A Real-Time Sign Language Recognition Framework Using Deep Learning and Internet of Things

Lama Al; Soukeina

doi:https://doi.org/10.54216/FPA.210225

Journal of Intelligent Systems and Internet of Things | 2025

A Secure Biometric Passkey Pipeline Combining Continuous Thinking Machine Models with Post-Quantum and Neuro-Symbolic Cryptography

The generation of cryptographic keys from biometric traits offers a secure alternative to password-based authentication, but is hindered by challenges related to entropy, reproducibility, and adversarial resistance. This work presents a dual-path framework in which a Continuous Thinking Machine Model (CTMM) extracts multimodal embeddings from iris and fingerprint data. Feature vectors undergo projection through principal component analysis and graph-based distance encoding, followed by chaotic sequence modeling with Lorenz-like dynamics and an error-correcting routine to stabilize bitstreams. A secure mixing function consolidates the outputs, while SHA3-512 ensures deterministic expansion. Final passkeys are generated using the Kyber512 post-quantum key encapsulation mechanism (KEM), with neuro-symbolic reasoning applied as a validation layer to enforce entropy, avalanche properties, and inter-user separation. Evaluation confirmed compliance with NIST statistical tests, including monobit, runs, and longest-run assessments, while the system maintained a near-zero false acceptance rate. The originality of this work lies in combining CTMM-driven multimodal feature extraction with a quantum-safe cryptographic pipeline, augmented by neuro-symbolic validation, to establish a reproducible and secure method for biometric passkey generation in high-assurance authentication contexts.

groups

Nahla Abdulnabee Sameer mail -

Bashar M. Nema mail

link https://doi.org/10.54216/JISIoT.170217

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Decision-Making Approach by Using Choice Value and Weighted Choice Value of Interval-Valued Fuzzy Sets

This paper tackles the difficulty of accurately modeling uncertainty in complicated DM settings, where conventional FS models frequently fail. The IVFS theory, a broadening FS theory, is a potent tool that can offer the potential to approach uncertain data in vague environment in order to get over these restrictions. This paper presents an application of IVFS in a DM challenges, where on CV and WCV of an IVFS are used to select a qualified applicant for the HR manager position. Additionally, sensitivity analysis has demonstrated the stability of the final decision.

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Bhavani Gokila D. mail -

Vijayalakshmi V. M. mail

link https://doi.org/10.54216/IJNS.270123

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

Intelligent Tutoring System to Establish Hand Knitting Skill in Home Economics Students

This study proposes an Intelligent Tutoring System (ITS) to enhance hand-knitting skills among Home Economics students through AI-driven personalized learning, addressing the limitations of traditional generic methods. The system integrates computer vision, adaptive algorithms, and interactive tutorials to provide real-time feedback and track progress. A study involving 60 students (30 control, 30 experimental) showed the ITS group achieved significantly higher post-test scores, confirming improved proficiency and engagement. Results reveal that the IT IS effectively accelerates skill acquisition and deepens understanding compared to conventional instruction.

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A. F. Elgamal mail -

S. S. Al-Saidi mail -

S. A. Abdelsamie mail -

A. A. A. Kamel mail

link https://doi.org/10.54216/FPA.210224

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

A Reinforcement Learning Framework for Adaptive Detection of Phishing Attack

Phishing is one of the most dominant forms of cybercrime, with over half a billion incidents occurring annually. It remains one of the most insidious forms of fraud due to its effectiveness. Phishing attacks are on the rise with increasingly deceptive tactics, often leading unwitting victims to divulge personal information. Phishing frauds also involve website phishing, which mimics legitimate sites. Despite the best user training and practices, people still fall for these frauds. The methodology of detecting phishing attacks using the blacklisting approach was not very effective since these URLs are active for a limited period. Hence, Machine Learning methods were used for detecting the phishing attempt. Machine learning solutions are not adaptive to changes in the approach and are biased towards the developed solution. In addition, there is a need to develop a solution to this constantly evolving phishing attack. The proposed system is an attempt to use reinforcement-learning methodology as the solution to detect phishing. It has trained an adaptive intelligent learning system based on previous experiences using the Q-learning algorithm. The system focuses on dynamically selecting the relevant features and the classification model. The agent is trained to select optimal features and classification models dynamically based on Q-learning algorithm. In contrast to static methods, the proposed system continuously adapts its strategy of combinations feature subsets and classification models as defense against the rapidly evolving attacks. The system aims to supplement existing cybersecurity measures with an adaptable tool capable of countering sophisticated phishing schemes. The experimental analysis shows that the proposed methodology attained an accuracy of 99.25%, demonstrating its high performance in phishing detection.

