Metallurgical and Materials Engineering (E-Journal)
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Detection Of Offense And Generating Alerts Using Ai
Get PDFIn today’s urban environments, ensuring public safety has become a growing priority. However, traditional surveillance systems dependent on continuous human monitoring often struggle with delayed threat recognition and response. This paper presents a real-time, dual-mode surveillance system that integrates deep learning-based visual analysis with automated alert generation to enhance situational awareness in public and private security domains. The proposed system leverages a lightweight CNN (Convolutional Neural Network) trained to detect high-priority criminal offenses, namely harassment, theft, and burglary, from both live camera feeds and uploaded video fragments. A calibrated decision logic module filters out low-confidence predictions, significantly reducing false positives while maintaining high recall. To support real-world deployment, the technique integrates an alerting mechanism comprising real-time alarms, email notifications with frame evidence, and a live web dashboard for visual analytics. The lightweight design is containerized and optimized for edge deployment on devices such as the NVIDIA Jetson Nano, or mid-tier GPUs are suitable for deployment. Empirical evaluation on a composite dataset combining UCF-Crime, HarX, Shoplift-23, and proprietary CCTV clips demonstrates a classification accuracy of 92.4% and an F1-score of 89.9%, outperforming baseline models including YOLOv5 + DeepSORT. Designed with modularity, scalability, and ethical AI considerations, this research bridges the gap between theoretical computer vision models and practical, real-time crime detection solutions for smart surveillance environments
Building Trustworthy Agentic Ai Systems FOR Personalized Banking Experiences
Get PDFArtificial Intelligence (AI) systems have become ubiquitous in contemporary society and have the potential for transformative impact on user behavior. These systems are capable of learning autonomously to personalize their behavior to deliver improved user experiences. However, there exists the potential for unintended consequences, as the same agentic features associated with positive outcomes may also increase the capacity for negative outcomes. Financial services is an example of a domain where deploying AI systems with agentic features would be high risk. The automated decision-making capabilities of these systems could influence billions of dollars. Nonetheless, they would be entrusted with taking actions that affect users without human oversight, such as reallocating entire portfolios of assets in ways that users do not wish. Therefore, a foundational requirement for adopting these systems must be the capacity to build shared norms of beneficial behavior prior to their deployment.
Several commercially available AI systems with agentic features are already deployed in the domain of personal banking. Automated personal financial management combines categorization of transactions followed by predictions of future expenditure and savings to improve budgeting decisions, amongst other impacts. Digital banking assistants embed intelligent conversational agents used primarily for accessing banking information and services. These systems typically operate in conjunction with non-intelligent user interfaces, and thus the extent of agentic features in user-bank interactions is limited. However, envisionable advancements include wider adoption of natural language processing capabilities, comparative financial analysis, and customized query suggestions.
Agentic AI systems must operate under a formal specification of trustworthiness constraints. Therefore, AI agents must embody the technical requirements for trustworthy AI systems. Management of risks associated with agentic AI is a dangerous task given the scale of money flows in financial markets and the unprecedented scale, scope, and speed of analysis, prediction, and execution in such markets. At the same time, AI systems that endow agents with entity-level legal ownership and agency create a scarcity that could be captured in trust funds in the form of wealth to protect a material asset class from better prediction by other agents (all other predictions being sub-optimal). Glücksspiel unter Vertrauensbildung para-poker could be a conceptually rigorous game of chance. Thereby, the proposed system could help to promote beneficial forms of AI agency while governing risks effectively
A Comprehensive Machine Learning Framework for Predicting the Energy and Economic Impact of Electric City Buses
