The increase in criminal activities has led to a growing demand for advanced smart home security solutions. Ensuring safety, especially when away from home, necessitates the integration of reliable, cost-effective security systems. This paper presents the design and implementation of a smart home security system that leverages an Arduino microcontroller and a GSM (Global System for Mobile Communications) module. The primary objective of the system is to enhance home security by promptly notifying homeowners of unauthorized entry or motion. The system integrates an Arduino UNO microcontroller with a PIR (Passive Infra-Red) motion sensor and a GSM module. Upon detecting motion or unauthorized access, the system uses the GSM module to send alerts to the homeowner’s mobile phone, while simultaneously activating a buzzer and LED indicators. The solution is designed to be both affordable and accessible, utilizing basic, readily available components. This paper includes a detailed software implementation through Arduino programming and outlines the hardware configuration, including circuit diagrams and component specifications. The system’s reliability and performance in detecting intrusions are validated through various testing scenarios.

Algebraic expressions are used in many application areas. In this paper, we propose a novel code optimization technique for algebraic expression. We used two techniques to reduce cost with respect to time. Experimental results are obtained by comparing optimization ratio of existing optimization approach with the proposed approach. Therefore, we used simple substitution method to solve algebraic problem and standard optimization technique and calculate the results that how both techniques reduce the complexity of Algebraic expression. The results show that cost of Algebraic expression using substitution and cost of optimize algebraic expression without substitution results is less than the original optimize Algebraic expression.

Sustainable functioning of renewable energy systems depends on accurate demand forecasting, yet conventional methods like regression and econometric models frequently fail to capture nonlinear, high-dimensional, and time-dependent patterns in energy consumption. Addressing this research challenge, this study explores the integration of Artificial Intelligence (AI), particularly Long Short-Term Memory (LSTM) networks, into energy demand forecasting frameworks. The objective is to improve forecasting accuracy, adaptability to dynamic loads, and operational efficiency in renewable-powered systems. The research employs a hybrid methodology—combining theoretical modeling, sector-based case studies, and empirical evaluation—to develop and validate AI-based forecasting models. Industry-relevant scenarios, including implementations by Enel, GE, and Uber, demonstrate real-world applicability. Model performance is assessed using standard metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with the LSTM model achieving an MAE of 0.87 kW, an RMSE of 1.10 kW, and 92.3% accuracy, measured by the coefficient of determination (R² score), outperforming conventional models. Key findings highlight improvements in grid stability, cost efficiency, and responsiveness to demand variability. The study’s novelty lies in its multi-sectoral synthesis of AI forecasting applications, offering insights for developing scalable models for smart grid operations. This work provides significant implications for energy providers, engineers, and policymakers by enabling more accurate, data-driven decisions in energy planning and policy formulation. Moreover, it reinforces the transformative potential of AI in addressing operational uncertainties, environmental constraints, and technological disruptions in modern power systems. Future research may explore explainable AI models to enhance transparency and stakeholder trust.

Blockchain technology is rapidly becoming one of the most groundbreaking technologies for revolutionizing supply chain management with unprecedented security, transparency, and efficiency. This paper presents a comprehensive literature review of blockchain and its applications in leading industries such as transportation, manufacturing, food and beverage, and healthcare. Blockchain applies distributed ledger technology to secure tamper-evident record-keeping, which significantly enhances traceability and provenance verification across complex supply chains. By integrating smart contracts, IoT connectivity, and decentralized financial services, blockchain can solve significant challenges, such as counterfeiting, supplier management, and enforcing sustainable and responsible sourcing practices. Despite these benefits, the mass-scale adoption of blockchain faces serious challenges, such as scalability, interoperability, regulatory ambiguity, and a lack of standardized frameworks. The report also addresses the environmental concerns of blockchain’s power-intensive proof-of-work algorithm and discusses ways to counteract them. Future developments in artificial intelligence and 5G networks will continue to evolve supply chain management in ways that unleash unmatched efficiency and potential.

Cloud computing has a significant role in our daily life. Having many features, it made our life easier. Keeping cloud environment reliable and secure is very essential in order to support large number of users and many smart devices. Now-a-days Cloud computing security is one of the important challenging fields. The biggest security threat for services availability in Cloud Computing is Distributed Denial of Service (DDoS) attack. A major attribute to DDoS attack that hides attacker’s identity is IP address spoofing. With the passing of time DDoS attack becomes powerful, the attack may be minimized if it is detected at first. So we focused on prevention mechanism against the attack for securing the cloud environment.

