This paper objects to improve power quality in grid-connected photovoltaic (PV) system through development of an advanced Unified Power Flow Controller (UPFC) these participates dual control strategies with the multi-level inverter architecture. UPFC, the flexible and multifunctional power electronic device, addresses common current-related power quality issues including harmonic distortion, current imbalance, and transient disturbances by effectively managing power flow and compensating to reactive currents at a point of common coupling (PCC). Here, proposed control scheme employs sliding mode control (SMC) under steady-state conditions and switches to proportional resonance (PR) control during dynamic disturbances, enabling precise and responsive current regulation. In contrast to high-frequency inverters has the switching losses in high-power applications, an inclusion of MLI connected to UPFC design significantly reduces Total Harmonic Distortion and improves inclusive system efficiency. Here, system's efficiency is authenticated through simulations in a MATLAB/Simulink environment, since various working scenarios such as harmonic injection, unbalanced current conditions and transient events, and current swell. Simulation results establish the proposed UPFC configuration noticeably increases current and PCC, PV system’s output, and across grid interface. These findings highlight Voltage of UPFC, integrated with MLI, as robust and efficient solution to enhancing current regulation and overall quality power in PV grid-tied systems.

Keywords : Grid Computing , Power Quality, PR Control System, PFCE, Smart Grid, uPFC, PCC, MATLAB.

Authors : KUPPIREDDY KRISHNA REDDY, N V KISHORE KUMAR

Title : Enhancing Grid-Connected Photovoltaic Systems Power Quality through the Unified Power Flow Controller Equipped with an Innovative Sliding Mode and PR Control System

Volume/Issue : 2025;7(6 (November - December))

Page No : 83 - 96

The Secured data safe guard transaction with multi-tenant environments run on private-protected authenticate platforms runs by secured handed environments that emerges with the expansion of cloud-based AI services. To enhanced this secured leakage address challenges solution to protect a secure AI Watermarking system incorporating key distributed between trusted parties based on key authentication as we proposed solution to guided safe guarded way to reactive, and proactive security alert systems using algorithms. This proposed system before attacks can be prevented through the active measures. domain run base restrictions with limited access. Conversely, Proposed system reactive methods to captured on watermarking and biometric identification owner device specific IP leakage that occur during the exchange of data and models in federated and remote learning algorithms.

Keywords : AI Watermarking, Deep Neural Network Protection, Federated Learning , Cloud AI Security.

Authors : M. Anjankumar, Prathima Chilukuri

Title : Secure AI Watermarking Framework for IP Protection in Multi-Tenant Cloud Platforms

Volume/Issue : 2025;7(6 (November - December))

Page No : 69 - 82

This paper presents the implementation of an intelligent Hybrid Energy System (HES) tailored to a detached micro-grid comprising Solar Power Panels(SPP), wind turbines (WT), and a Diesel Power Producer (DPP). The proposed system incorporates an advanced Utility Management (UM) strategy to dynamically balance Source and demand. To achieve optimal system performance, a Grey Wolf Optimizer (GWO)-based control framework is employed, focusing on minimizing operational costs, maximizing Source reliability, and enhancing the efficiency of load distribution. The study models a 24-hour operational cycle under realistic and variable environmental conditions, specifically fluctuating solar irradiance and wind speeds. Simulation results demonstrate that the integration of GWO with UM significantly enhances system performance across multiple metrics. Notably, the Levelled Cost of Energy (LCOE) is reduced, the Loss of Power Source Probability (LPSP) is minimized, and the Unconventional Energy Fraction (UEF) is maximized, indicating increased utilization of Unconventional resources. Additionally, the optimized energy dispatch strategy reduces diesel fuel dependency, thereby lowering emissions and improving overall energy sustainability. The results confirm that the GWO-enhanced UM approach offers a viable and efficient solution for managing energy in off-grid hybrid systems, promoting energy autonomy, and supporting the global shift toward cleaner energy sources.

Keywords : Standalone Micro-Grid, Hybrid Energy System, GWO, Unconventional Energy Integration, LCOE, LPSP, UEF.

