Machine Learning for Link Prediction between Nodes in Complex Networks

Elaf Adel; Nisreen Abbas; Raaid

doi:https://doi.org/10.54216/FPA.180104

Journal of Intelligent Systems and Internet of Things | 2025

A Hybrid Speech Recognition System Using Deep Learning Methods

Speech-to-text Conversion is a type of Speech Recognition Program that effectively takes audio content as input and transcribes it into written words. With increasing technologies and large data corpus, the importance of speech recognition has increased. Now everyone seems to be exploitation Speech Recognition Technology for users to work a tool, perform commands, or write while not having to use a keyboard, mouse, or press any buttons. It is also easy for everyone to utter sound or speak than using hands to be work done and it is also convenient to use. In this paper, a system capable of converting audio files to text has been developed. The proposed system consists of a set of algorithms for processing audio files, where the MFCC algorithm combine with standard deviation was adopted to extract the features of the audio file and convert it into an image. The features of audio files are stored as images because deep learning algorithms can be trained on images better than CSV files. The second part of the proposed system is the design of a deep learning model in which two algorithms, Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are combined to predict words. The model consists of a set of layers to extract the features from the images, choose the best features, then train and classify them based on the proposed DNN model. In this thesis, three types of datasets (Arabic, English, and Real) were adopted to test the proposed system in speech prediction and the accuracy of the proposed system has reached more than 95%.

groups

Hadeel Luhaib Fouad mail -

Husam Ali Abdulmohsin mail

link https://doi.org/10.54216/JISIoT.150109

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Optimized KNN Algorithm for Diabetic Retinopathy Classification with PCA-Based Data Fusion and Cuckoo Search Optimization

Diabetes is a disease that occurs when the body is unable to use the insulin it produces effectively or the body fails to produce enough insulin. One of the most important complications of this disease is diabetic retinopathy (DR), which is considered the main cause of severe visual impairment and blindness. Previous studies have proven that the KNN algorithm is an effective algorithm for solving classification and prediction problems, as the performance of this algorithm rely on determining the value of the K parameter because the inappropriate choice of this value can negatively affect the accuracy of classification. On the other hand, adjusting this value manually is very difficult because this value depends on the state of determining the solution to the problem each time. Therefore, there is still an urgent need to use smart algorithms to adjust this value and obtain an ideal value that ultimately leads to obtaining a very high classification accuracy. In this paper, the Cuckoo Search algorithm was used, which is considered one of the smart and modern algorithms in the field of diagnosis, in addition to applying more than one technique and algorithm to build an integrated system to enhance the accuracy of diagnosis and obtain competitive diagnostic accuracy. The proposed work was implemented using the Debrecen diabetic retinopathy dataset and competitive results were obtained for recall, sensitivity, precision, F1 score, accuracy and specificity (98.05%), (97.30%), (99.01%), (98.70%), (99.70%), and (99.08%), respectively. Our results demonstrate that the Cuckoo Search algorithm is an effective and suitable choice for optimizing the parameters in the KNN algorithm, in addition to enhancing this algorithm to diagnose the disease early and support direct intervention and treatment, and this method lays the foundation for diagnosing other diseases and thus improving patient care in most related fields.

groups

Ali Azawii Abdul Lateef mail -

Ahmed Subhi Abdalkafor mail -

Ahmed Adil Nafea mail

link https://doi.org/10.54216/JISIoT.150110

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Deep Learning for Multi-Label Facial Attribute Classification on Large-Scale Image Datasets (CelebA)

The exponential growth of data in recent years has led to an increasing demand for advanced techniques, especially those that work on large and complex data. This has given deep learning a significant advance in dealing with the tasks of analyzing, improving, and distinguishing big data. Our research focused on CNNs from this data and applying deep learning algorithms and their analysis to a large-scale image dataset. More specifically, our research focused on a dataset called CelebA, which contains more than 200,000 face images annotated with 40 binary facial features. It is a multi-label classification model based on the ResNet-50 architecture that has been fine-tuned to predict different facial features and hair color such as age, gender, and facial expressions. It was also trained using data augmentation, taking into account pose differences and background clutter to reduce imbalance between classes. These results reflect very strong predictive performance, with an average mean accuracy of 0.86 and an overall F1 score of 0.81 across all features. Attributes identified by clear visual cues—for example, “smiling,” “male ”and“ wearing lipstick”—were highly accurate, while less obvious attributes such as “big lips” and “narrow eyes” were more difficult to classify. We would like to point out that the results demonstrate the high efficiency of using deep learning models for multi-label classification on big data while solving problems associated with class imbalance and overfitting models. This research leads to the larger general field of big data analytics; in particular, it demonstrates how deep learning can be efficiently applied to large image datasets for automatic attribute recognition. It also opens up potential applications in areas such as biometric identification, surveillance, and human-computer interaction.

