The rapid expansion of highways in desert regions has resulted in an increase in camel-vehicle collisions, leading to substantial human, economic, and animal welfare impacts. Despite the success of virtual fencing in managing livestock such as cattle, goats, and sheep, its application for camels remains largely unexplored. This paper introduces an innovative global positioning system (GPS)-enabled virtual fencing prototype that leverages non-invasive auditory cues and real-time monitoring to control camel movements. The system integrates advanced geofencing algorithms, a random forest machine learning classifier trained on accelerometer data with an accuracy of 92% for activity recognition, and long range (LoRa) communication for reliable long-range data transmission. Field tests on camels demonstrate that auditory signals at 2000 Hz effectively deter the camels from crossing virtual boundaries after repeated interactions. The system maintains robust performance, achieving GPS positional accuracy of 8.08 meters and ensuring effective communication over distances up to 1.5 km. This study offers a significant contribution to the fields of wireless communication and Internet of Things (IoT)-based animal management systems.

Diabetic retinopathy (DR), a leading cause of vision loss among individuals with diabetes, necessitates accurate and timely diagnosis for effective management. This paper evaluates two classification models: the gray-level co-occurrence matrix (GLCM) and the convolutional neural network (CNN) ResNet-50 architecture, for automated DR diagnosis. The study employs retinal images from Kaggle and Zenodo datasets, assesses model performance, and optimizes the ResNet-50 parameters to enhance classification accuracy. The results demonstrate the superior performance of ResNet-50 compared to GLCM. The achieved accuracies for distinguishing normal and diabetic retinal images are $\mathbf{9 7. 8 8 9 \%}$ and $\mathbf{9 2. 0 5 3} \%$, based on Kaggle and Zenodo datasets, respectively. This indicates a robust performance of ResNet- 50 in multi-class classification tasks and highlights its potential for improving DR diagnosis systems. These findings underscore the significance of advanced computational techniques in early DR detection, offering enhanced diagnostic efficiency and potentially alleviating healthcare burdens.

This research explores the latest advancements in channel estimation, precoding, and detection techniques within massive multiple-input multiple-output (MIMO) systems, which are crucial for the evolution of fifth-generation (5G) and beyond. As global data traffic surges, traditional methodologies face significant limitations, necessitating innovative approaches to enhance performance. This paper critically examines how these advanced techniques effectively address challenges such as increased spectral efficiency and reduced latency while significantly improving overall signal processing efficiency. This work presents practical applications of these methodologies, showcasing a detailed analysis of novel signal detection algorithms designed to maximize system performance in real-world scenarios. By leveraging state-of-the-art signal processing frameworks, it is demonstrated how these techniques enhance detection accuracy and optimize resource allocation, ensuring robust communication in dense user environments. Ultimately, this study underscores the transformative potential of massive MIMO in revolutionizing wireless communications. The findings offer critical insights and practical guidelines that contribute to advancing telecommunications infrastructure, equipping stakeholders to meet the dynamic demands of next-generation wireless networks. This research aims to inspire further exploration and development in this rapidly evolving field, establishing massive MIMO as a cornerstone of future connectivity solutions.

The advent of fifth-generation (5G) wireless communication systems is reshaping the connectivity landscape, enabling unprecedented speed, reliability, and capacity. Central to this transformation is multiple-input multiple-output (MIMO) technology, which plays a critical role in harnessing the full potential of 5G networks. This research paper offers an in-depth exploration of emerging MIMO technologies, focusing mainly on massive MIMO (MMIMO) as a pivotal case study. This study delves into the fundamental principles that underpin these technologies, examining their substantial benefits, inherent challenges, and diverse applications across various sectors, from smart cities to autonomous systems. Through a thorough review of the existing literature and technical specifications, this paper illuminates recent advancements and highlights key open research questions. Additionally, this work proposes future directions for MIMO technologies, envisioning their vital role in the evolution of 5G and beyond. By addressing these dimensions, this paper aims to contribute to the ongoing discourse in wireless communications and inspire further innovation in the field.

In recent years, the proliferation of data and sensor measurements in various scientific fields, particularly within the realm of the Internet of Things, has opened new avenues for knowledge extraction through advanced data analysis techniques. However, the presence of outliers and anomalies poses significant challenges, leading to inaccuracies that can compromise analytical outcomes. Outliers are defined as data points that deviate markedly from other observations, often resulting from measurement errors or inconsistencies within the dataset. Their detection and removal during the data cleaning process are crucial for enhancing data quality and ensuring robust analysis. This study systematically investigates the impact of outliers and their detection on the accuracy and performance of various machine learning algorithms and statistical models in regression and classification tasks. A series of MATLAB simulations is conducted on standard datasets to evaluate the effects of outliers and validate the performance of different methodologies. The findings highlight the critical importance of effective outlier detection, demonstrating a marked improvement in the accuracy and reliability of analytical results.