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Sharvari Patil mail -

Narendra M. Shekokar mail -

Aditya Surve mail -

Priyanka Ramchandran mail

link https://doi.org/10.54216/JCIM.170215

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid AI Ensemble for Real-Time Adaptive Optimization in Solar Energy Systems

Solar energy systems play a crucial role in fulfilling global energy needs sustainably; however, their performance is often affected by dynamic environmental factors. This study investigates the use of Artificial Intelligence (AI) for real-time optimization and adaptive control to improve the operational efficiency of solar energy systems. The research specifically addresses output variability arising from fluctuations in solar irradiance, temperature, and panel soiling, limitations that conventional control approaches fail to manage effectively. The primary goal is to develop intelligent AI-based models capable of predicting and automatically adjusting critical system parameters in real time, thereby reducing manual intervention and enhancing operational reliability. Data from a solar photovoltaic (PV) and thermal hybrid testbed in Jodhpur, India were collected over a six-month period. The Indian Meteorological Department provided more than 10000 hourly data samples that included weather and seasonal variations. An NI DAQ system with high-precision sensors was used to measure important parameters such as solar irradiance panel, and ambient temperatures wind speed inclination angle and energy output. For predictive control, the suggested methodology uses a hybrid ensemble framework that combines Extreme Gradient Boosting (XGBoost), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Deep Neural Networks (DNN). In this framework, XGBoost carries out variable importance ranking to determine the dominant influencing factors ANFIS enables adaptive operational control and DNNs forecast energy output. In contrast to previous research that concentrated on distinct AI methods this work presents a cohesive hybrid approach that integrates feature significance analysis adaptive optimization and forecasting accuracy into a single system. The hybrid ensemble model outperforms individual approaches in achieving stable and effective energy generation according to evaluation using RMSE, R2, and MEF metrics. Furthermore, its compatibility with IoT-enabled edge devices underscores its potential for large-scale, real-time, and automated solar energy management within future smart grid infrastructures, advancing global efforts toward sustainable energy transitions.

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Srinivasa Chanakya Muramshetti mail -

Kishore Kunal mail -

R. Murugadoss mail -

Vairavel Madeshwaren mail

link https://doi.org/10.54216/JISIoT.170218

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Low-Cost Multi-Sensor Localization for Indoor AGVs

Indoor transportation systems are a key area of development, where Automated Guided Vehicles (AGVs) help to increase efficiency and reduce labor costs. However, high-precision positioning technologies such as LiDAR and GNSS are expensive, making them unsuitable for widespread use. This research has developed a low-cost positioning system for indoor AGVs using multiple sensors, including CCTV, UWB, inertial measurement units (IMUs), and encoders. The experiment was carried out under both static and dynamic conditions. In static tests, Trilateration distance measurements show a lower positioning error than the triangular method, with a maximum error of 1.4464 m (x-axis) and 1.0464 m (y-axis) in dynamic tests. The integrated Encoder and IMU sensor data yielded the lowest error (RMSE = 0.0732 m at 0.4 m/s, 0.0678 at 0.27 m/s), Next is CCTV, while UWB has the highest error rate. The application of a Parallel Sensor Fusion architecture optimized using a Generalized Reduced Gradient (GRG) nonlinear algorithm, significantly reduced localization errors. The RMSE values decreased to 0.0623 m (0.4 m/s) and 0.0411 m (0.27 m/s). The results, in a controlled environment laboratory, indicate that combining multiple sensors will improve the positioning accuracy. Combining the encoder and IMU effectively reduces accumulated errors and increases system stability. While Adjust the weight of the sensor offline, this proposed system offers a cost-effective positioning solution for indoor AGVs, which contributes to the development of affordable and accurate AGV navigation systems.

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Nopparut Khaewnak mail -

Siripong Pawako mail -

Akkharachai Kosiyanurak mail -

Suradet Tantrairatn mail -

Jiraphon Srisertpol mail

link https://doi.org/10.54216/JISIoT.170219

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhanced Lightweight Cryptography-based Authentication Protocol for IoT Devices

