Artificial Intelligence based Automated Sign Gesture Recognition Solutions for Visually Challenged People

Khalid Hamed

doi:https://doi.org/10.54216/JISIoT.140211

Journal of Intelligent Systems and Internet of Things |

Prediction and Classification of Fatty Liver Disease Using Probabilistic Neural Networks

Fatty liver disease, encompassing conditions like NAFLD (Non-Alcoholic Fatty Liver Disease) and NASH (Non-Alcoholic Steatohepatitis), is a significant global health issue linked to metabolic syndrome and increasing incidences of liver-related complications. Accurate and early detection of fatty liver illness is critical for effective intervention and management. This paper proposes a novel method for the prediction and arrangement of fatty liver disease using Probabilistic Neural Networks (PNNs), leveraging advanced machine learning techniques to enhance diagnostic accuracy and reliability. We developed a PNN-based model to classify liver conditions from a dataset comprising clinical and imaging features, including liver fat content, texture metrics, and demographic information. The PNN was chosen for its capability to handle complex, high-dimensional data and provide probabilistic outputs, which are crucial for assessing the likelihood of different disease stages and improving interpretability. The proposed methodology includes preprocessing steps to normalize and augment the data, followed by feature extraction using advanced techniques to capture relevant patterns. The PNN architecture was designed with multiple layers to process features and deliver class probabilities. The method's concert was estimated utilizing average system of measurement such as accuracy, precision, recall, and F1-score, demonstrating its efficacy in distinguishing between different stages of fatty liver disease. Experimental results indicate that the PNN model achieves high classification accuracy and outperforms traditional machine learning methods in detecting fatty liver illness. This study highlights the potential of PNNs in enhancing diagnostic processes and providing a robust tool for clinicians. Future work will concentrate on expanding the dataset, refining the model, and integrating it into clinical workflows to support better patient outcomes in liver disease management

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Appanaboyina Sindhuja mail -

Seetharam Khetavath mail

link https://doi.org/10.54216/JISIoT.140207

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Automated Detection and Classification of Pneumonia using Deep Learning and Convolutional Neural Networks

Lung disease is considerable deprivation from health standpoint. These include chronic obstructive pulmonary illnesses, asthma, lung fibrosis, lung parenchyma illnesses, and tuberculosis among others. It is highly critical in the early phase of lung illnesses when they are the most treatable. Many of these were made for the purpose of applying machine learning and image processing. Many types of DL methods including CNN, VNN, VGG networks, capsule networks are used during lung illness prediction process. Following the release of the book on Pandemic Covid-19, many projects have been carried out at international level intending to study the feasibility of such work for prediction of future events. Pneumonia is a lung infection that starts earlier in the disease course and is closely associated with the virus (pneumonia condition), which was responsible for considerable chest infection in some covid-positive individuals. While doctors are no strangers to lung diseases and their complicated nature, many will find it difficult in some of them to make distinctions between common pneumonia and the Covid-19. X-ray imaging of the chest provides the highest degree of accuracy in suffem lung diseases. In this work, a novel approach for the calculation of lung illnesses such as pneumonia and COVID-19 is proposed. The data source for this method is Chest X-ray pictures taken from patients. The system includes characteristics such as the extraction of features, the prediction of illnesses, and the precise and adaptive evaluation of ROI, the collecting of datasets, and the enhancement of image quality. In future, this research can be extended with IOT devices for the recognition of COVID-19 and pneumonia.

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Gurijala Anita mail -

Sunil Singarapu mail

link https://doi.org/10.54216/JISIoT.140208

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Research on Big Data efficient hybrid cloud storage model and algorithm based on 5G network

Due to the large demand for big data storage capacity, the storage intensity index is not calculated in the current big data cloud storage process, resulting in a high storage space usage. This paper proposes a big data efficient hybrid cloud storage model and algorithm under 5G network. The model is based on the 5G network performance framework and consists of three parts: users, private cloud and public cloud storage service providers. The purpose of efficient hybrid cloud storage of big data is achieved by using consistent hash algorithm. The simulation results show that the above algorithm occupies less storage memory space, the device load variance is smaller, the overall system load is more stable and balanced, and the average response is fast, which provides a favorable basis for the efficient hybrid cloud storage algorithm of big data.

groups

Lei Hu mail -

Yangxia Shu mail

link https://doi.org/10.54216/JISIoT.140209

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2025

Adversarial Machine Learning Challenges in Modern Network Security Systems

Hostile machine learning has network security issues that reduce prediction model accuracy. A full defence against these assaults entails establishing hostile scenarios, strengthening models via strategy training, and applying powerful defences. Small adjustments introduce antagonistic inputs into the research. These teach the model to recognize and withstand deception attempts. The proposed solution competed with Trust Shield, Secure Guard, Defend, and Adversary Block in rigorous performance testing. The recommended strategy has a 95.0% success rate for discovering assaults and a much lower 5.0% false positive rate. This is much superior to conventional approaches. Due to its modest accuracy loss and rapid response, it's effective at fighting assaults. This comprehensive overview demonstrates the wide-scale application of the strategy with minimal resources. Finally, this research emphasizes the need for robust and adaptable AI security. This will assist in creating secure and trustworthy AI solutions to protect sensitive data and ensure prediction model accuracy in an increasingly hostile future.

