Volume 14 | Issue 2

 Abstract

The need of facial skincare is growing and the market is huge. Skin kinds, skin problems and skin tones can all be used to classify facial skin. For customized skin care, an accurate skin type classification is essential. This paper investigates the classification of facial skin types using Convolutional Neural Networks (CNNs) and makes recommendations for how CNNs can be used to efficiently categorize skin types and provide a useful tool for individualized dermatology and skincare. This study opens the door for further developments in individualized dermatological care by adding deep learning technology into the skincare sector.

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 Keywords

Facial Skin Type; Convolutional Neural Network; Dermatology; Deep Learning

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The Internet of Things (IoT) represents a revolutionary shift in the way devices, systems, and people are interconnected, enabling automation and efficiency across various domains, from healthcare to smart cities. However, the massive scale and complexity of IoT networks introduce significant security challenges. In this paper, we explore various security patterns and architectural frameworks designed to address these challenges. The goal is to provide a comprehensive overview of the current state of IoT security, identify critical vulnerabilities, and discuss evolving patterns and architectures that strengthen IoT systems against emerging threats.

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Exploring Patterns and Architectures for Strengthening IoT Security

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Modern advancements in technology, such as sensors, satellites, and predictive models, have significantly improved early warning systems for disasters like hurricanes, floods, and tsunamis. These tools enable experts to issue timely alerts and evacuate at-risk populations. The Internet of Things (IoT) plays a crucial role in disaster response by facilitating real-time monitoring of environmental conditions and infrastructure integrity. IoT-driven solutions enhance damage assessment, monitor vulnerable communities, provide real-time data, and predict disaster impacts. By integrating IoT into disaster management frameworks, response times can be improved, coordination between agencies can be strengthened, and recovery efforts can be accelerated. This paper explores IoT-based disaster recovery, highlighting key enabling technologies and proposing an innovative algorithm for establishing temporary network connections in disaster-affected areas. [1].

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 Keywords

Analytics, Coordination, Disaster Response, IOT, Predictive, Sensor, Social Media, Resilient

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Smart cities are rapidly emerging as the backbone of modern urban development, driven by the integration of cutting-edge technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data. These technologies enhance operational efficiency, improve public services, and contribute to sustainable development. However, they also introduce significant cybersecurity challenges, as the interconnected nature of smart city infrastructure creates vulnerabilities that can be exploited by malicious actors. From transportation systems and energy grids to healthcare and public safety networks, critical urban infrastructure faces a growing risk of cyberattacks that can disrupt essential services, compromise public safety, and lead to significant financial losses. This paper explores the cybersecurity risks facing smart cities, with an emphasis on safeguarding critical infrastructure through a multi-layered defense strategy. It presents a comprehensive analysis of the most common threats, including ransomware attacks, Distributed Denial of Service (DDoS), data breaches, and unauthorized access to IoT devices. The paper proposes a multi-layered approach to security, encompassing network protection, application security, endpoint hardening, data encryption, and real-time monitoring. Each layer serves as a critical component of a holistic defense model, aiming to mitigate risks and enhance resilience against cyber threats. Furthermore, this research highlights the importance of collaboration between public and private sectors, smart city administrators, and cybersecurity professionals. It discusses the necessity of developing proactive incident response plans and implementing advanced technologies such as AI-powered threat detection and blockchain for secure data management. By adopting a multi-layered security strategy, cities can better protect their urban infrastructure and ensure the continued safe operation of smart technologies.

