HI2NN: Heuristic Intelligence towards Enhancing Rainfall Prediction with Improved Artificial Neural Networks

Sachin Subhashrao; Sonali

doi:https://doi.org/10.54216/FPA.190221

Fusion: Practice and Applications | 2025

Enhancing IoT Intrusion Detection with a Hybrid Deep Learning-Evolutionary Algorithm: An Ensemble Strategy Approach

In the context of dynamic and highly diverse IoT (Internet of Things), the nature of threats and the amount of data that needs to be processed by IDSs (Intrusion Detection System) have become much greater and represent considerable problems for modern security systems. This work presents a new method called a Hybrid Deep Learning-Evolutionary Algorithm with an Ensemble Strategy (HDLE-EASE) for improving intrusion detection in IoT systems. Our method combines the spatial feature extraction capability of CNN (Convolutional Neural Networks) and temporal feature extraction of LSTM (Long Short-Term Memory) networks with the optimization feature of GA to optimize model parameters. We further incorporate a composite of boosting-bagging hybrid to enhance the stability and reliability of the intrusion detection mechanism. As privacy and scalability are critical issues in IoT networks, we propose a federated learning approach, allowing for model training on IoT networks while preserving data privacy. Furthermore, the presented approach includes a reinforcement-learning module for the capability of adapting to newly emerge and changing security threats. Initial tests show that the detection accuracy and model optimization capabilities of HDLE-EASE significantly outperform other methods, while its adaptability makes the tool a promising one for developing a holistic solution to protect IoT systems against a wide range of attacks.

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Basil Xavier mail -

Jaspher Willsie Kathrine mail -

Priyadharsini mail -

Gladwin Rufus mail -

R. Venkatesan mail

link https://doi.org/10.54216/FPA.190214

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Over-Under Sampling Approach with Adaptive Synthetic and Tomek Links Methods to Handle Data Imbalance in Sentence Classification on Halal Assurance Certificate Documents

Data imbalance is a common problem in machine learning, specifically in classification, in which examples in a dominant class outnumber examples in a minority class many times over. Besides, such a problem keeps a model unable to discover meaningful patterns for a minority class —hence, such a problem reduces model performance specifically in terms of Recall and F1-Score.  In current work, activity is performed in overcoming data imbalance problem in sentence classification model of documents of assurance certificate for halal with a combination of over-sampling and under-sampling techniques, namely Adaptive Synthetic (ADASYN) and Tomek Links. Text Classification technique is adopted in classifying sentences regarding assurance of halal in documents of assurance certificate for halal Text Classification; since incorrect classification of such sentences is not preferable, therefore, it is important to make sure no information about halal product is missed out. Over-sampling techniques considered include the SMOTE, Borderline SMOTE, ADASYN, and SMOTENC, and under-sampling techniques include the Random Under-Sampler, Near Miss, and Tomek Links. As comparative result, best performance gain in terms of Accuracy (0.759), F1-Score (0.748), Recall (0.759), and Precision (0.768) is generated with ADASYN. In our use case, ADASYN + Tomek Links is effective; recall is important in case of classification of documents for assurance certificate for halal and therefore, we cannot miss any relevant sentences. The proposed approach remarkably enhances the accuracy level for halal-related sentence identification and can be adopted in the halal product checking systems in industries with a halal feature.

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Dadang Heksaputra mail -

Rahmat Gernowo mail -

R. Rizal Isnanto mail

link https://doi.org/10.54216/FPA.190215

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Using federated learning for detecting autism in children

Identifying Autism early in children is vital for ensuring more precise developmental support and effective therapeutic interventions. Traditional diagnostic approaches are frequently delayed, and data privacy concerns limit the availability of broad, multi-institutional datasets required for effective machine learning models. To address these limitations, this study proposes a CNN-LSTM-based autism detection model for children using Federated Learning (FL). In the model, temporal and spatial information is extracted from the facial CNNs are highly adept at using convolutional filters to extract spatial features from images. LSTM networks are a specific type of Recurrent Neural Network (RNN) that is ideal for processing time-series or sequences because it can identify long-term relationships in sequential data. This architecture uses CNN layers to extract spatial information from important indications that are important for detecting ASD, like eye patterns, gestures, and facial expressions. After that, these features are sent to LSTM layers, which examine the time-dependent and sequential behavioral patterns associated with autism. Federated Learning allows the locally to train the model on its own dataset locally, sharing only model updates with a central server, thereby preserving data privacy while promoting diverse data contributions. According to experimental results using the proposed techniques, the federated CNN-LSTM model performs 4.3% better than the conventional centralized models because it has less overfitting and is more resilient to a range of data distributions. The model’s performance metrics further highlight its reliability, accuracy, precision, recall, and F1-Score values reaching 98.90%, 97.80%, 98.05%, and 98%, respectively, showing its potential for reliable ASD detection in children across diverse populations.  