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Lissett Margarita Arévalo Gamboa mail -

Alberto León-Batallas mail -

Jhonny Ortiz-Mata mail -

Denis Mendoza-Cabrera mail

link https://doi.org/10.54216/JCIM.150201

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Predictive-based Models for Efficient Energy Management in Smart Buildings

The integration of sensing technologies with residential buildings raises the concept of a smart home, which has facilitated the life of the habitant nowadays. This technology helps us to track and understand the behavior of the client in the house to give him maximum comfort. A neighborhood area is an interconnected set of houses that exist in the same geographical region and share the same energy resources. The most important component in the process of decision-making is the energy usage in the smart building. The energy optimization problem in the smart building created a challenge for enterprises and the government for a long time. A lot of research were made to solve this energy optimization problem. One of these problems is the organization of energy usage within a neighborhood area network. The main challenges are to maintain the user comfort in each house and to not exceed the total energy offered to the network. For this, we proposed a technique that predicts, based on historical data of each house, its future behavior and created for each one a weekly schedule with hourly annotated field with: high, normal, or low, where each one represents the amount of energy user is able to use at this time. At the end, an incentive-based program is created to give the client an incentive on his bill if he used the daily high energy consumption in the annotated high in his schedule. To create the schedules, we extracted some features from the data, then we used the genetic algorithm to create schedules, then we did an improvement to the technique using dynamic programming that stores the features of a house with created schedule, later when we meet a similar house we can directly give a schedule that fits the need.

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Osama Mohammed mail -

Marwa Ibrahim mail -

Abdalrahman Fatikhan Ataalla mail

link https://doi.org/10.54216/JCIM.150202

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Smart Energy Management in Green Cloud Computing using Machine Learning Algorithms

Cloud computing has many advantages as well as some disadvantages. An internet connection is required to use Cloud Computing. In other words, it is not possible to access the data in cases without internet. Cloud Computing can provide infrastructure services, platform services and software services to individuals with any device connected to the internet. If the connection speed is low when there is internet, the data transmission is also slower. In this context, it may not be practical for individuals or institutions to benefit from Cloud Computing in places where internet connection is low, limited, or absent. A new technology was obtained in this study; this method depends on deep learning and machine learning techniques applied to detect the attacks in the cloud computing-based systems. The suggested method compared with many traditional machine learning techniques.

groups

Assel Hashim Salman mail -

Abdullahi Abdu Ibrahim mail

link https://doi.org/10.54216/JCIM.150203

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Dual Convolutional Neural Network for Skin Cancer Classification

Skin cancer detection through deep learning is an evolving field, where convolutional neural networks (CNNs) have proven to be very effective in feature extraction. However, this approach still faces some limitations due to the use of data augmentation, It is the generation of artificial images. Which significantly increase the computational load without generate new clinically meaningful data and may introduce shadowed features. Therefore, this study aims to propose a new approach that use CNNs to extract important features from skin cancer medical images using the HAM 10000 dataset. The proposed approach involves training two different CNN architectures, extracting features from convolutional layers, and then use PCA to make the retrieved features less dimensional. In order to categorize skin cancer into seven different categories of skin lesions, the remaining features are then merged and fed into a classifier that uses neural networks. In comparison to earlier studies that employed CNN architectures on the same dataset, the results demonstrated that this method preserves significant information while improving computational efficiency and achieving superior classification performance. The suggested approach achieved 95.66% accuracy for multi-class classification.

groups

Raya Sattar Shahadha mail -

Belal Al-Khateeb mail

link https://doi.org/10.54216/JCIM.150204

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Quantum Machine Learning for Video Compression: An Optimal Video Frames Compression Model using Qutrits Quantum Genetic Algorithm for Video multicast over the Internet