Authors : KUPPIREDDY KRISHNA REDDY, K KATHIRAVAN

Title : Optimized Hybrid Energy Management System using Grey Wolf Optimizer for Reliable and Cost-Efficient Microgrid Operation

Volume/Issue : 2025;7(6 (November - December))

Page No : 57 - 68

The advancement in Industrial Wireless Sensor Networks (IWSNs) demands intelligent energy management strategies to enhance network longevity and reliability. This research proposes an adaptive federated learning (FL) framework to optimize energy consumption in IWSNs, incorporating predictive maintenance and dynamic clustering. Utilizing lightweight machine learning models integrated with FL, the framework predicts network conditions and proactively adjusts sensor operations. Dynamic clustering facilitates efficient data aggregation and reduces transmission energy. The proposed solution significantly minimizes unnecessary energy usage by predicting sensor node failures and scheduling optimal sleep states, thus extending network lifespan and reliability. Experimental evaluation demonstrates that this approach reduces energy consumption by up to 25% compared to traditional energy management schemes, validating its effectiveness in industrial environments.

Keywords : Federated Learning, Energy Optimization, Dynamic Clustering, Industrial Wireless Networks.

Authors : K Chandrasekhar

Title : Adaptive Federated Learning for Industrial Wireless Energy Optimization

Volume/Issue : 2025;7(6 (November - December))

Page No : 50 - 56

This paper proposes a novel technique for reducing crossover disturbances, consumption of electricity, Power Delay Product (PDP), and Energy-Delay Product (EDP) in MWCNT-based through-silicon vias (TSVs) with polymer dielectric liners. A new TSV structure is introduced, which uses MWCNT bundles as conductive elements and polymer liners such as polyimide, Polypropylene Carbonate (PPC), and Benzo Cyclobutene (BCB) as insulators. An analogous electrical circuit model is developed to assess crosstalk effects in connected TSVs powered by a ternary inverter. Simulations with the HSPICE tool show that BCB-based TSVs significantly improve performance, especially at lower TSV heights, by up to 30.21% over single-walled carbon nanotube (SWCNT) TSVs. Additionally, BCB-lined TSVs exhibit superior crosstalk suppression, especially at larger TSV pitch values, with a 40.03% enhancement at 0.5 to 3 ?m pitch compared to SWCNT TSVs. The study further explores the impact of TSV height and dielectric constant variations, revealing that BCB provides a 43.8% reduction in crosstalk delay over conventional SiO? liners. These findings highlight the potential of polymer liners, particularly BCB, in improving signal integrity and overall performance in next-generation 3D ICs, making them a promising alternative to traditional materials.

Keywords : Crosstalk mitigation, MWCNT, PDP, SWCNT, TSVs

Authors : T Chinna Sanjeeva Rayudu

Title : Comparative Analysis of Advanced Shielding Techniques for Crosstalk Mitigation in Multi-walled Carbon Nanotube TSVs

Volume/Issue : 2025;7(6 (November - December))

Page No : 44 - 49

Medicine dispensing at hospitals is one of the challenging issue at healthcare management. It has led to the need of smart and automatic medicine dispenser without human intervention to speed up the drug delivery process and avoid the delay at counters. The hospitals already automated the process of patient appointment booking before hospital visit but after doctor consultation, patients still have to wait for medicine delivery at counters as the drug delivery process is not automatic. This paper works on Automatic Drug dispensing with IoT and IPFS. This approach reduces waiting time counters and provides simple and effective way of dispending medicines. The IoT based vending device is designed and developed to read the post doctor consultation Prescriptions digitally through QR codes, process and identify the accurate medicines and dispense the accurate medicines. IPFS Is used to store patient and Prescription data for more security .

Keywords : Dispenser, Inter Planetary File System(IPFS), NodeMcu Patient, Hospital, Medical Units, Pharmaceuticals.

Authors : Dr D Aruna Kumari, Kodumuri Bhavya

Title : Intellignet Scheduling Drug Dispenser using Patient Management System

Volume/Issue : 2025;7(6 (November - December))

Page No : 39 - 43

Women Safety is one of the challenging areas in daily life’s, and it has led to different security problems. Due to security issues, women hesitate to work beyond hours to reach the safety targets. Our proposed system announces the real-time concept of live surveillance tracking system when the Women available in various unprotected places, and its to tracking many through IoT and Deep Learning. It observes and capture images from live streaming and tracks the exact situations or movements where unacceptable situations on dangerous, abnormalities take places. Risk Desk System will track automatically notified with alerts and when there is changes in unacceptable behaviour happens. Current research work mainly focus on the posture estimation to resolve the identified problems through deep learning, and computer vision. It will be capable of identifying persons, recognize their gender, and estimate their situation exactly on live or SOS. It will help in recognizing harmful situations, where a women is left alone or occupied with a group of men, which also does not limit to only night time but daytime too. Additionally, it observes the people rigorously and continuously, providing more data related to the group of people acting in that situation. The system proposed mainly focuses on SOS gestures, alert automatically to concerned dedicated teams on surrounding police station’s , NGOs, existing family relatives with noticeable alerts on timely and live tracking.

