Army Institute of Technology

Massive Multiple Input Multiple Output (MIMO) communication system is created for the fifth-generation (5G) network to improve spectral efficiency and transmission reliability. However, accurate channel estimation remains a critical challenge in massive MIMO systems, particularly due to issues such as pilot contamination and the high-dimensional beamforming complexity associated with large antenna arrays. Despite the recent development of effective estimating approaches, estimation accuracy still has to be increased. In order to estimate channels, this study presents an optimized LSTM system. The Least Square (LS) channel estimation approach is first used to gather historical data on the channel state information (CSI) derived from pilot sequences. These gathered channel answers are used to train the proposed LSTM, in which the squirrel search algorithm (SSA) is used for optimal weight initialization in the most effective manner. By optimizing the initial weights, SSA enhances convergence behavior and avoids poor local minima during training. The trained SSA-LSTM model is subsequently used to predict the current channel response. The recommended transmit-predict strategy's functionality is assessed by changing the pilot sequence. Bit Error Rate (BER) and Mean Square Error (MSE) as functions of Signal-to-Noise Ratio (SNR) were used to assess the performance of SSA-LSTM model. In order to compare various channel estimate methods, the studies altered the SNR (usually between 5 and 50 dB), QAM modulation order (128, 256, 512, and 1024 QAM), and number of transmit and receive antennas (Nt = Nr = 100, 200, 300, and 400) under a Rayleigh fading channel model. The suggested SSA-LSTM model obtained a lower BER, which is a 27

Friction Stir Welding (FSW) of aluminium alloy dissimilar welding is essential to lightweight constructions in the automotive, aerospace and energy sector, yet stabilizing the quality of the joints in this context continues to be a challenge because of extreme thermal gradients, Intermetallic compound (IMC) formation and the dynamics of the process. Traditional offline or rule-based optimization methods are not flexible to material flow and heat production changes in real-time, which usually leads to defects and poor mechanical behavior. The paper was dedicated to the optimization of the main FSW process parameters, such as the tool shoulder-to-pin diameter ratio (SP), tool rotational speed (TRS), and tool traverse speed (TTS) with the help of a reinforcement learning (RL)-based real-time optimization algorithm to maximize the weld integrity and performance. The suggested methodology incorporated the in-situ sensor feedback, such as torque, axial force, interface temperature, and tool vibration in a digital process environment to allow the continuous state monitoring during welding. A model-free RL model based on Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to dynamically adjust SP, RS and TF in order to regulate heat input and material flow. An objective rewarding multi-objective was developed to reduce IMC thickness, thermal variability, and maximise tensile strength and joint effectiveness. The dissimilar aluminium alloy butt joints were experimentally validated to converge rapidly on policy and to be able to stay stable amidst process disturbances. The RL-optimized process demonstrated a 19.7% increase in tensile strength (142 f 5.8 MPa to 170 f 4.2 MPa), a 23.3% reduction in IMC thickness (8.6 f 0.7 & micro;m to 6.6 f 0.5 & micro;m), and a 17.2% improvement in electrical conductivity compared to the optimized static baseline. The study demonstrates that reinforcement learning enables intelligent and self-optimizing FSW through realtime adjustment of SP, TRS, and TTS, thereby offering a scalable route toward autonomous manufacturing of high-performance Cu-Al dissimilar joints.

Green silver nanoparticles (AgNPs) were successfully prepared by the reducing and stabilizing action of the Terminalia arjuna stem bark (TASB) extract. The formation of nanoparticles was established by the surface plasmon resonance band at 510 nm and the FTIR spectrum, which revealed phytochemical capping interactions. Electron microscopy images revealed well-dispersed spherical nanoparticles with a size range of 20–40 nm, and EDX confirmed the presence of silver as the major component. TASB-AgNPs exhibited pronounced antimicrobial, antifungal, and antioxidant activities. Against A549 lung carcinoma cells, the nanoparticles exhibited significant cytotoxicity, which was primarily due to apoptosis. DFT calculations revealed a very small HOMO-LUMO gap of 0.0331 eV, which indicated high electronic softness and good charge transfer potential. Docking analysis revealed weak but complementary interactions with BCL-2, KEAP1, and KRAS targets. Taken together, the findings suggest that TASB-AgNPs exhibit multifaceted biomedical properties due to interfacial phytochemical-nanoparticle interactions.

In the worldwide scenario, solar air heating system (SAHS) is mainly used for various applications. Renewable energy plays very important role in air heating system for industrial and domestic applications. Basically solar air heating system using Copper U-Tube Evacuated Solar Collector is useful for agricultural applications. In our experimentation, author primarily focuses on improving the thermal performance of solar air heating system. Experimental result shows that maximum output temperature of air achieved is 130–140 °C. This solution will be easily adoptable in agricultural as well as selected industrial applications.

Deep learning has advanced at a staggering pace, facilitating the production of near-original deepfakes—video and audio files that impersonate real people convincingly. These advancements can be beneficial in entertainment, accessibility, and content creation, however, the misuse of such technologies can cause significant harm relating to misinformation, digital trickery, Impersonation in cybercrimes, fraud and identity theft. This paper describes a dual-modality deepfake detection system that targets both video and audio forgeries through custom deep learning techniques. For the video detection, we suggest a hybrid CNN-LSTM model which retrieves spatial information of cropped faces and also tracks temporal changes across sequences. For audio deepfake detection, a lightweight LSTM architecture with Mel Frequency Cepstral Coefficients (MFCCs) is suggested. Both models are embedded within a web-based detection platform with independent upload and processing pipelines for audio and video content implemented through a Flask-based backend to enable practical application in the real world, allowing users to independently upload and examine media content for credible authenticity. For video deepfake detection, the results are evaluated using a composite dataset comprising FaceForensics++, Celeb-DF (v2), and DFDC facial frame samples, and attained an accuracy of $94\%$ whereas for audio detection, the SceneFake dataset with different noise and enhancement levels is used with detection accuracy of $98 \%$. The suggested methodology works in real-time and can be deployed in non-controlled scenarios using standard CPU-based systems during inference, while model training may benefit from GPU acceleration. The experimental results show that the framework remains reliable across different types of manipulations, making it an effective instrument for multimedia forensics and digital content authentication.