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 Keywords

Smart Cities, Cybersecurity, Critical Infrastructure, IoT, Multi-Layered Security, Threat Mitigation, Incident Response

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 Abstract

The process of compression becomes vital to maximize data storage and transmission optimization and especially applies to big data environments. Digital data continues to expand rapidly thus demanding more effective compression methods to become essential. Many types of complex datasets containing high numbers of dimensions prove incompatible with standard compression methods like Huffman coding and Run-Length Encoding. Generative AI models represent new methods for compression, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Vector Quantized Auto encoders (VQ-VAEs), which are emerged as promising alternatives. This study analyzes how generative AI systems develop image and video compression process output and checks their output quality along with resource usage metrics. The main function of compression techniques is to decrease redundant data without compromising crucial information that needs for efficient storage and transmission. The advancement of generative models now enables them to acquire complicated data distributions along with compressed representations which maintain essential aspects from original data. The core purpose of this investigation is to analyze different generative AI models for data compression based on their effectiveness and evaluate their strengths and weaknesses when processing images and video content. In addition the study will identify optimal models for particular applications through performance-based assessments that measure compression effectiveness and reconstructive quality and computational efficiency. Some generative models demonstrate their competence by reaching high compression ratios alongside preserving quality standards in lossy compression operations. These models either present realistic visual reconstructions although they demand increased computational resources. This research into generative model comparison with traditional methods reveals significant findings suitable for various application-related decisions making

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Generative AI, Data Compression, Compression Ratio, Reconstruction Quality, Computational Efficiency

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 Abstract

The proposed research is based on a smart strategy for encrypting audio files using Chaotic map and DNA Encoding. It targets the important parts of sound to protect from unethical use. Chaotic map puzzles hackers and DNA adds a layer of mystery, making it very secure indeed. Algorithm is tested for various statistical test like correlation analysis, information entropy analysis, scatter plots diagrams, histogram, mean squared error, PSNR and NIST tests, these show it's strong and dependable. A great thing is, once you decode the audio, it sounds just as good as before. So, if you're into sharing secret tunes, this approach suits you well.

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Sound scrambling, DNA encoding, Multimedia data, Henon map, Hybrid chaotic map

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 Abstract

Road accidents remain a major global challenge, requiring rapid and effective emergency response mechanisms. Traditional accident detection methods rely on manual reports or eyewitness accounts, resulting in delays in response times and increased severity of the consequences. This paper presents a CNN-based accident detection and emergency response system using YOLOv8, an advanced deep learning algorithm for real-time video analysis. The system processes live video surveillance images to detect accidents quickly, records critical details such as location, time, and severity, and triggers immediate alerts to the emergency services. The proposed system automates accident monitoring, integrating machine learning and computer vision technologies to achieve accuracy while minimizing false positives and negatives. It features a user-friendly interface for traffic authorities and emergency responders, a robust detection module powered by convolutional neural networks, and a scalable database for efficient storage and analysis of accident data. The methodology includes data collection, pre-processing, model training, evaluation, and deployment, guaranteeing the adaptability of the system, deployment, and ensuring adaptability in a variety of environments, including urban areas, freeways, and high-traffic areas. The preliminary results from controlled experiments demonstrate the system's potential to significantly reduce emergency response times and coordination between emergency services. By filling the gaps left by traditional detection systems, this study provides a scalable and reliable solution that contributes to intelligent transportation systems and improves public safety through real-time monitoring notifications and effective emergency management.

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 Keywords

Accident Detection; Convolutional Neural Networks; Emergency Response; YOLOv8

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 Abstract

Nowadays, The Internet of Things (IoT) has made our lives more reliable and efficient in multiple ways. IoT is a rapidly emerging technology in the consumer, business, industrial, and social ecosystems. IoT networks use the communication technologies, such as IoT devices, to share and spread information applications and hardware. As such, Distributed Denial of Service attacks use multiple connected devices executed by botnets and creates many harmful and dangerous threats to the security of IoT networks. Attackers can analyze and attack IoT devices as part of botnets to launch DDoS attacks by taking advantage of their flaws and targeting the server by sending a flood of messages and creating internet traffic then the system halts and reduces the performance of the system. In this research, when an attacker sends a flood of fraud messages, then some alert notifications, warning messages, and alarms are triggered in the victim's machine to avoid data loss. Also includes secondary data (research papers, case studies, and past cybercrime studies) to detect the threats and prevent them in the network. This presenting paper throws light on the prevention and techniques of DDoS attacks in IoT.

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 Keywords

Internet of Things (IoT), Alert notification, Internet traffic, Distributed Denial of Service (DDoS) attack, Botnet.