Keywords : Women Safety, Workplace, Public Safety, SOS Gestures, Real-time Monitoring System.

Authors : Dr D Aruna Kumari

Title : Women Safety and Surveillance System using YoLov8

Volume/Issue : 2025;7(6 (November - December))

Page No : 34 - 38

The world's food supply relies on individual with small holding farmers, but these farmers frequently lack access to sophisticated decision-support systems. The models are difficult to grasp, which is limiting the application of artificial intelligence (AI), despite its promise to increase output through prediction, disease detection, and input optimization. For smallholder farmers in particular, our proposed approach integrates interpretable approaches like decision trees and rule-based categorization with post-hoc explainability techniques like SHAP and LIME to generate explainable AI (XAI) models. Additionally, it develops user-friendly interfaces that enhance clarity through the use of visual, verbal, and contextual cues. Trust, usability, and decision-making assistance are assessed using field appraisals and participatory design approaches. Last but not least, the study establishes a link between AI effectiveness and its understandability, which empowers farmers via transparency and promotes long-term agricultural expansion.

Keywords : AI Data Preprocessing, Decision Trees Rule-Based Classification, Complex and Additive Models, ANN.

Authors : YEGIREDDI RAMESH

Title : Designing Interpretable and Explainable AI Frameworks for Smallholder Agriculture

Volume/Issue : 2025;7(6 (November - December))

Page No : 26 - 32

Design and analysis of a circular split-ring resonator (CSRR) loaded high-gain slotted rectangular microstrip patch antenna (RPA) exhibiting frequency notch characteristics. The primary objective of this work is to enhance the gain and bandwidth of the conventional microstrip patch antenna operating in the X-band. The CSRR structure, functioning as a metamaterial, is incorporated onto the patch to achieve the desired electromagnetic response. The proposed antenna attains dual gain peaks of 20.1 dBi and 12.0 dBi, exhibits a stable radiation pattern, and resonates at 10.76 GHz with a return loss (S11) of -18.94 dB. Owing to its high gain performance and operation within the X-band, the antenna demonstrates strong potential for satellite communication applications.

Keywords : RPA, Metamaterial, CSRR, High gain, Slot, DGS.

Authors : Jada Nageswara Rao

Title : High Gain Slotted Rectangular Patch Antenna Loaded with Circular Split Ring Resonator

Volume/Issue : 2025;7(6 (November - December))

Page No : 20 - 25

As an extension of cloud services to the edge, Fog computing is a current paradigm for latency-sensitive and resource-constrained applications. Nevertheless, its distributed architecture makes it vulnerable to sophisticated cybersecurity threats, especially botnet assaults. To tackle this problem, we propose a reinforcement learning–enhanced deep learning framework for multi-class classification of botnet attacks in a fog computing environment. At the heart of our approach is a Bidirectional Long Short-Term Memory (BiLSTM) network, a well-known sequential data modeling network. Reinforcement learning based feature selection was used to find and retain the best relevant set of attributes such that detection can be maximized. We performed extensive experimentation and evaluated over four cross-validation folds using a real-life dataset. This proposed model consistently proved micro-averaged precision, recall, F-score, and accuracy greater than 95% on all classes — namely High, Moderate, Low, and Normal attack intensities — over three benchmarked datasets, BADD, RLUF, and DIBCA. Particularly, precision and sensitivity scores were relatively close to perfect, ranging from near-perfect scores for higher severity attacks in terms of detection and false alarms. By proving that deep learning can be further improved by incorporating reinforced feature optimization, this study provides evidence that deep learning can be further boosted to assist in fog networks with deep botnet detection. The proposed framework is ready to adapt, efficient, and holds great promise for real-time intrusion detection based on Internet of Things (IoT) backed fog infrastructures.

Keywords : Fog Computing, Botnet Detection, DL, BiLSTM, Reinforcement Learning, IoT Security, DDoS.

Authors : Surya Pavan Kumar Gudla, Preeti Nutipalli, YEGIREDDI RAMESH, T Chalapathi Rao

Title : Predicting Botnet Attack and Severity in Fog Computing Networks using Deep Learning with Reinforced Feature Optimization

Volume/Issue : 2025;7(6 (November - December))

Page No : 1 - 19