Parkinson’s disease (PD) is a progressive neurode-generative disorder that affects millions of individuals worldwide, significantly impairing their quality of life through motor symptoms such as tremors, rigidity, and particularly, Freezing of Gait (FOG). FOG is characterized by transient episodes where patients temporarily lose the ability to initiate or continue walking, posing risks of falls, loss of independence, and psychological distress. This study leverages advancements in machine learning (ML) and explainable artificial intelligence (XAI) to develop and validate a predictive model for FOG. Using a publicly available dataset and employing various ML techniques, the study evaluates the performance of these models in terms of accuracy, precision, recall, and F1-score. Incorporating XAI methods enhances the interpretability of the model, making its predictions more transparent. The results indicate that a 5-second window size with a 5-second pre-FOG period and a Random Forest classifier achieved the highest performance, with an accuracy of $\mathbf{9 9. 3 4 \%}$. Interpretability analyses, including permutation importance and Partial Dependence Plots (PDP), highlight the key features influencing predictions, such as peak frequencies and statistical measures. Future work will focus on model generalizability, exploring additional datasets to improve predictive accuracy and clinical applicability.

Alzheimer’s disease (AD) is a complex neurodegenerative disorder that presents significant challenges for early and accurate diagnosis. Early diagnostic and treatment strategies can help enhance the circumstances by slowing the progression of the illness and enhancing the patient and family’s quality of life. Machine learning (ML) approaches have shown promise in improving the diagnosis and prognosis of Alzheimer’s based on relevant risk factors. This paper aims to develop and evaluate a machine learning model for classifying Alzheimer’s, mild cognitive impairment (MCI), and normal cognition (NC) using a diverse data set from the ADNI database. The model had high performance with a sensitivity rate of up to 97%, accuracy rate of up to 94%, and specificity rate of up to 96%. Moreover, none of the Alzheimer’s cases were falsely detected as normal cognition but as mild cognitive impairment and none of the normal cognition cases were detected as Alzheimer’s, but as mild cognitive impairment. The algorithm that has the highest number of true positive detections, which is 78 out of 85 Alzheimer’s cases, is the decision tree algorithm. The performance of the system heralds a promising future for Alzheimer’s diagnosis by machine learning with the aim of developing smart health systems.

Medication non-adherence is a prevalent concern, particularly among individuals managing chronic illnesses who rely on consistent pill consumption. This study addresses forgetfulness and non-compliance in medication intake by proposing a system that ensures accurate administration of prescribed medications at designated times. This paper investigates medication adherence challenges, primarily focusing on chronic condition management. Leveraging mobile phones, our innovative approach aims to mitigate these challenges. This paper proposes a system that delivers timely reminders to patients via mobile devices, fostering responsibility toward adhering to medication regimens. Central to the proposed solution is a patient-to-hospital communication framework, enabling caregivers to curate medication schedules. Caregivers have control over medications, timings, and dosages. This empowers short-term and long-term medication users to monitor regimens, alleviating concerns of omissions or deviations. Implications of the proposed framework are far-reaching. Consistent medication adherence can enhance therapeutic interventions, potentially reducing morbidity and mortality. The presented technology-healthcare convergence underscores the positive impact of technological interventions in medical contexts.

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios.

Artificial Intelligence is transforming the healthcare industry due to the increasing accessibility of organized and unorganized information and the rapid development of analytical techniques. As artificial intelligence becomes more significant in healthcare, concerns are arising regarding the lack of transparency, explainability, and the possibility of bias in model predictions. The goal of this paper is to utilize interpretable machine learning to provide a better understanding of breast cancer using the Local Interpretable Model-agnostic Method (LIME). This study uses Local Interpretable Model-Agnostic Explanations to explain how the machine-learning model accurately classifies breast cancer cases as either Benign or Malignant. It provides a LIME plot for Benign cases, highlighting the significant role of "Bare Nuclei" where lower values strongly suggest Benign predictions. Other features like Normal Nucleoli, Marginal adhesion, single adhesion, Mitoses, and uniformity of cell size also contribute to the Benign class prediction when they fall below a particular threshold. The study also presents a LIME plot for Malignant cases, emphasizing the importance of "Bare Nuclei" and Clump thickness, where higher values indicate a higher likelihood of Malignant predictions. Other features like Normal Nucleoli, Marginal adhesion, Bland chromatin, uniformity of cell size, and Mitoses also contribute to Malignant predictions when their values exceed specific thresholds. The feature "Concave points_worst" influences the model’s benign predictions when it is below 0.07, while the "texture" feature affects predictions when it exceeds 29.41. Furthermore, the study provides further explanations for Malignant predictions based on "Concave points_worst" and "Texture." These findings provide valuable insights into the decision-making process of the model, making it more interpretable and useful for breast cancer diagnosis.

Animal-Vehicle Accidents have shown deep increase in the middle east regions over the last decades. These collisions resulting from camels fleeing the wildlife and crossing the roads and hence endangering drivers and camel's lives and leading to habitat degradation. Additionality, the size, strength, and the unpredictable behavior of camels play a key role in high mortality rates in the camel-vehicle collisions. Various solutions and countermeasures such as warning signs and fences have been adopted in the past. However, several drawbacks are associated to them, and their effectiveness are reducing with time. Therefore, this study proposes a framework for the use of machine learning approaches and computer vision for the detection and recognition of camels. This can help to provide warning to drivers about potential animal crossings in an effort to mitigate camel-vehicle accidents.

