Visible Light Communication (VLC) has the potential to advance Intelligent Transportation Systems (ITS). This study explores the current advancements of VLC in ITS applications that may enhance traffic flow, road safety, and vehicular communication performance. The potential, benefits, and current research trends of VLC in ITS applications are discussed first. Then, the state-of-the-art VLC technologies including overall concept, IEEE communication protocols, hybrid VLC systems, and software-defined adaptive MIMO VLC systems, are discussed. We investigated different potential applications of VLC in ITS, such as signalized intersection and ramp metering control, collision warning and avoidance, vehicle localization and detection, and vehicle platooning using vehicle–vehicle (V2V), infrastructure–vehicle (I2V), and vehicle–everything (V2X) communications. Besides, VLC faces several challenges in ITS applications, and these concerns, e.g., environmental issues, communication range issues, standards and infrastructure integration issues, light conditions and integration issues are discussed. Finally, this paper discusses various advanced techniques to enhance VLC performance in ITS applications, such as machine learning-based channel estimation, adaptive beamforming, robust modulation schemes, and hybrid VLC integration. With this review, the authors aim to inform academics, engineers, and policymakers about the status and challenges of VLC in ITS. It is expected that, by applying VLC in ITS, mobility will be safer, more efficient, and sustainable.

Free Space Optics (FSO) is a highly viable solution for high-speed wireless communication and is widely preferred over radio frequency communication systems because of its faster data transmission, no regulatory requirements and highly secure long-range operations. However, the capacity and availability of FSO optical bands are a significant concern in varying atmospheric conditions. Our objective is to enhance network flexibility and expand wireless network coverage in adverse weather conditions by combining optical and FSO links using optical bands C, S, and O. The study analyzed the performance of a hybrid 4 channels FSO-WDM system with a 100GHz or 0.8 nm channel spacing under different conditions, including adverse weather and varying data rates. An attenuation of 0.25 dB/km was fixed, and the system's performance was analyzed up to 3 km. The results showed that as the data rate increased, the system's performance declined, and the O band was the best performer up to 25 Gbit/s. BER values were analyzed at different weather conditions using the Kim model, and the O band consistently outperformed the S and C bands. Eye diagrams were used to evaluate the signal quality, and the O band was shown to perform better than the other two bands, even in adverse weather conditions. Overall, the study suggests that FSO is a viable solution for high-speed wireless communication, particularly when using the O band.

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices.

Visible light communication(VLC)has gained attention for enabling gigabitdata transmission over a short-range. Inradio over fiber (RoF), modulatedradio frequency (RF) is carried over optical fiber. Millimeter-wave (mmW)range also offers a vast amount of spectrum and enables integration with RoF.We propose a novel hybrid network using mmWave based RoF backhaulandcoarse wavelength division multiplexed(CWDM)-VLC for indoorcommunication. Three different optical tones were introduced to produce thedesired mmW signal using optical heterodyning with one of them carryingmodulated data and the other two carrying unmodulated data. Opticalsideband signal with the carrier(OSSB+C) is used for uplink communication.Modulated mmWave signal is used for VLC downlink to drive a multi-colorCWDM system. The performance of the VLC downlink is measured usingdifferent optical filters such as Bessel, Trapezoidal, Gaussian, and FabryPerot. A maximum data rate of 2.64 Gb/s and 6.58 Gb/s were achieved with10 and 20channelsoff the shelf LEDs with 16quadrature amplitudemodulation (QAM) with reasonable BER for downlink VLC communication.The uplink communication was carried out using mmW with 2.5 Gb/s datarate usingon-off keying(OOK)modulation and the data from thiscommunication down converted atcentral office(CO) through RoF backhaul.

Kidney disease is a major global health concern that requires timely diagnosis and effective monitoring to prevent severe complications and improve patient outcomes. This data article presents BD-KDD, a structured clinical dataset designed to facilitate research on kidney disease diagnosis. The dataset was collected retrospectively from electronic medical records obtained from Popular Diagnostic Center, Savar Branch, Dhaka, Bangladesh, following institutional authorization for academic research. The BD-KDD dataset contains 988 patient records with 26 variables, including demographic attributes, physiological measurements, biochemical laboratory tests, urinalysis indicators, hematological parameters, comorbidity indicators, and clinical symptoms. Key laboratory features include serum creatinine, blood urea, blood glucose, sodium, potassium, hemoglobin, packed cell volume, red blood cell count, and white blood cell count, along with urinalysis indicators such as specific gravity, albumin, and sugar levels. Each record is assigned a binary diagnostic label representing either healthy individuals or kidney disease cases based on clinical evaluation and laboratory findings. The curated dataset includes 481 healthy and 507 kidney disease cases and is provided in CSV format together with a dataset dictionary describing variable definitions and coding schemes. BD-KDD offers a valuable resource for biomedical data analysis, health informatics research, and the development of machine learning–based diagnostic models and clinical decision support systems for renal health assessment.