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Ant Colony Optimization (ACO) is a bio-inspired optimization technique based on the foraging behavior of ants. It has been widely applied to complex problems in various domains, including logistics, robotics, and artificial intelligence. This paper provides an overview of ACO, highlighting its principles, applications, and various adaptations. It also discusses the challenges and opportunities for future research in ACO, with a focus on hybrid approaches and emerging technologies. The goal is to shed light on the evolving role of ACO in solving real-world optimization problems.

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 Keywords

Ant Colony Optimization (ACO), Swarm Intelligence, Ad-hoc Network, TSP, VRP

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Social media sites like Twitter have become increasingly important over the past few years in creating awareness and provoking debate around social issues like gender-based violence. Social media sites offer extensive user-generated content reflecting public sentiment and engagement on specialised subjects. This study uses clustering, community detection, and sentiment analysis to analyse public debate around gender violence on Twitter. The research aims at a corpus of 6,799 tweets representing varying gender violence-related discourse. The corpus was initially preprocessed to remove extraneous content such as stopwords, URLs, and uncorrelated tweets. The tweets were then vectorised using TF-IDF (Term Frequency-Inverse Document Frequency) to identify the meaningful attributes. K-Means clustering was employed to group similar tweets, while Louvain's community detection algorithm was employed to identify individual communities of users discussing gender violence. Sentiment analysis was done to classify tweets as positive, negative, or neutral. At the same time, Different evaluation measures, such as Modularity, Silhouette Score, and Davies-Bouldin Index, were used to analyse the efficiency of clustering and community detection. The objective of this study is to create a machine-learning model that classifies gender-related tweets into one of five categories: sexual violence, emotional violence, harmful cultural practices, physical violence, and economic violence. The research indicates that most problems are unreported, and information is filtered through perpetuating mechanisms and consequences for Indian society. The mental well-being of the women and children also impacts the problems in question. With the support of increasing efforts to promote women's empowerment and mainstreaming gender equality, we can create social awareness on social networking sites with actions that favour women's and girls' development and livelihoods.

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 Keywords

Gender violence, Women empowerment, Gender equality, Social media activism, Data analysis

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 Abstract

Escalating air pollution as a result of urbanization and industrialization is one of the most problematic issues affecting the environment and public health. Urban living requires stringent controls on emissions and effective management of growth trends. In this paper, we propose an emission control system for metropolitan cities called GreenGuard, which is a cloud-based emission monitoring system. This pollution emission control system incorporates IoT (Internet of Things) sensors, cloud computing, and data analytics to enable real-time air quality monitoring along with predictive insights for pollution control. As part of GreenGuard, IoT-empowered sensing devices are deployed throughout the city to monitor critical pollutants, including PM2.5, and PM10, CO, NO?, and SO?. The aforementioned sensors relay data to the cloud where sophisticated machine learning algorithms monitor air quality indicators and identify pollution hotspots. Environmental agencies, politicians, and citizens receive information and alerts through dashboards. Thus, giving them the ability to make decisions in realtime. GreenGuard's predictive analytics component is one of the most remarkable features of the project. It provides the ability to predict the level of pollution based on analyzing historical data along with meteorological conditions. Through this, authorities are able to take preventive measures by restricting traffic, controlling industrial emissions, and issuing public health warnings. It achieves all necessities for metropolitan cities like scalability, enhanced security, and improved accessibility using a cloud based platform. Therefore, making it an ideal solution for larger metropolitan cities. To evaluate the efficiency of the system, GreenGuard was implemented in a metro area through a pilot study. Findings showed successful integration with the environmental systems already in place and a strong ability to accurately calculate pollution levels. The ease of use of the systems interface along with the pollution report automation made pollution management strategies much more sophisticated. GreenGuard's predictive emission monitoring capabilities enable sustainable development within metropolitan areas, which leads to better public health. Addition of AI-powered analytics and wider geographic coverage would improve the results of the proposed system even further.

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 Keywords

Emission Monitoring, IoT, Cloud Computing, Air Quality, Pollution Control, Smart City.