Get PDFThe research work currently attempts to reduce the carbon emissions and make energy-efficient urban public transportation with electric buses. This research sets up a big data analytics framework with machine learning to forecast and optimize the energy consumption of electric city buses. Such system offers accurate prediction of energy economy by utilizing real-time large-scale telematics and operational information processed via batch and stream through Apache Spark. As per the objective of fast-paced transit environment subjected to continuous disturbances by traffic, weather, and vehicle load, the scalability of the framework serves as a crucial capacity for distributed computation and in-memory processing. Energy consumption can be viewed in a holistic manner charged with heterogeneous data sources. Such predictive insights enable transit agencies to undertake proactive energy strategies, model optimization on routes, and introduction of batteries that last longer into lower operational costs with reduced environmental impact. Future work will be targeted towards real-time integrated streaming tools such as Apache Kafka and Flink and deploy advanced models like LSTM and Reinforcement Learning while developing visual analytics and cloud scale. The research will explore how NLP can be subjected to use for unstructured data analysis, for instance through driver logs and maintenance reports. From intelligent transport systems, this framework is considered a great major step and indeed becomes a crucial building block towards the vision of smart energy-efficient cities
Ai Techniques For Robust Data Integrity And Security In Adhoc Networks
Get PDFAd-hoc networks are supported by an AI-based framework to enhance robustness as well as secure data transmissions. The Artificial Intelligence framework is made for getting robust and secure ways for data transfer through ad hoc networks. It combines reinforcement learning for optimizing routing in dynamic environments, supervised learning for intrusion detection, and resource management for energy efficiency and improvement in network lifetime. Changes in routing and security according to different conditions like node mobility, traffic patterns, detection of security anomalies will also be done along with such intelligent techniques. Furthermore, advanced techniques for anomaly detection will counteract black hole and denial-of-service attacks. Besides this load distribution and bandwidth allocation would also be performed dynamically for better performance in the system. Experimental results showed enormous improvements over traditional methods in terms of packet delivery ratio, latency, and security resilience of dynamic ad hoc communication
Gender Behind Bars: A Critical Analysis Of The Laws, Policies, And Realities Of Women In Indian Prisons
Get PDFA prison is an institution which is established to confine the persons who have violated the laws of a nation. Though the primary purpose of these incarceration facilities is rehabilitation and reformation of the offenders, yet these places often conceal instances of violence which undermine the basic goal to reform. This makes it essential to recognise and uphold the fundamental rights of the prisoners to allow their transition and enable them to join back the society. These issues are apparent especially for the female prisoners’ whose needs are often ignored in these prisons
Aluminothermic production of titanium alloys (Part 2): Impact of activated rutile on process sustainability
Get PDFThe aluminothermic process provides a cost-reduced production method for titanium and titanium alloys by reduction of TiO2 with subsequent refining by electroslag remelting The aluminothermy involves high heating rates, high temperatures and short reactions times combined with a self-propagating behaviour of the reaction. By co-reduction of TiO2 and oxides of alloying elements such as vanadium pentoxide, direct synthesis of a titanium alloy is possible. The use of rutile ore concentrates causes a further reduction of process steps. In order to charge rutile ore complex thermodynamic calculations are required taking enthalpy input of various bycomponents into account. The aluminothermic reduction is conventionally enhanced by a highly heatproviding reaction based on the reduction of KClO4. In order to minimize the use of chlorine-based products extensive studies are made to investigate the feasibility of using mechanically activated rutile as input material for the aluminothermic process. Due to the mechanical activation the intrinsic enthalpy of the reaction is increased thus facilitates a process with reduced amount of KClO4. A major challenge represents the determination of a compromise between low activation duration and reduced KClO4 amount. In order to define the process window parameters like intrinsic chemical energy (enthalpy of the reaction mixture), equilibrium temperature and physical properties (particle size and mixing degree) were optimized. After adjusting the process parameters it is possible to save up to 42 % KClO4 for the ATR reaction with 2h activated input material. This reduction of KClO4 material affects a decrease of the produced gaseous compounds and the subsequent off-gas cleaning system
Big Data-Driven Predictive Maintenance for Industrial IoT (IIoT) Systems