This study proposes a flexible distributional framework the Generalized Exponential Power Distribution with Modified Variance Transformation (GEPDMVT) designed to improve classification accuracy in datasets characterized by skewness, heavy tails, and mixed-variable structures. Traditional classifiers like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis QDA, and Support Vector Machine (SVM) often rely on restrictive assumptions such as normality and equal covariances, which may not hold in real-world data. The GEPDMVT modifies the variance structure of the multivariate exponential power distribution to align the shape and scale parameters, enhancing robustness. A novel Multivariate Discriminant Analysis (MVDA) classifier was developed based on this distribution and evaluated using simulated data across sample sizes (n = 20 to 10,000) and four benchmark datasets (Iris, Wine, Pima, and Glass). Results show that while MVDA slightly underperforms in small samples, it competes favourably and even surpasses existing methods in large datasets, achieving 99.92% accuracy at n = 10,000. MVDA outperformed all methods on the Wine dataset and was comparable in others, confirming its adaptability and resilience in heterogeneous and complex data scenarios. The findings demonstrate the potential of GEPDMVT-based MVDA in enhancing classification performance across a wide range of real-world applications, especially where data deviates from traditional parametric assumptions.

Modelling heavy-tailed and skewed data presents substantial challenges in statistical analysis, especially when dealing with mixed-type variables and complex distributional structures. This study proposes a novel and flexible distributional model, the Generalized Exponential Power Distribution with Modified Variance Transformation (GEPDMVT), to address these challenges. The GEPDMVT extends the classical Exponential Power Distribution by incorporating distinct shape and scale parameters along with a variance transformation mechanism that enhances its flexibility in modelling diverse hazard rate shapes, including increasing, decreasing, and bathtub forms. The study derives key statistical properties of the GEPDMVT such as the probability density function, cumulative distribution function, moments, survival function, and hazard function, providing analytical tractability and robustness. Through extensive Monte Carlo simulations across a range of sample sizes, the model’s performance is evaluated in terms of bias, root mean square error (RMSE), and flexibility. Additionally, the model is empirically validated using secondary datasets comprising public health expenditure in Nigeria and Ghana (1995–2014), renal transplant graft survival times, tax revenue data, and daily COVID-19 case counts in Nigeria. Comparative analyses with existing exponential-based distributions demonstrate the superior fit, versatility, and robustness of the GEPDMVT under varied data conditions. This study contributes to the advancement of statistical modelling by offering a unified and interpretable distribution that enhances prediction accuracy and inferential reliability in heavy-tailed and skewed data contexts.

Recent breakthroughs in semiconductor crystal growth are fundamentally transforming next-generation solar optoelectronic devices. Controlled synthesis of high-quality III-V semiconductors, 2D materials, and hybrid perovskites now enables precise tuning of band gap, charge carrier mobility, and light-matter interactions. Innovations such as chemical vapor transport (CVT) for large-area SnS₂ (2.15 eV bandgap) and strain-minimized organic-inorganic interfaces directly address the longstanding challenges of structural defects and scalability. Perovskite crystal engineering, for example, achieves high carrier mobility and solution-processability, which are essential for flexible solar cells. Integration with silicon platforms is advancing through lattice mismatch mitigation using buffer layers and post-growth annealing, ensuring high-quality epitaxial layers. Enhanced crystallinity in organic semiconductors, achieved via van der Waals growth, reduces interfacial defects and improves charge transport in hybrid systems. Scalable production methods, such as iodine-mediated CVT, are accelerating the transition to cost-effective, large-area optoelectronic devices. By bridging material innovation and manufacturing-readiness, this review demonstrates how crystal growth advances are enabling high-efficiency photovoltaics, LEDs, and photo detectors for future energy and communication systems.

This paper presents an Arduino-based smart meter system to help monitor energy usage in an economic and efficient way. The system is applicable to linear electrical loads, i.e., light bulbs, and gives accurate readings of important parameters such as voltage, current, and apparent power. A unique aspect of this system is that it not only tracks the quantity of electricity consumption but also enables users to control electrical loads remotely using SMS. where users can conveniently control, turn on, and turn off electrical loads, making it a simple and effective method of energy management in real time. In addition, the system has also been field-tested under real-world conditions for performance, reliability, and accuracy, and it works well in a wide variety of applications. The implementation cost is extremely low. Priced at an estimated $60, this design is a low-cost, effective solution that can work both for residential and commercial use. The system’s real-time energy monitoring and remote-control functionality will meet the growing need for intelligent energy consumption analysis and efficiency solutions. The proposed system is effective, where electrical load monitoring and control are conveniently achieved through the utilization of Arduino technology. Feasibility, scalability, and cost-efficiency is what it is all about. This study shows how essential most intelligent solutions are in electricity management today. These results directly exemplifies how this system supports a sustainable power use by giving users more power over their power use, reducing power waste and finally enhancing the efficiency of power utilization.

Accurate reconstruction of surface topography from Atomic Force Microscope (AFM) images is crucial for nanoscale characterization. This study investigates the application of a blind tip estimation algorithm, which leverages set theory and morphological operations, to address distortions caused by the AFM tip-sample interaction. The method, building on Villarrubia’s approach, allows for the estimation of the AFM tip shape directly from images of samples with unknown surface geometries. We present experimental results from two real samples: one featuring an array of square pillars and another with cylindrical pillars, demonstrating the efficacy of this restoration technique.