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 Abstract

Anthrax, caused by Bacillus anthracis, is a highly contagious zoonotic infection with important connotations for animal as well as human health. Compartmental epidemiological models and fractional order differential equations are traditionally used models to analyse anthrax dynamics. These models tend to face nonlinearity complexities, so high computational methods are needed for exact solutions. This paper is a critical analysis of various neural network- based intelligent computing processes for the solution of anthrax disease models. Sophisticated techniques like Radial Basis Bayesian Regularization Deep Neural Networks (RB-BRDNN), Mayer Wavelet Neural Networks (MW-NN), and supervised neural networks (SNNs) are investigated in terms of their effectiveness for the solution of nonlinear differential equations. These models, when paired with optimization methods like Particle Swarm Optimization (PSO) and Bayesian regularization, enhance numerical stability and accuracy. The review points out the benefits of neural network- based methods in forecasting anthrax outbreaks and aiding disease control measures. Directions for future research are presented, focusing on hybrid AI models for real-time disease surveillance and intervention planning.

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 Keywords

Anthrax disease, Neural network, fractional order, Single or multiple layers optimization schemes

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This paper presents a systematic literature review of reinforcement learning (RL) techniques, particularly deep reinforcement learning (DRL), applied to stock market price prediction. Through comprehensive analysis of recent research, we find that advanced algorithms such as Q-Learning, Double DQN, and Dueling DQN, especially when combined with sentiment analysis from news and social media, create powerful frameworks that address financial markets' complexity. Our review indicates that DRL approaches significantly outperform traditional methods in the literature, resulting in more adaptive and dynamic solutions for stock market forecasting. This paper synthesizes current research findings and identifies promising directions for future work in this rapidly evolving field.

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deep reinforcement learning, stock market prediction, Q-learning, Deep Q-Network, financial forecasting, systematic review, sentiment analysis

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This document explores the development and implementation of the "Women Safety Night Patrolling Robot in IoT," a cutting-edge solution designed to enhance the safety of women, particularly during nighttime. In light of increasing incidents of violence and harassment, this project leverages advanced technologies such as the Internet of Things (IoT), Raspberry Pi, cameras, and sensors to create an autonomous robotic system capable of patrolling designated areas. The robot is equipped to respond to sounds, providing real-time surveillance and transmitting critical information to a control center for immediate assistance in emergencies. By fostering a sense of security and independence, this initiative addresses the pressing societal challenges surrounding women's safety. Future enhancements may include the integration of artificial intelligence for improved threat detection, the expansion of the robotic network for comprehensive coverage, and adaptations for rural and underserved regions. Ultimately, this project represents a significant advancement towards creating a safer environment for women, empowering them to navigate public spaces confidently.

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 Keywords

Women safety, Night patrolling robot, Internet of Things (IoT), Autonomous surveillance, Raspberry Pi, Real-time monitoring, Sound detection, Emergency response, Threat detection, Artificial intelligence (AI), Public safety

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This research introduces a mobile-controlled river cleaning robot designed to address the growing challenge of water pollution. The robot, powered by an ESP32 microcontroller, serves a dual purpose: removing floating debris and monitoring water quality through a Total Dissolved Solids (TDS) sensor. By integrating a conveyor belt for waste collection and an IoT-based platform (ThingSpeak) for real-time TDS data visualization, this system offers a cost-effective, scalable solution for environmental protection. The robot operates using L298 motor drivers, DC motors, and a 12V power supply, with manual movement control. The TDS sensor provides continuous monitoring of water pollution levels, ensuring that pollution trends are tracked efficiently. The system's remote monitoring capabilities allow authorities to make informed decisions. This research demonstrates the feasibility of combining waste removal with pollution tracking, paving the way for smarter, automated solutions for cleaner water bodies

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Water Pollution, River, Mobile Controlled, TDS

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In today's rapidly advancing digital landscape, maintaining a strong online presence is essential for security-centric companies. Xproguard Pvt. Ltd. has developed a portfolio web application that effectively presents its range of security offerings through modern web technologies. Utilizing React.js, Next.js, Tailwind CSS, and Framer Motion, the platform ensures a responsive interface, enhanced SEO capabilities, and smooth user experiences. Key features include dynamic product displays, a real-time ranking mechanism, and personalized privacy policies, all aimed at boosting user interaction and fostering transparency. This paper explores the chosen technology stack, architectural design, and development process of the application, emphasizing its contribution to strengthening Xproguard's brand identity and commitment to digital innovation..

