has expanded, so has the amount of data to be processed, making previous techniques both costly and ineffective. Many academics have examined a range of reliable and quick techniques for identifying and categorizing BTs. Recently, deep learning (DL) methods have gained popularity for creating computer algorithms that can quickly and reliably diagnose or segment BTs. To identify BTs in medical images, DL permits a pre-trained convolutional neural network (CNN) model. The suggested magnetic resonance imaging (MRI) images of BTs are included in the BT segmentation dataset, which was created as a benchmark for developing and evaluating algorithms for BT segmentation and diagnosis. There are 335 annotated MRI images in the collection. For the purpose of developing and testing BT segmentation and diagnosis algorithms, the brain tumor segmentation (BraTS) dataset was produced. A deep CNN was also utilized in the model-building process for segmenting BTs using the BraTS dataset. To train the model, a categorical cross-entropy loss function and an optimizer, such as Adam, were employed. Finally, the model's output successfully identified and segmented BTs in the dataset, attaining a validation accuracy of 98%.

A framework for brain tumor detection based on segmentation and features fusion using MRI images.

Irregular growth of cells in the skull is recognized as a brain tumor that can have two types such as benign and malignant. There exist various methods which are used by oncologists to assess the existence of brain tumors such as blood tests or visual assessments. Moreover, the noninvasive magnetic resonance imaging (MRI) technique without ionizing radiation has been commonly utilized for diagnosis. However, the segmentation in 3-dimensional MRI is time-consuming and the outcomes mainly depend on the operator's experience. Therefore, a novel and robust automated brain tumor detector has been suggested based on segmentation and fusion of features. To improve the localization results, we pre-processed the images using Gaussian Filter (GF), and SynthStrip: a tool for brain skull stripping. We utilized two known benchmarks for training and testing i.e., Figshare and Harvard. The proposed methodology attained 99.8% accuracy, 99.3% recall, 99.4% precision, 99.5% F1 score, and 0.989 AUC. We performed the comparative analysis of our approach with prevailing DL, classical, and segmentation-based approaches. Additionally, we also performed the cross-validation using Harvard dataset attaining 99.3% identification accuracy. The outcomes exhibit that our approach offers significant outcomes than existing methods and outperforms them.

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition.

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Insight into the photocatalytic properties of diatomite@Ni/NiO composite for effective photo-degradation of malachite green dye and photo-reduction of Cr (VI) under visible light.

Diatomite frustules decorated by nano Ni/NiO nanoparticles (Diatomite@Ni/NiO) were synthesized as a novel photocatalyst for effective degradation of malachite green cationic dye (M.G) and photocatalytic-reduction of Cr (VI) ions. The composite was characterized by different analytical techniques and revealed enhancing in the surface area (400 m2/g), 5.8 nm as average pore diameter and showed lower band gap energy (1.71 eV) than NiO as single phase. The photocatalytic activity of the composite in the removal of M.G and reduction of Cr (VI) was evaluated under visible light considering the pH, illumination time, catalyst mass, and the pollutants concentrations. The results revealed complete removal of 25 mg/L M.G can be achieved using 20 mg, 30 mg, 40 mg and 50 mg of the after 150 min, 90 min, 60 min, and 30 min, respectively. The complete degradation of 50 mg/L can be obtained after 240 min, 90 min, and 60 min using 20 mg, 40 mg, and 50 mg of the catalyst, respectively. This also was reported for the photocatlytic-reduction of 25 mg/L of Cr(VI) ions as the complete reduction was estimated after 180 min, 60 min and 30 min using 20 mg, 40 mg, and 50 mg, respectively. Also, 50 mg/L of Cr (VI) can be completely reduced after 240 min, 90 min, and 60 min using 20 mg, 40 mg, and 50 mg as catalyst dosage, respectively. The photocatalytic degradation of M.G controlled mainly by the generated electron-hole pairs and the superoxide species while the photocatalytic-reduction of Cr (VI) controlled mainly by the directly excited electrons of Ni/NiO and partially by the formed superoxide radicals. Hence, the synthetic diatomite@Ni/NiO composite can be considered as potential photocatalyst in the degradation of M.G dye and photoreduction of Cr (VI) ions.

Facile synthesis of bentonite/biopolymer composites as low-cost carriers for 5-fluorouracil drug; equilibrium studies and pharmacokinetic behavior.

Two types of bentonite/biopolymer composites (bentonite/chitosan (BE/CH) and bentonite/Co-Poly 2-hydroxyethyl methacrylate-methyl methacrylate (BE/HEMA-MMA)) were synthesized after modification of bentonite by an organic surfactant (BE/CTAB). The products were characterized as low-cost carriers for the 5-fluorouracil drug of high loading properties and controlled releasing behavior. The experimental loading results revealed the suitability of BE, BE/CTAB, BE/CH and BE/HEMA-MMA to load 114 mg/g, 230 mg/g, 273 mg/g, and 310 mg/g, respectively. The loading behaviors of BE/CTAB, BE/CH, and BE/HEMA-MMA are of excellent fitting with the Langmuir model. The adsorption energies and the thermodynamic studies revealed a physisorption mechanism (coulombic attractive forces) for the drug molecules. The thermodynamic parameters reflected spontaneous loading reactions of endothermic nature. The releasing profile showed significant enhancement with the formation of bentonite/biopolymer composites to extend for 160 h without attending the complete release either in the intestinal fluid (pH 7.4) or the gastric fluid (pH 1.2) with a preference for BE/HEMA-MMA composite. The inspected pharmacokinetics reflected erosion mechanism for the releasing of 5FU from BE/HEMA-MMA and the releasing properties of it from BE/CTAB and BE/CH controlled by a combination of erosion and diffusion mechanisms.