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Sif. K. Ebis mail -

Bushra Majeed Muter mail -

Fatima Hameed Shnan mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/JISIoT.150111

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Health-Fots- A Latency Aware Fog Based IoT Environment and Efficient Monitoring of Body’s Vital Parameters in Smart Health Care Environment

Internet of Things (IoT) integrated with the disruptive technologies are becoming increasingly popular and they have extended their capabilities in all domains such as automotive, health care and automation. IoT is connecting the billions of devices and humans to bring the fruitful advantages to society. Since IoT devices are operated with the centralized cloud environment, pervasive and continuous monitoring of the user information can be facilitated. However, owing to the inherent characteristics of cloud, such as large end-to-end latency, larger bandwidth consumption, handling the larger volume of data from the IoT devices would be bottleneck for implementing the IoT for the smart health care system that aids for the treatment and diagnosis process. To address these issues, this research article proposes powerful paradigm, Heath-FoTs (Fog of things) which incorporates the fog devices where the data are processed and filtered near the IoT nodes which is useful for improving the quality of services. To further improve the speed of communication, distributed fogs are introduced between the IoT devices and Cloud to process the health care data and provides the optimal solution to tackle the latencies problems and bandwidth requirements. The complete experimentation is carried out using the NodeMCU and Raspberry Pi 3 Model in which the MQTT (Message Queuing Telemetry Transportation) protocol is used as the major communication protocol between the IoT and Fog Nodes. To evaluate the proposed model, performance metrics such as latency, throughput, and communication cost is measured and compared with the traditional environments. Results demonstrate the Health-FoTs environment has shown the promising performance with the 23% lesser latency, 32% higher throughputs and 25% less communication overhead than the traditional IoT infrastructure and proves its strong place for the high speed health care environment.

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Ponugoti Kalpana mail -

Potu Narayana mail -

Smitha, L. mail -

Dasari Madhavi mail -

K. Keerthi mail -

Aseel Smerat mail -

Muhannad Akram Nazzal mail

link https://doi.org/10.54216/JISIoT.150112

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Weekly and Monthly Forecasting Rainfall Model based on LSTM

The climate of Iraq has become increasingly variable in recent years, characterized by high temperatures and low rainfall. Rainfall plays a crucial role in agriculture in Iraq and thus affects the economy. Rainfall prediction has become essential for the favorable management of rainfall in various aspects of life. In this research, weather data were collected from Hilla station of the Climate Department of the General Authority of Meteorology and Seismology in Iraq for the period from 2012 to 2022. The data consist of several columns: date, wind speed, maximum temperature, minimum temperature, relative humidity, sea pressure, normal temperature, and rainfall. The time series data used with the long short-term memory method represents one of the most effective applications of deep learning techniques. Two LSTMs were trained the first time using all available features, which are 6 features, in addition to training the LSTM and the inputs were the influential features that gave high values in the correlation matrix (wind speed, sea pressure, and relative humidity) to achieve accuracy and reduce the prediction error of rainfall. The weekly and monthly forecasts made with the influential features outperformed the forecasts made with all features. The evaluation metric (root mean square error) showed lower error when using all data columns (RMSE = 0.05 and RMSE = 0.025) for weekly and monthly forecasts, respectively, and less errors when using only a limited number of columns (RMSE = 0.04 and RMSE = 0.01) for weekly and monthly forecasts, respectively.

groups

Kifah Hamzah Allawi mail -

Hadab Khalid Obayes mail

link https://doi.org/10.54216/JISIoT.150113

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An upgraded IoT based Mobile Ad- hoc Network enactment founded on optimized Signal Strength Based Routing algorithm

It is a wireless network with mobile nodes that function independently and communicate with one another with radio waves. When a node gets a packet, they evaluate all of the possible routes before selecting the optimal one. In this way, the capabilities of routing are incorporated into each node of the network. Researchers used ant colonies to find the best path between two sites. The Simple Ant Routing Protocol has improved with the assistant of Internet of Things (IoT). Energy Aware Simple Ant Routing Algorithm (EASARA), a new protocol, considers each node's energy usage. The change improved routing overhead and packet delivery ratio. It also improved communication. EASARA performed better with more hosts. Traffic congestion statistics followed. One parameter estimated host power reserve and connection congestion. Energy-congestion conscious protocol is basic routing algorithm is ECSARA (Energy-congestion based Simple Ant Routing Algorithm). The protocol improves with more hosts. It sends packets faster. Hence, transmission data transfer increased. Energy savings extended the route's lifespan. Signal strength predicted connection failures. This metric chooses a new route. Signal-to-Anchor-Receiver-Attendance based Simple Ant Routing Algorithm is SS-SARA. All host monitors detected strong signals. Communication uses substitute pathways when signal strength goes below a threshold. The protocol improved packet delivery and throughput on congested networks, according to experiments.