The rapid advancement of telecommunication infrastructures and endpoint technologies has led to a significant incorporation of Internet of Things devices in modern lifestyles. IoT involves a wide range of applications, such as connected video surveillance systems for security, wearable body sensors for health monitoring, and temperature sensors for environmental control in agricultural fields. These devices are essential for gathering and transmitting data in real-time. However, data acquisition and transmission processes are often exposed to serious security threats, particularly concerning data integrity, user privacy, and communication reliability. Conventional security mechanisms are typically inappropriate to resource constrained IoT devices. Thus, to overcome these challenges, extensive research has been devoted to developing secure communication frameworks, with a particular focus on robust authentication and key agreement protocols. Authentication is essential to guarantee the legitimacy of the information source, and many proposed AKA schemes rely on asymmetric cryptographic techniques. In this paper, we introduce an Enhanced Lightweight Cryptography-based Authentication Protocol for IoT devices, conceived to meet the computational constraints of IoT devices by employing simple XOR and hashing operations. The protocol enables mutual authentication between IoT devices and routers without the need to share credentials directly. Prior to authentication, an offline registration phase is conducted through an Authentication Server (AS), which generates unique key parameters based on the identifiers of the devices and routers. These parameters are securely distributed to both parties. Authentication is then performed using these pre-shared parameters in a computationally efficient yet secure manner that safeguards against common security threats. Theoretical analysis demonstrates that the proposed protocol is resistant to several common attacks, including man-in-the-middle, impersonation, session key disclosure, replay, and eavesdropping attacks. Additionally, the protocol ensures device anonymity and data privacy while maintaining lightweight performance suitable for constrained IoT environments.

groups

Sanâ Elaoudi mail -

Marouane Sebgui mail -

Slimane Bah mail

link https://doi.org/10.54216/JISIoT.170220

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Developed Non-Polynomial Spline Method for Solving Fuzzy Partial Differential Equations

This research introduces a novel approach to the non-polynomial spline dependent method for solving fuzzy partial differential equations. The tensor product of non-polynomial spline functions is derived in order to obtaining a solution to fuzzy partial differential equations, such as fuzzy hyperbolic and parabolic equations. The advantage of this method is that it simplifies the complex procedure that arises from the term of the typical product of a fuzzy number by fuzzy functions. Examples are presented to show that the outcomes of the research indicate that the technique is extremely useful to construct the solution to the desired fuzzy partial differential equations.

groups

Ahmed Hanoon Abud mail -

Laheeb Muhsen Noman mail -

Ahmed Bakheet mail

link https://doi.org/10.54216/IJNS.270124

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

MFWX: Multi-Scale CNN with Multi-Frequency Channel Attention and Weighted Particle Swarm Optimization for Enhanced Brain Tumor Segmentation and Classification

The research-automated segmentation of brain tumors occurs due to the need to enhance diagnosis and/or treatment planning. The existing techniques suffer the effects of scale variation, redundant features, and the high dimensionality that causes ambiguous findings. We suggest the model named MFWX, which unites Multi-Scale CNN, Multi-Frequency Channel Attention (MFCA), Weighted Particle Swarm Optimization (WPSO) to identify features and XGBoost methods to classify them. The Multi-Scale CNN will capture the structure of the tumor at multiple resolutions, MFCA adjusts the features by zeroing in on significant frequency zones and WPSO eliminates redundancy to heavy-hit the strong forecasts of XGBoost. However, MFWX attained 94.2 accuracy and 92.5 Dice on the BraTS-2020 dataset surpassing ResNet50, EfficientNet-B7, and U-Net. It achieved an accuracy of 96.7%, and Dice of 95.1% on BraTS-2018, and performed well on classes of tumors. Ablation experiments proved the necessity of every part. In general, MFWX presents an efficient, clinically meaningful, scalable solution that outsmarts the current segmentation techniques.

groups

Mohammed Nazneen Fathima mail -

Prabhjot Singh1 mail -

Simrandeep Singh mail

link https://doi.org/10.54216/JISIoT.170221

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

A Real-Time Sign Language Recognition Framework Using Deep Learning and Internet of Things

  Sign language is a vital communication mean for hearing-impaired individuals, combining manual gestures with non-manual signs like facial expressions and body movements, often requiring both hands and sequential actions. Recently, an automatic Sign Language Recognition (SLR) has gained increasing attention, with Machine Learning and Deep Learning systems achieving competitive performance. While convolutional neural network has been widely employed owing to their effectiveness in image-based recognition tasks, existing methods, however, often struggle with efficiency, adaptability, and real-time deployment. This paper proposes an Internet of Things-Integrated Deep Learning Model for Real-Time SLR to enhance the communication among individuals with hearing-impairment and non-signers. The framework employs IoT-based wearable sensors for capturing hand and finger movements, followed by Sobel filtering for noise reduction. MobileNetV3 is applied for lightweight feature extraction, while a Variational AutoEncoder enables robust sign detection. To further improve performance, an Improved Sparrow Search Algorithm is introduced for hyperparameter tuning, constituting the novelty of this work. Experimental results show that the proposed framework achieves an outstanding accuracy of 99.05% when compared to state-of-the-art systems, validating its robustness and effectiveness for real-time SLR applications

groups

Lama Al Khuzayem mail -

Soukeina Elhassen mail

link https://doi.org/10.54216/FPA.210225

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new