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React.js, Next.js, Tailwind CSS, Framer Motion, TypeScript, Web Portfolio.

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In the context of intelligent transportation systems, the detection of road irregularities like potholes in advance is crucial to improving public safety and maximizing maintenance. This paper introduces the creation of a real-time pothole detection system based on the YOLO (You Only Look Once) object detection algorithm. Utilizing the YOLO model's speed and accuracy, the system extracts video streams from road-facing datasets to detect and localize potholes with great accuracy. The app architecture includes phases like image acquisition, pre-processing, CNN-based detection, and post-processing to refine the results. With performance and scalability in its design, this solution provides a viable solution for municipal authorities and smart city programs to automate road condition monitoring, thereby saving time on manual inspection and enhancing urban mobility.

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YOLO, Pothole Detection, Image Pre-processing, Post-processing, Deep Learning, Python, OpenCV

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Air pollution in urban areas is increasingly driven by vehicle emissions, which release harmful gases include such things as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and particulate matter (PM). These pollutants pose significant risks to both environmental quality and public health, highlighting the urgent need for effective monitoring and control systems. This project presents the development of an automated Effective Air Pollution Monitoring System for Eco Balance, designed to detect exhaust gas levels in real-time as vehicles pass designated points. The system employs advanced sensors to monitor pollutant concentrations and utilizes a camera- based recognition system to capture the number plates of vehicles that exceed permissible emission levels. The collected data, including pollution metrics and vehicle details, is transmitted to the Regional Transport Office (RTO) for necessary enforcement actions. By promoting compliance with emission standards, this automated system aims to significantly reduce vehicle-related air pollution, fostering a cleaner and healthier urban environment.

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Air Pollution, Vehicle Emission, Pollution Sensor, Camera-Based Recognition, Vehicle Identification, Regional Transport Office (RTO)

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The rapid expansion of the Information Technology (IT) sector has significantly contributed to the global e-waste crisis. Electronic waste (e-waste) consists of discarded electrical and electronic equipment, which, if improperly managed, poses severe environmental and health hazards. This review paper consolidates insights from various research studies, examining the challenges, technological innovations, circular economy approaches, regulatory frameworks, and socio-environmental impacts associated with e-waste management. It also highlights sustainable solutions, including Artificial Intelligence (AI)-driven recycling, blockchain-based tracking systems, and eco-design strategies, offering a roadmap for future advancements in the domain. By integrating data from multiple sources and case studies, this paper presents a holistic overview of current e-waste management trends and suggests actionable strategies for effective waste mitigation.

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Advancements in E-Waste Management Systems and Pollution Control: A Comprehensive Review in the IT Domain

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Proper attendance record-keeping is critical in schools for tracking student participation and performance. Conventional approaches, including manual registers and RFID-based systems, are prone to errors, tampering, and inefficiencies. This research proposes a Fingerprint-Based Smart Attendance Management System Using IoT, which tracks attendance automatically using biometric verification. The system utilizes an ESP32 microcontroller coupled with a fingerprint sensor to scan and authenticate student identities. Attendance records are stored and analyzed in a Python-based system with real-time tracking through a web-based interface. Additionally, when a student has an attendance level of less than 75%, an automatic warning message is sent to their parents. The proposed system improves the accuracy, stops proxy attendance, and enhances overall efficiency in handling attendance. With the integration of IoT and biometric authentication, the system guarantees a secure, scalable, and user-friendly process for tracking attendance

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Attendance Monitoring System (AMS), Automated Attendance Tracking, Cloud-based Systems, Fingerprint Recognition, Internet of Things (IoT).

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