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Maddala Kranthi mail -

Saswati Debnath mail -

Priyadharsini mail -

R. Venkatesan mail

link https://doi.org/10.54216/FPA.190216

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Automatic and Robust Technique for Segmentation and Classification of Acute Lymphoblastic Leukemia using Adaptive Multi-Dilated Residual Attention Network and Heuristic Strategy

Leukemia is a very dangerous kind of malignancy troubling the blood or bone marrow in all age categories, both in adults and children. The deadly and threatening kind of leukemia is named Acute Lymphoblastic Leukemia (ALL). The accurate and automated ALL diagnosis of blood cancer is complex work. Medical experts and hematologists in the bone marrow and blood samples detect it by employing a high-quality microscope. The manual classification is observed as tiresome and is restricted by varying expert considerations and other attributes. Presently, the Convolutional Neural Networks (CNNs) have become an acceptable mechanism for analyzing the medical image. However, for attaining outstanding performance, conventional CNNs normally demand large data sources for better training.   Thus, to alleviate the existing complexities, we implemented an effective ALL detection system using deep learning. At first, the necessitated images are aggregated from global resources of data. Further, the garnered images are inputted into the Optimized Trans-Res-Unet+ (OTRUnet+)-based segmentation model. Here, the Fitness-aided Position Updating in the Social engineering Algorithm (FPUSA) for improving the segmentation process’s efficacy optimally tunes the OTRUnet+ technique parameters.  In addition, the segmented images are taken to perform the classification process using the Adaptive Multi-Dilated Residual Attention Network (AMDRAN); here several parameters are optimally tuned by the same FPUSA to enrich the classification process. Finally, the suggested AMDRAN technique offered the ALL classified output. The effectiveness of the designed ALL detection system is explored with several existing systems to display its enhanced performance over other models

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Abirami M. mail -

Victo Sudha George G. mail -

Dahlia Sam S. mail

link https://doi.org/10.54216/FPA.190217

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Detection of Leaf Disease in Plantation Process for Fruits, Vegetables, Grains and Cereals using Application

One of the most important sectors for providing for daily human requirements is agriculture. At the same time, digitization has a big impact on a number of businesses, making it simpler to carry out a number of challenging tasks. In order to help the farmer and the consumer, technology and digitization must be adopted. Utilizing technology and routine monitoring, diseases can be identified and eliminated, increasing agricultural output. This paper suggests a system for recognizing and categorizing plant illnesses, initially focused on five separate classes: two fruit classes, one vegetable class, one edible pulse class, and one-grain class. The Plant Village and UCI ML Repository Dataset, which is well known as a freely accessible, accepted standard, and reliable data source, was used for this purpose. Based on them, a CNN model is prepared for analyzing them with an accuracy upto 95.42%. Image segmentation will also play a role in calculating precise amount of infection followingly, a good interface is must to utilize it in a proper way for a user which can be provided in the form of app, a feature that every user requires on daily basis.

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Madhuri Kanojiya mail -

Lokesh Chouhan mail -

Vipin Tiwari mail -

Dheresh Soni mail -

Devika A. Verma mail -

Yashwant Dongre mail

link https://doi.org/10.54216/FPA.190218

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems

We present a new metaheuristic optimization technique, the Dynamic Leader Sibha Algorithm (DLSA), based on the structured dynamics of the ‘Sibha’ (an Islamic tool). Using a hierarchical leader-follower framework, DLSA dynamically balances exploration and exploitation to resolve the difficulties of high dimensional and multimodal optimization. DLSA is applied to three well-known engineering problems, namely the Speed Reducer, Welded Beam, and Pressure Vesseldo, to tackle the objectives of minimizing the weight of these structures and achieving the desired results with regularity. Key results indicate that DLSA is faster in convergence, gives better quality solutions and is more robust among diverse problem domains. DLSA is an effective and reliable optimization tool that can readily be applied to solve real-world and complex engineering problems.

