Rice adulteration poses a serious challenge to food safety, consumer trust, and market integrity, with common adulterants such as plastic granules, stones, and inferior rice varieties often mixed with genuine grains. Conventional detection methods are accurate but time-consuming, labor-intensive, and unsuitable for large-scale or real-time applications. This study presents an AI-based framework for rice adulteration detection using machine learning and image processing. High-resolution imaging is employed to extract morphological and textural features of rice grains, followed by classification through algorithms such as SVM, Random Forest, and Convolutional Neural Networks (CNNs). Results indicate that CNN models achieve superior accuracy, precision, and reliability compared to traditional approaches, enabling efficient differentiation of pure and adulterated samples.

The system is designed to be scalable and cost-effective, with potential for integration into IoT or mobile platforms for on-site monitoring. By offering a rapid, automated, and reliable solution, the proposed approach addresses the limitations of conventional methods and demonstrates the transformative role of AI in ensuring food quality and safety.

The primary objective of this project is to monitor soil moisture content under dry and wet conditions using a moisture sensor circuit, compute the corresponding relative humidity, and automate irrigation accordingly. The system integrates a PC-based LabVIEW interface, GSM communication, and an automatic water inlet mechanism to ensure efficient irrigation. In addition to soil moisture, the system also monitors and records environmental parameters such as temperature, humidity, rainfall, and sunlight. These data are continuously updated, stored in a database for backup, and further utilized for weather forecasting and crop planning. The information helps guide farmers in selecting suitable crops for future cultivation based on prevailing soil and climatic conditions. By leveraging IoT technology, the system provides real-time feedback and remote access to operators, ensuring optimized resource utilization. Experimental analysis is carried out on different soil types suitable for various crops under diverse climatic parameters, enabling high-frequency data collection with minimal labor. Overall, the proposed IoT-based smart irrigation framework enhances plant growth, improves crop yield, and supports sustainable agricultural practices.

This paper presents a comprehensive study of an advanced hybrid Free Space Optics (FSO) and Radio Frequency (RF) airborne communication system designed to ensure reliable, high-capacity data transfer in varying atmospheric conditions. By integrating a weather-dependent link switching strategy and Unmanned Aerial Vehicle (UAV) relays, the proposed system overcomes line-of-sight blockages and mitigates the degradation effects of fog, rain, and cloud coverage. Mathematical models for outage probability and bit error rate (BER) are derived, and conceptual simulations are conducted using Python (NumPy and Matplotlib). Results indicate significant performance improvement in link availability and quality when compared to conventional single-link systems.

Over the past decade, the rapid growth of electric vehicles (EVs) has been fueled by significant advancements in battery technologies and Battery Management Systems (BMS). This study provides a comprehensive evaluation of EV battery chemistries, particularly lithium-ion variants, supported by real-world case studies and technical data. Charging and discharging parameters, safety issues such as thermal runaway and fire hazards, and the evolution of BMS architectures are analyzed in detail. Benchmarks for voltage, current, temperature, cycle life, and cost are reviewed to present a practical understanding of battery performance. The paper further examines engineering solutions for cell balancing, thermal management, and fault diagnosis, while highlighting the potential of emerging technologies including solid-state batteries, polysulfide-iodide redox flow batteries, and AI-integrated BMS. Special attention is given to challenges in fast charging, state-of-health prediction, and lifecycle management. Finally, the study emphasizes research gaps and future directions, including artificial intelligence, cloud-based connectivity, and second-life strategies, positioning EV batteries and BMS as critical enablers for safe, efficient, and sustainable mobility.

This research examines the psychosocial factors shaping consumer purchase intentions toward electric two-wheelers (E2Ws) in Kerala. Survey data from 170 respondents across 14 variables spanning policy, economic, cognitive, and social dimensions were analyzed using Hayes’ PROCESS macro. Results highlight perceived social benefits as a significant mediating factor in adoption decisions. Interestingly, household financial capacity was found not to moderate the relationship between cost and purchase intention, suggesting that price sensitivity is uniform across income groups. These findings imply that policy interventions should move beyond an exclusive focus on environmental messaging. Instead, strategies emphasizing social prestige and recognition associated with electric vehicle ownership may prove more effective in encouraging adoption. Leveraging social influence mechanisms could therefore accelerate E2W penetration in Kerala and provide a replicable framework for promoting sustainable mobility in similar socio-cultural settings across the developing world.

Distribution transformers serve as critical components in electrical power distribution systems, directly affecting service reliability and grid stability. With the increasing deployment of smart grid technologies and distributed energy resources, the need for advanced health monitoring techniques has become paramount. This paper presents a comprehensive review of modern diagnostic and prognostic techniques for distribution transformer health assessment, including traditional methods, artificial intelligence approaches, and real-time monitoring systems. The study evaluates various methodologies including health index calculations, fuzzy logic systems, machine learning algorithms, and hybrid approaches. Additionally, this research discusses the integration of Internet of Things (IoT) technologies and advanced metering infrastructure (AMI) for continuous monitoring. The findings reveal that hybrid artificial intelligence approaches combined with real-time monitoring systems offer superior accuracy and reliability for transformer condition assessment, enabling predictive maintenance strategies that can significantly reduce operational costs and improve system reliability.

Mobile phones have changed a lot since the first one came out for everyday use in 1983. These changes are grouped into "generations" based on better tech, features, and ways devices connect. This paper looks at how these generations have grown, beginning with 1G. It used old-style analog signals and a method called FDMA to handle just voice calls at a very slow 2.4 kbps. Next came 2G, which switched to digital signals with techniques like TDMA and CDMA. This allowed text messages (SMS), simple data sharing through things like GPRS and EDGE, and better use of signals up to 64 kbps. 3G brought big improvements with WCDMA, reaching speeds of up to 2 Mbps. People could make video calls, surf the web faster, check emails, and use basic apps, making internet on phones more common. 4G stepped up with LTE and WiMAX, using internet-like IP setups for speeds from 100 Mbps to 1 Gbps. It made smooth video streaming, online games, cloud storage, and doing multiple tasks easy. Today, 5G from the 3GPP group gives super low wait times, speeds of 10-100 Gbps, and stronger safety features. But it has issues like using more battery and needing expensive setups. In the future, 6G could mix in satellites for worldwide reach, tiny 1-microsecond delays, and super high frequencies in the terahertz range. 7G might go further with smooth connections in space, satellite orbits, and blazing fast data using OFDM and FEC for things like real-time HD video shares. The paper compares things like speed, signal sharing methods, main networks, and switching between connections. It also talks about problems, like weak security in old versions and big hurdles for new ones still in testing.