Inclusive Communication through Real Time AI Sign Language Translation
Smart Route-FW: AI-Based Dynamic Routing and Firewall System for Adaptive Network Security
A Hybrid Elliptical Curve Cryptography Technique for Secured Communication
IoT-Enabled Autonomous Communication Framework for Embedded Electronic Systems
Next Generation 6G Communication: A Review of Technologies, Applications and Open Issues
Emerging Trends in Cybersecurity
Secure Data Transmission using Cryptographic and Steganographic Technique
Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
A Comprehensive Study on YouTube Spam Comments Recognition using Python, AI and ML
Enhancing Source Location Privacy in Networked Systems: A Review
Smart Route-FW: AI-Based Dynamic Routing and Firewall System for Adaptive Network Security
Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
IoT-Enabled Autonomous Communication Framework for Embedded Electronic Systems
Secure Data Transmission using Cryptographic and Steganographic Technique
A Hybrid Elliptical Curve Cryptography Technique for Secured Communication
Sign language is the primary mode of communication for more than 4.6 million people globally, yet communication barriers between signers and non-signers persist across essential domains such as healthcare, education, employment, and public services. Existing sign language recognition systems suffer from high latency, platform dependency, limited vocabulary, and poor generalization to real-world environments. This research presents a real-time American Sign Language (ASL) translation system integrating a Convolutional Neural Network (CNN) with MediaPipe Hand Tracking for robust and efficient gesture interpretation. The proposed model is trained and validated using the Kaggle American Sign Language Alphabet Dataset and the Kaggle Sign Language MNIST Dataset, enabling strong generalization and improved recognition stability. The system achieves sub-500 ms end-to-end latency and 95%+ accuracy across varying lighting conditions and user hand morphology. A Flutter front end ensures cross-platform deployment, while a hybrid edge-cloud architecture supports both online and offline operation. Experimental evaluation demonstrates improved performance compared to legacy solutions, positioning this work as a scalable and inclusive tool for bridging communication gaps between deaf individuals and the broader community.
The escalating sophistication of cyber threats demands adaptive and intelligent defense mechanisms beyond static routing and fixed firewall configurations. This paper presents SmartRoute-FW, an Artificial Intelligence (AI)–based dynamic routing and firewall framework that provides real-time protection through automated learning and decision- making. The system leverages machine-learning algorithms to monitor network conditions, predict anomalies, and proactively mitigate risks. SmartRoute-FW dynamically adjusts routing paths according to congestion, bandwidth, and threat levels, while simultaneously optimizing firewall rules through AI-based policy updates. Experimental evaluations demonstrate significant improvements in detection accuracy, lower false-positive rates, and enhanced network throughput when compared with conventional techniques. The results establish SmartRoute-FW as an adaptive, scalable, and self-learning security model suitable for modern enterprise and cloud environments.
The rapid expansion of IoT devices, cloud-based services, and mobile platforms has intensified the need for cryptographic systems that balance strong security with low computational overhead. This paper presents a fully redesigned hybrid encryption framework that integrates Elliptic Curve Diffie–Hellman (ECDH) for secure key establishment with high-performance symmetric encryption using AES-GCM and ChaCha20. The proposed model leverages ECC's compact key sizes to reduce computational complexity while providing multi-layered confidentiality, integrity, and authenticity. Experimental evaluation demonstrates a 25–40% improvement in encryption latency, reduced memory usage, and better scalability compared to conventional RSA-based and standalone symmetric approaches. The framework is lightweight, attack-resilient, and optimized for constrained environments such as IoT nodes, wireless sensor networks, embedded controllers, and smart healthcare devices. The study concludes that integrating ECC with modern symmetric primitives forms a robust architecture capable of addressing emerging cyber threats, with future enhancements planned toward post-quantum resilience and adaptive cryptographic mechanisms.
The Internet of Things (IoT) is transforming the landscape of embedded systems by enabling intelligent and autonomous communication between electronic devices. This paper presents the design and implementation of a real-time IoT- based system that facilitates autonomous interaction among embedded devices using ZigBee wireless communication conforming to IEEE 802.15.4 standards. The framework leverages a customizable System-on-Chip (cSoC) architecture with advanced microcontroller features for energy-efficient environmental monitoring and data transmission. The proposed work emphasizes modularity, scalability, and reliability in real-world applications. The methodology includes detailed hardware and software components, data acquisition procedures, and transmission metrics. Experimental results validate performance in terms of sensor accuracy, power consumption, data size, and transmission latency. Limitations and challenges are also discussed to guide future enhancements. The framework demonstrates suitability for deployment in smart environments, including agriculture, home automation, and industrial monitoring.
The world of wireless communication is moving quickly toward the sixth generation (6G), which is expected to bring much more powerful features than today's 5G networks. While 5G supports fast internet, reliable connections, and massive use of smart devices, the growing demand for intelligent applications needs even better solutions. 6G will make use of new technologies such as terahertz (THz) communication, reconfigurable intelligent surfaces (RIS), visible light communication (VLC), integrated sensing with communication (ISAC), and artificial intelligence (AI)-based networking. These will enable advanced applications like holographic communication, digital twins, extended reality (XR), smart healthcare, self-driving systems, and global space-air-ground networks. However, there are still many open challenges, including limited spectrum, high energy use, hardware cost, security, privacy, and lack of standards. This paper gives a clear overview of the main technologies, possible applications, and open issues of 6G communication and will help researchers and engineers to understand future directions in this field.