Get PDFBig data-driven predictive maintenance is becoming a fundamental component of IIoT systems to enable failure predication proactively and streamline the scheduling process. This work examines the intersection of machine learning, digital twin technology, and optimization techniques in the context of increasing predictive maintenance efficiency and effectiveness. Four algorithms were evaluated via live IIoT sensor reading inputs: Random Forest, XGBoost, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). The performance outcome indicates that XGBoost achieved the highest in fault detection accuracy at 96.4%, followed by CNN at 94.8%, LSTM at 92.3%, and then Random Forest at 90.1%. A blockchain-based federated learning framework was also utilized to facilitate secure and decentralized predictive maintenance and minimize false alarms by 28% compared to conventional methods. Optimization methods such as Koopman observables and Dynamic Mode Decomposition with Control (DMDc) also enhanced system efficiency, reducing computing cost by 35%. Scalability issues with predictive maintenance in large-scale industries are confirmed as part of this study, as well as edge AI integration and reinforcement learning as probable future trends. These results form the basis of the significance of data-driven predictive maintenance in minimizing downtime, optimizing resource utilization, and facilitating cost-efficient industrial processes
High Speed Power Efficient Dynamic Comparator with Low Power Dissipation and Low Offset
Get PDFWhen designing digital circuits with high speeds, dynamic comparators are necessary. In particular, central processing units (CPUs) in a wide variety of electronic devices rely on low-power, high-speed dynamic comparators. Numerous comparators, which are comparison circuits, make up these central processing units. This research article introduces a low-voltage, low-power Double Tail Dynamic Comparator (DTDC) that uses less power than previous designs. This journal article compares and contrasts the suggested design with several kinds of dynamic comparators. The suggested architecture is contrasted with dynamic comparators that rely on techniques such as regenerative latch, floating inverter amplifier, and Double Tail. The Tanner EDA simulation program is used to model this design using 18nm technology. This suggested design use the self-biasing approach to execute the pre-amplification process. This suggested design operates with less kick back noise thanks to the self-biasing mechanism
BaSr(Al₀.₅Nb₀.₅)O₃ Perovskite Nanoparticles: Structural and Optical Evaluation for Efficient Light Absorber Layers in Solar Cells
Get PDFIn this work, BaSr(Al₀.₅Nb₀.₅)O₃ (BSAN) perovskite nanoparticles were synthesized using the solid-state reaction method and evaluated for potential application in solar cells. XRD confirmed the formation of a single-phase cubic perovskite structure with crystallite sizes between 33 nm and 55 nm. FTIR analysis indicated characteristic metal–oxygen vibrations, confirming phase formation. SEM analysis revealed uniform, granular nanoparticles with sizes predominantly in the 30–70 nm range. BET surface area analysis exhibited a high specific surface area of 58.4 m²/g and mesoporosity. EDS confirmed elemental homogeneity without impurities. UV-Vis spectroscopy showed strong visible light absorption with an optical bandgap of 1.56 eV, suitable for efficient solar energy harvesting. The combined results highlight BSAN’s suitability as an absorber layer or electron transport layer in photovoltaic devices
GDP Per Capita Variability in Emerging Economies and Scale Effect of Inflation-Tax Burden
Get PDFThis paper explores the variability of the Gross Domestic Product (GDP) per capita in emerging economies and the impact of inflation-tax burden. We investigate how inflation influences economic stability and the fiscal mechanisms to manage this variability. Understanding the effects of the volatility that exists primarily in emerging economies and the inflation rates and tax burden variables that are accepted as the causes of instability as macro variables on the national income per capita constitutes an economic research framework based on the fact that they have an essential place in making economic and financial decisions, especially for emerging economies. It is observed that the most critical financial instability issues in emerging economies have emerged for two main reasons. The main reason is the search for financial resources related to increased inflation rates and tax burden variability. This affects economic growth on a GDP basis and changes with the GDP per capita. This impression sometimes contradicts the economic growth targets and creates a mutual handicap by creating different impact values on emerging market economies created by the global economic crisis. The search for financial resources in the emergence of current deficits in the most critical instability problems related to countries representing emerging markets is evolving into a position where direct tax resources can be further increased and thus affect the tax burden. This evolution also occurs in countries representing emerging market economies in a structure where inflation continues. The findings, especially with the impact of emerging market economies on each other at the global level, are observed to be in a remarkable position, especially with the impact values of emerging market economies on each other, which are close to each other. This finding reveals that although GDP per capita is affected by different impact values, for countries representing emerging markets, this triggers a process where the two main reasons are the constant increase in tax burden and price instabilities and, above all, the emergence of a higher scale deviation effect trend