This paper introduces a simple and efficient Microwave Imaging (MWI) setup for detecting fractures in superficial bones, specifically in the tibia. This setup holds promise for the use by first-responders in swiftly assessing fractures in emergency scenarios where X-ray equipment may not be readily available or recommended. The key component of this setup is a single wearable monopole antenna, employed to linearly scan the bone across an ultra-wideband frequency range of 8.5 GHz (3.5-12 GHz), with a maximum gain of 5.7 dBi and an efficiency of more than 90%. The antenna system is designed to fit the human body shape without experiencing any performance degradation as compared to the conventional planar antenna counterpart. The practicality of the proposed antenna is demonstrated through simulations involving a bone model, wherein the distribution of electric fields (E-fields) inside the bone is examined. The reconstructed images resulting from these simulations underscore the potential of this conceptual model as a portable platform for efficiently detecting and pinpointing 1 mm fractures within bones by utilizing the extracted field distributions at 4.7 GHz and at 7.9 GHz.

This paper proposes an almost constant, programmable, and extremely high (T$\Omega$) Complementary MOS pseudo-resistor cell. A theoretical proof is developed and precisely matches the simulation results and confirms the implementation of high resistance, considering the symmetric dynamic range and the simplicity of the realization. Different bio-medical amplifiers as applications of the proposed pseudoresistor are demonstrated to verify the effectiveness of the high linearity performance, lower cutoff frequency controllability, and process variations compensation. The proposed cell and the bio-medical amplifiers are designed and simulated using 90 nm CMOS technology, BSIM4, level 54.

This paper presents a fourth-order elliptic low pass filter for the detection of biopotential signals. The filter has a bandwidth of 24 Hz to be used for the acquisition of Electroencephalogram (EEG) and Electrocardiogram (ECG). An embedded variable gain amplifier provides the filter with variable gain ranging from 0.39 dB to 18 dB. A single notch is positioned at frequency 50 Hz to eliminate the power-line interference signal, with an attenuation of 51 dB achieved. The proposed OTA-C filter structure is simulated in LTspice using 90 nm CMOS model BSIM4 (level 54) under ± 0.6 V voltage supply. The proposed filter has an input referred noise spectral density of 19.6 pV/Hz½ at 10 Hz at DC gain 18 dB. The total harmonic distortion (THD) is 2.45% for a sinusoidal input of 10 mV, 5Hz at DC gain of 6.6 dB. The standby power consumption of the filter ranges between 1.5n nW at a gain of 0.39 dB and 1.7 nW at gain of 18 dB.

In this paper, the generation of fully differential 2ndorder voltage-mode allpass filters is systematically studied and modeled using two-port network techniques. The proposed filters are based only on the generic two transistor common-drain differential pair and a series of surrounding RLC impedances. Five designs of possible allpass filters based on various choices of the surrounding impedances are also presented. Selected designs were verified with experimental results using discrete MOS transistors as proof of concept. One of the proposed filters was simulated in 65-nm CMOS and showed 46GHz delay-bandwidth while the delay can be electronically tuned via the bias current and independent from the pole frequency.

A sixth order complex filter based on the usage of a newly proposed fully differential current conveyor (FDCC) is presented in this paper. The FDCC new structure is based on usage of differential difference operational floating amplifier (DDOFA) and floating current source circuits. The block is realized using 0.25 mum CMOS technology under plusmn1.5 V power supply. PSPICE simulation for the FDCC is done for testing the block. The simulation shows that the FDCC has plusmn0.5 V input dynamic range, 95 MHz 3-dB frequency at output terminal under 10 KOmega load and 7.21 mW total power dissipation. The FDCC is used to realize first order complex filter with 1 MHz center frequency and second order complex filter at 500 KHz center frequency. Finally; using cascading technique, a sixth order complex filter at 500 KHz center frequency is proposed. The proposed filter is suitable for applications like Bluetooth receivers. All the proposed filter circuits are simulated using ADS simulator.

In this paper, a new technique for linearizing long tail differential pair (LTP) is proposed. It is shown that the proposed linearized fully differential transconductor offers excellent linearity. The proposed transconductor is used to design fully differential second order lowpass and bandpass filters suitable for VLSI. PSpice simulation results for the proposed fully differential transconductor and its filter application indicating the linearity range and verifying the analytical results are also given

In this paper, the design and analysis of a CMOS fifth-order low-pass GM-C filter are presented. It has a cutoff frequency of 4.3 MHz to accommodate the wideband CDMA standard. The transconductor used in this filter is based on a four-transistor cell operating in triode or saturation mode. It achieves high linearity range of plusmn 1 V at plusmn 1.5 V supply voltages. PSpice simulations show that total harmonic distortion at 1 Vpp and 1 MHZ is equal to 0.1% with 1.234 mW standby power dissipation. The proposed filter and the transconductor are simulated using 0.35 mum technology