The transmission of video is greatly aided by video compression. Redundancy (spatial, temporal, statistical, and psycho-visual) within and between video frames is something that video compression approaches aim to get rid of. The degree to which similarity-based redundancy exists between consecutive frames, however, is a function of how often the frames are sampled and how the objects in the scene are moving. Existing neural network-based video compression approaches rely on a static codebook to perform compression, which prevents them from adapting to new video’s data. In order to create an optimal codebook for vector quantization, which is then employed as an activation function inside a neural network's hidden layer, this research offers a modified video compression method based on a Qutrits based Quantum Genetic Algorithm (QQGA). Using quantum parallelization and entanglement of the quantum state, QQGA is capable of solving the same set of problems as a traditional genetic algorithm while considerably accelerating the evolutionary process. The technique is built on the concept of utilizing Qutrits (three-level quantum system) to represent population individuals. The evolution operator, which is responsible for the updates to the quantum system state, has been constructed using a straightforward approach that does not need a lookup table. Compared to qubit, qudit provides a larger state space to store and process information, and thus can enhance the algorithm’s efficiency. To create the context-based initial codebook, the background subtraction algorithm is used to extract moving objects from frames. Moreover, important wavelet coefficients are compressed losslessly using Differential Pulse Code Modulation (DPCM), whereas low energy coefficients are compressed lossy using Learning Vector Quantization neural networks (LVQ). To obtain a high compression ratio, Run-Length Encoding is then used to encode the quantized coefficients. In comparison to the conventional evolutionary algorithm-based video compression method, experiments have shown that the quantum-inspired system may achieve a greater compression ratio with acceptable efficiency as evaluated by PSNR.

groups

Oday Ali Hassen mail -

Huda Lafta Majeed mail -

Mohammed Abdulhasan Hussein mail -

Saad M. Darwish mail -

Omar Al-Boridi mail

link https://doi.org/10.54216/JCIM.150205

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model

As online education has become increasingly prominent, the primary objective of this study is to evaluate students' opinions of online classes taught by teachers with no prior experience in online teaching, focusing on their teaching style, teaching efficiency, and pedagogy in the online classroom. Online teaching is a kind of teaching system that depends on network management technology. It concludes the teaching method by the process of live courses or recorded courses employing software containing special online teaching environments and any APP software employed for teaching. Social media, with its massive pool of user-generated content and instant feedback, offers a great opportunity to calculate teaching styles in online class management. Therefore, this study offers a Social Media Based Evaluation of Teaching Style in Online Education Systems using Heuristic Search (SMBETS-OESHS) Algorithm. The main objective of the SMBETS-OESHS technique for evaluate teaching styles in online education systems using insights derived from social media platforms. At primary stage, the SMBETS-OESHS model takes place linear scaling normalization (LSN) is implemented for scaling the input data. Next, the bayesian optimization algorithm (BOA) based feature selection process can be employed to allow for the detection of the most relevant features from the data. In addition, the SMBETS-OESHS model exploits stacked sparse autoencoder (SSAE) technique for classification process. In order to achieve optimal performance, the SSAE model parameters are fine-tuned using the improved beetle optimization algorithm (IBOA), ensuring robust evaluation accuracy. The experimental validation outcome of the SMBETS-OESHS algorithm undergoes and the performances are examined over various measures. The simulation outcome stated that the enhanced solution of the SMBETS-OESHS system over the recent techniques.

groups

Walaa Fouda mail -

Sanjar Mirzaliev mail -

Reneh Abokhoza mail

link https://doi.org/10.54216/JISIoT.140210

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Artificial Intelligence based Automated Sign Gesture Recognition Solutions for Visually Challenged People

Gesture recognition is employed in human-machine communications, enhancing human life with impairments or who depend on non-verbal instructions. Hand gestures role an important role in the field of assistive technology for persons with visual impairments, whereas an optimum user communication design is of major importance. Many authors with substantial development for gesture recognition modeled several methods by using deep learning (DL) methods. This article introduces a Robust Gesture Sign Language Recognition Using Chicken Earthworm Optimization with Deep Learning (RSLR-CEWODL) approach. The projected RSLR-CEWODL algorithm majorly focuses on the recognition and classification of sign language. To accomplish this, the presented RSLR-CEWODL technique utilizes a residual network (ResNet-101) model for feature extraction. For optimal hyper parameter tuning process, the presented RSLR-CEWODL algorithm exploits the CEWO algorithm. Besides, the RSLR-CEWODL technique uses a whale optimization algorithm (WOA) with deep belief network (DBN) method for the sign language recognition method. The simulation result of the RSLR-CEWODL algorithm is tested using sign language datasets and the outcome was measured under various measures. The simulation values demonstrated the enhancements of the RSLR-CEWODL technique over other methodologies.

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Khalid Hamed Allehaibi mail

link https://doi.org/10.54216/JISIoT.140211

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new