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Ayat Aljarrah mail -

Mustafa Ababneh mail -

M. Karthiga mail -

Krishna Bhimaavarapu mail

link https://doi.org/10.54216/JISIoT.150114

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

Blockchain technologies for UAV swarms and UAV-based networks, SLR

Unmanned aerial vehicles (UAVs) and swarms unmanned aerial vehicles (UAVs) have recently shown themselves capable of providing dependable and reasonably priced solutions for a variety of real-world issues. UAVs provide a wide range of services due to their autonomy, adaptability, mobility, and communications interoperability. Despite the fact that UAVs are frequently used to facilitate ground communications, data exchanges inside those networks are susceptible to security threats due to the ease with which radio and Wi-Fi signals can be hacked. However, there are many ways to stop cyberattacks. One of the potential methods to enhance user privacy, data security, and authentication—especially in peer-to-peer UAV networks—may be blockchain technology, which has lately gained prominence. Using the benefits of blockchain technology, several entities can communicate in a decentralized. This paper uses some supporting technologies to provide a thorough overview of privacy and security integration in blockchain-assisted swarm and UAV networks. For this goal, this work is compared to earlier research to find effective solutions, and blockchain technology is integrated to improve the capacity of swarm UAV networks and communication to move, manage, and exchange data. We conclude by talking about open research issues, the limitations of the UAV standards as they stand right now, and possible research paths in the future. This comprehensive review is an invaluable tool to know study and analyze a good number of reviews and research papers in recent years to overcome obstacles and find appropriate solutions for integrating UAV swarms with block chain Technology.

groups

Naser Abbas Hussein mail -

Jihene khoualdi mail -

Khadija Rammeh Houerbi mail -

Hella Kaffel Ben Ayed mail

link https://doi.org/10.54216/FPA.180101

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Using Robotic Arm as Sidekick to the Teacher in Classroom

The lack of practical teaching tools, such as a robotic arm, hinders students' understanding of complex concepts in robotics courses, where hands-on experience is essential for effective learning. This study introduced a 6DOF Robotic Arm as a teaching aid to address this issue, evaluating its impact through an experimental study with 30 computer science students. The findings revealed that the robotic arm effectively enhanced both basic and advanced Arduino programming skills, with students who used it performing better and expressing higher satisfaction than those who did not. The study also identified gaps in hardware control comprehension, leading to software development that could further aid in mastering programming concepts. The paper concludes with a discussion of the potential of the robotic arm as a valuable educational tool and its implications for future research and practical applications.

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Mona Esmat mail -

Amira Atta mail

link https://doi.org/10.54216/FPA.180102

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

CORRECTED VERSION: Integrating a Secure and Low-Cost WSN Layer with Medical Cloud Computing for Medical Image Transmission

Throughout a Wireless Sensor Network (WSN), information collected from the environment is continuously transmitted from one node to the next, and then the main collector or server receives and processes it. With the growth of a network, data transfers within the network also grow dramatically. Medical images increase traffic on a network if they are transmitted. An interlayer transmission protocol (WSN) was developed for this study. Pixels are used to create the medical image using the protocol. A gray-level medical image with 512x512 pixels provided by Brain was used to conduct the study. Medical image size is reduced from 256 KB to 192 KB, providing a 25% advantage. A study found SSIM of 51, 1365 and PSNR of 0,9976 for the structural similarity ratio (SSIM). The Advanced Encryption Standard (AES) encryption algorithm safeguards data during the transfer. By creating such a layer, transmissions became safer. In the WSNs, 12.5% and 25% of the data transfer has been reduced based on the information obtained from the study without changing the medical image.

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Israa Hussain Abd Alla mail -

Falath M.Mohammed mail -

Saif Al-din M. N mail -

Azmi Shawkat Abdulbaqi mail

link https://doi.org/10.54216/FPA.180103

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning for Link Prediction between Nodes in Complex Networks

Recently, the complex network has become popular use as it can transfer huge amounts of multimedia, text, ideas, and other information, encouraging many participant connections. Social media is one of these networks that make the most connections. Predicting the formation or dissolution of links between nodes presents a problem for social network analysis researchers. Since social networks are dynamic, this task is exciting as it may also forecast lost network links with less information. On the other way, current link prediction methods use simply node similarity to find links. This study proposes a new technique that relies on node attributes and similarity measures. Nodes are labeled by their centrality and similarity. The network's edges are negative and positive samples. A well-defined dataset for link prediction comprises the features of the nodes at the edges labeled either positive or negative. The dataset is passed to multiple machine learning classifiers. On several real-world networks. The experiments conducted during the research show that Gradient Boosting gave the highest accuracy of 99% compared with other methods.

groups

Elaf Adel Abbas mail -

Nisreen Abbas Hussein mail -

Raaid Alubady mail

link https://doi.org/10.54216/FPA.180104

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new