In the contemporary context, non-volatile-based Resistive Random-Access Memory devices are one of the most promising and emerging technologies. It is one of the top alternatives for industrialists since they are considered to offer neuromorphic in-computing capacity. This study aims to build a 2T2R RRAM cell with reduced power dissipation compared ...

By Ancy Joy, Jinsa Kuruvilla

This paper derives a novel direct model predictive based power controller (DMPPC) for renewable energy systems (RES) to address voltage fluctuations caused by varying power demands and renewable source outputs. This method utilizes the bi-directional DC-DC converter in the Battery Energy Storage (BES) System to level the renewable energy output, al...

By P. Sai Sampath Kumar, P. Suresh, D. Lenine

Inclusive education aims to provide all children, irrespective of their capabilities, with equal opportunities to learn and participate meaningfully. For students who have speech and hearing disabilities, conventional classrooms often act as communication barriers which inhibit their participation and academic advancement. This study focuses on the...

By Akbarbek Allashev, Muyassar Akhmedova, Diyora Karimova, Shakhnoza Khujamberdieva, Sayyora Davidova, Nasiba Bayturaeva, Manzila Khabibova, Dilfuza Abdullayeva

Terrain Type Identification is an essential aspect of environmental monitoring, urban planning, and resource management. This study discusses a comparative investigation of the effectiveness of three Convolutional Neural Network (CNN) architectures Simple CNN, ResNet-50, and the Enhanced Efficient CNN Model which is Efficient Net in training with t...

By V. Sahaya Sakila, R. Sujeetha, S.V. Ramanathan, M. Adhithya Raj, B. Philip Regin

Even though agriculture is now one of the most technologically advanced civilian industries, there is still a big gap between the innovations that are available and how they are actually put into practice, particularly in areas where traditional supervision methods are predominant. This study looks at how farmers' ability to implement precision agr...

By Shoira Bobomuratova, Kurbonalijon Zokirov, Najimiddin Jumakulov, G'ofur Allamuratov, Oybek Ulugbekov, Muhabbat Mullajonova, Avazbek Turdunov

The detection of stroke is vital since any delay in diagnosis may lead to significant disability or the loss of life. The existing predictive models fail to capture stroke symptoms with accuracy because of low complexity, and the ability to be used in the real-time situation in the clinical setting. In the following paper, an AI-based system of ear...

By D. Ushasree, A.V. Praveen Krishna, Ch. Mallikarjuna Rao, D.V. Lalita Parameswari

Predictive maintenance has become an important factor in improving the reliability and efficiency of industrial robots in the evolving environment of smart manufacturing. The proposed paper is a predictive maintenance framework based on AI to be implemented to multi-sensor industrial robots that will be used in a smart manufacturing setting. The po...

By Priya Vij, Ashu Nayak

The world aerospace industry is now facing an important shift from traditional metallic structures to high-tech advanced composite materials in order to meet the two-fold needs of structural optimization and environmental sustainability. This study examines the evolution of high-performance composite materials, particularly carbon fiber reinforced ...

By Ravinder Sharma, Shyam Maurya

Purpose- The main objective of the proposed paper is to create and implement a real-time wearable health monitoring system based on IoT, i.e., Oxy Sense-Wear, that will enable the constant control of the main physiological parameters, such as ECG, EMG, SpO2, body temperature, and physical activity. The system is aimed at long-term surveillance of t...

By M.N. Vimal Kumar, M. Pravin Kumar, Baskar Duraisamy, A.K. Jaithunbi, P. Samson Peter, V.M. Thejashri

Hazardous air pollutants (HAPs) can be a critical risk to the sustainability of the environment and human health, which must be addressed by highly sophisticated predictive models to eliminate risks successfully. In this study, the researcher presents a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with the ability t...

By K. Muralisankar, G. Balaji, C. Ramkumar, M. Vasuki, S. Vijayananthan, D. Angayarkanni, Mohammed Aslam, M. Narmatha