José Gabriel Carrasco Ramirez, CEO at Quarks Advantage, Jersey City, New Jersey. United States of America
The creation of proprietary frameworks for the development of neural models is essential to meet specific needs that generic frameworks cannot address. This article examines the key stages in the design of these frameworks and offers best practices for their effective implementation. It explores everything from needs identification and resource assessment to architectural design and implementation. Additionally, it emphasizes the importance of user-centered design and continuous evaluation to ensure the frameworks usability and adaptability to changing needs.
Proprietary frameworks, neural models, artificial intelligence, framework design, model optimization, user-centered design, continuous evaluation, scalability, performance optimization, data management, model training, regulatory compliance, explainable AI (XAI), agile methodology, security and privacy.
Kruti Shah, Preksha Patel, Varenya Uchil, and Prof. Pankaj Sonawane, SVKM‘s Dwarkadas J Sanghvi College of Engineering, Mumbai- 400056, India
The infestations of pests cause big losses in crops, both in quantity and quality. Present-day pest management systems are imprecise as they often rely on the traditional traps that give wrong data which can result in the incorrect estimates of infestation levels. Optimal trap placement is one of the requirements for traditional methods so as to enhance the accuracy of the monitoring data and hence cut down the overall costs. This chapter proposes the idea of “Ecobot: an IoT-based pest detection and eradication system” with ESP32-CAM for visual detection and infrared sensors for motion detection. It also includes INMP441 MEMS microphones for audio detection, alongside artificial neural networks for sound classification in ResNet to recognize images for accurate pest identification. Ecobot stands out due to its intelligent trapping system, which incorporates pheromone lures to attract pests effectively while minimizing pesticide use and handling large quantities without compromising trapping efficacy. Real-time alerts are sent across with the help of a Telegram bot to facilitate timely intervention with a substantial reduction in crop loss. The system can aid in improvement of accuracy in real-time monitoring and reduction in crop damage. It is well ahead of conventional methods in efficiency, scalability, and cost-effectiveness.
Sustainable, IOT, ANN, ResNet, ESP32, Pest Detection, Intelligent Trap System.
Anastazja Drapata, University of Warsaw
AI is a technology of significant importance for use in the space and defense industry thanks to its features such as the ability to learn quickly and precision, which allows to achieve efficiency at a higher level than with the use of traditional weapons.The use of AI in the construction of space weapons is a controversial matter due to the choice of the level of regulation and the high risk of error, which may give rise to international liability.The increasing militarization of space raises ethical and legal doubts about the use of AI as a base technology for the production of space weapons, violating the purposes of using space set out in the 1967 Space Treaty, and also requires the adoption of legal regulations regulating its proper use in space research.Therefore, an analysis of AI applications in the production of space weapons was conducted.In order to reconstruct the legal norms governing the permissible use of AI, traditional legal inference, linguistic interpretation and the comparative method were used, referring to diplomatic documents and regulations issued by UN bodies.
AI, militarization of space, space, space weapons .
Akshaykumar Wankhade and Prema M. Diagavane, Research Scholar, Department of Electrical Engineering, GHRU, Amravati, (M.S.), India
In various applications, such as electric cars, energy storage devices, and portable gadgets, accurately measuring the current charge level is essential to safely administering and operating lithium-ion batteries. This investigation introduces an exhaustive audit of practical contemplates that have been led to validate the precision, dependability, and robustness of different SOC appraisal techniques under varying activity conditions, comprising altering charge/release rates, temperature profiles, and battery maturing impacts. The survey examines the execution of procedures, such as extended Kalman channels, particle channels, and adaptive onlookers, which have been embraced to address lithium-ion battery elements nonlinear and time-fluctuating nature. Additionally, the paper examines the criticalness of creating physics-based models that can get a handle on the underlying electrochemical cycles inside the battery and how these models can be incorporated with propelled appraisal calculations to improve SOC figure precision. The discoveries from this audit highlight the need for broad practical approval of SOC appraisal techniques in practical activity situations and the potential for additional upgrades through versatile and information-driven methodologies that can account for the unpredictable conduct of lithium-ion batteries.
State-of-charge appraisal, lithium-ion batteries, practical approval, activity conditions, versatile estimation, physics-based demonstrating.
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