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El-Sayed M. El-kenawy mail -

Amel Ali Alhussan mail -

Doaa Sami Khafaga mail -

Amal H. Alharbi mail -

Sarah A. Alzakari mail -

Abdelaziz A. Abdelhamid mail -

Abdelhameed Ibrahim mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JCIM.160110

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

A Swarm Inspired Chaotic Map Evoked Attribute Encryption Framework Using Multi-Model Inputs in Cloud Environment

As an increasing number of people and corporations move their data to the cloud side, how to ensure efficient and secure access to data stored on the cloud side has become a key focus of current research. Attribute-Based Encryption (ABE) is largely recognized as the best access control method for safeguarding the cloud storage environment, and numerous solutions based on ABE have been developed successively. Attribute-based encryption (ABE), which provides fine-grained access control and ensures data confidentiality, is widely used in data sharing. Hence, the strong and lightweight encryption schemes need more limelight of implementation in ABE to overcome the tampering and leakage problem that may cause the severe consequences to the users. To solve this problem, this paper proposes the Swarm Inspired Chaotic Encryption principles for designing the CP-ABE Systems for effective data sharing process. This scheme utilizes the chaotic properties along with the swarm properties for every individual transmission that leads to the strong defence characteristics. The intensive experimentation is carried out using Multi-modal Inputs such as the biometric images and eye iris images. The extensive experimentation is carried out using the various standard tests such as NIST (National Institute of Standard and technology), communication cost (CC) and metrics such as NPCR, UACI, entropies has been evaluated and analysed. Furthermore, excellence of the proposed model is determined by comparing with the other existing schemes. The evaluation demonstrates the CC of proposed scheme is only 30% than other algorithms and passed all the 12 standard tests. The experimental results illustrate the proposed scheme has more advantage in exhibiting the more randomness and light weight characteristics for health care which can more defensive against the attacks

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A. Jeneba Mary mail -

K. Kuppusamy mail -

A. Senthilrajan mail

link https://doi.org/10.54216/JCIM.160111

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel Computer Vision-Based Approach to Mitigating Fall Risks in the Elderly through Spatial-Channel Decoupled Downsampling in YOLOv10

Elderly health has always been a matter of concern for the medical doctors and researchers to come up with advanced recovery techniques. With the rise in population of elderly people and mostly residing alone at home in solitude has motivated many researchers to work on remedial measures for the biggest safety risk faced by them which is elderly fall prevention and mitigating thereby causes of injuries. In this paper, an intelligent deep learning and computer vision based elderly fall recognition system is designed which utilizes advanced spatial-channel decoupled downsampling in You Only Look Once version 10 (YOLOv10), pytorch, darknet and cascaded CNN technologies for the fall detection. The results after testing manifest that the accuracy of the proposed system to recognize and detect the elderly fall is quite assuring, the values of accuracy and mean Average Precision (mAP50) coming out to be 92.46% and 94.1% respectively after the model validation. Moreover, the system displays a real time performance as it can process approximately 45 frames of images per second that realizes a real-time identification of elderly fall patterns. As compared to previous models, the proposed model is much more efficient and has shown promising results.

groups

Ajay Singh mail -

Alok Katiyar mail

link https://doi.org/10.54216/FPA.190219

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel CNN Model for Fruit Leaf Disease Detection: A Lightweight Solution for Grapes, Figs, and Oranges

Plant diseases are considered a real threat to food security due to the losses incurred by individuals and countries. Early detection is one of the real solutions that can help reduce the size of these losses, but early detection is still bleeding. This study presents the development of a Convolutional Neural Network (CNN) model for classification with a new architecture and optimal performance suitable for real-time applications for the detection of fruit diseases (figs, oranges, grapes). The developed CNN model balanced accuracy and FLOPs using Squeeze-Excitation (SE) and adaptive-average pool layers. After implementing new data developed from Iraqi farms, the CNN model achieved optimal performance compared to the most famous models such as VGG16, ResNet, EfficientNet, and AlexNet.

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Dalya Anwar mail

link https://doi.org/10.54216/FPA.190220

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

HI2NN: Heuristic Intelligence towards Enhancing Rainfall Prediction with Improved Artificial Neural Networks

Predicting rainfall proves critical for businesses to organize their water resources, make agricultural choices, and prevent disasters. Therefore, proposed model presents a novel approach, namely Heuristic Intelligence towards Enhancing Rainfall Prediction with Artificial Neural Networks (HI2NN) to enhance rainfall prediction by designing heuristic Intelligence combined with Improved Artificial Neural Networks (IANNs). The proposed HI2NN framework leverages heuristic optimization techniques to fine-tune ANN parameters to improve prediction accuracy. Prediction accuracy is computed through our designed custom accuracy metric. The methodology uses historical weather information to extract complex non-linear patterns, which neural models generate from the designed big dataset. The accuracy level of rainfall predictions using our methodology achieves 92%, which demonstrates superior performance than traditional approaches that include random forest and decision tree and XGBoost models. The new forecasting systems develop higher reliability through collaborative efforts between heuristic algorithms and neural networks as described in this research work targeting challenging meteorological forecasts.

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Sachin Subhashrao Patil mail -

Sonali Ridhorkar mail

link https://doi.org/10.54216/FPA.190221

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

Details open_in_new