Iraqi Journal for Computers and Informatics

The ship's event-triggered tracking algorithm, which combines game theory and remote sensing. The technique addresses issues with conventional continuous and discrete control techniques, which consume excessive amounts of energy and prematurely wear out actuators due to frequent updates. In order to locate ships, this paper first employs the YOLOv8 deep neural network and radar-based remote sensing. After determining the distances and connections between ships, the target trajectory is altered using a game-theoretic framework. In order to reduce needless computations and communications, the controller only activates when significant events take place. This is not the same as conventional time-triggered techniques. Good results were obtained from tests on two benchmark datasets: the Synthetic Aperture Radar Ship Dataset (SAR-Ship-Dataset) and the SAR Ship Detection Dataset (SSDD). On SSDD, the proposed algorithm achieved 92% accuracy and 93% sensitivity, while on SAR-Ship-Dataset, it achieved 93% accuracy and 91% sensitivity. These results lay the foundation for the development of more efficient marine tracking systems by demonstrating that event-triggered tracking can maintain high tracking accuracy while using less energy.

The rapid growth of mobile data traffic, supported via applications like extended reality (XR), communication-based holographic, and huge machine-type connectivity, led 5G network communications to the performance restrictions. Terahertz communication in 5G mobile communications has appeared as a promising solution for overcoming the limited spectrum lack and performing ultra-high data rates, in addition to capacity. This review paper supplies a thorough overview of modern evolution in Terahertz (THz) wireless communication systems, focusing on their potential to uphold data rates exceeding hundreds of gigabits per second. This review paper argues that the new key trends, like the availability of spectrum, the channels-based ultra-broadband, and consolidated arrays of utilized antennas, which are used to make THz communication necessary progress for the next generation of mobile communication systems. Crucial challenges consisting of coverage restrictions, propagation loss, hardware complexity, and scarcity of standardization are examined. Prospect strategies for mitigating such challenges have also been surveyed via the combination of intelligent considering surfaces, transceivers-based photonic-assisted, and characterized beamforming. Total, this paper concludes that THz mobile communication introduces a transformative move in realizing the ultra-high-capacity, very low latency, and high-efficiency aims of 5G mobile communications and beyond generations. The presented analysis in this review paper illuminates the practical trade-offs that presently restrict empirical deployment of THz and summarizes the extremely promising research study trends to get the better of them. This will provide a base for future expansion aimed at combining THz communication systems with 5G and beyond mobile communication network systems.

Due to their flexibility, NoSQL databases have gained popularity as a preferred data storage destination of modern web applications and can accommodate easy growth. But they are not without risk: Technical Articles can be susceptible to injection attacks that could result in a data breach or security concern. This study explores the ability to detect such NoSQL injection attacks which involves both machine learning models and data balancing techniques. We projected several classifiers - Support Vector Machines, Decision Trees, AdaBoost, Random Forest and Logistic Regression – in data balanced with approaches such as SMOTE, Random Oversamplingand NearMiss or downsampled with either Random UnderSampling, SMOTEENN or SMOTETomek. The findings indicate that ensemble classifiers (i.e., AdaBoost and Random Forest) consistently outperform the others, achieving weighted F1-scores of 0.920--1.0 with various resampling techniques. Hybrid balancing approaches triggered a significant boost in detection malicious queries, which becomes close to the ideal performance (i.e., precision and recall) for both infrequent and frequent types of GPCs. Less complex models such as Logistic Regression and Naive Bayes also performed extremely well with hybrid resampling, attaining weighted F1-scores greater than 0.99. Discussing these results show that the hybrid resampling strategy along with ensemble learning are able to make the most robust and accurate NoSQL injection detection system to secure threats web applications without ignoring real NoSQL threats.

Smart cities employ advanced technology like artificial intelligence (AI) and the Internet of Things (IoT) to resolve urban challenges and improve the quality of life. However, the literature lacks one comprehensive synthesis of AI and IoT's joint contribution to building smart city literature and beyond. The study wanted to take a systematic review to find the role and combined roles of AI and IoT.Researchers conducted research by following PRIMA guidelines, mostly on applications of AI and IoT in building smarter cities by taking many research papers as their sources. A total of 30 relevant peer-reviewed studies were identified from an initial pool of about 16,600 records generated primarily from four biggest databases: Science Direct, Scopus, Web of Science, IEEE Explore, and ProQuest, including related articles of the identified papers AI and IoT together play a vital role in six significant domains. AI, especially the Internet of Things (IoT), helps provide a broader data set in real-time. Whenever the AI techniques are set, it collects data and finds results in it to optimize and automate the city operations to improve the city's sustainability and public services. The most common domains of the application of Riot are: Data-driven urban service efficiency Energy efficiency and environmental sustainability Efficacy in smart transportation and traffic management to provide smart surveillance and avoid future cybersecurity threats. Urban planning also requires decision support.

Recently, chatbot systems have grown from the very basic rule-based systems to more advanced ones, such as natural and context-aware systems that incorporate sophisticated neural network techniques. This paper provides a comprehensive review of evolution and taxonomy and analyzes the application of chatbots in various fields, such as healthcare, banking, education, mental health, customer service, and image recognition, to reveal contemporary strengths and applications. In addition to observation of the most important assessment measurements and highlights of the latest studies, promising performance has been reported in the literature, with accuracy/success rates over 90% for some studies, with user satisfaction levels reaching 95% in certain health-related scenarios; however, these results remain specific to their settings rather than generalizable for all chat applications. The paper concludes that contemporary AI-based chatbots are strong in response generation and context appreciation but are still weak when dealing with ambiguous questions and multi-turn dialogues. Some of the key challenges, such as making the system scalable and personal and achieving human-like conversational quality in different contexts, were identified. This in-depth review summarizes recent developments as well as challenges in the area of chatbot technology and emphasizes the necessity of innovation in AI and NLP to overcome these challenges and improve chatbot performance and user experience.

Cloud computing has swiftly become a prominent trend in the technology industry, owing to its various benefits such as scalability, high availability, and cost-effectiveness. Consequently, an increasing number of organizations are shifting their operations to cloud-based environments. It is anticipated that cloud technologies will soon form a foundational component of nearly every enterprise.

This transformation has significantly influenced sectors like Software as a Service (SaaS), where traditional Database Management Systems (DBMSs) have transitioned into Cloud-based DBMSs (CDBMSs). Alongside this evolution, there has been a movement from conventional single-tenant database structures to multi-tenant architectures, which allow for more efficient utilization of infrastructure and resources.

However, despite these benefits, many organizations are still hesitant to adopt multi-tenant database systems due to persistent concerns over data security. The practice of storing data from multiple tenants on the same server—or even within shared tables—introduces considerable risks related to unauthorized access. In response to these concerns, the current research centers on database security in cloud settings, with a specific focus on the unique challenges associated with multi-tenant systems.

With the high explosion in data traffic in today's networks, the importance of secure and high-capacity optical wireless communication has become more pressing than ever before. Free Space Optics (FSO) in combination with Optical Code Division Multiple Access (OCDMA) has great potential to meet these demands. However, most of the current research has considered system performance and physical layer security independently and left a major gap in our knowledge on how they interact, particularly under practical conditions in the real atmosphere. This study takes up this challenge by investigating the combined operation of performance and security in FSO-OCDMA systems that are necessary for protection against eavesdropping, jamming and unauthorized access. We investigate a few FSO-OCDMA techniques such as PD-NOMA, SAC-OCDMA with various coding schemes, hybrid fiber-wireless (FiWi) systems and OAM-based multiplexing schemes in various turbulence conditions. Through simulation testing, we cover some of the most important metrics including Bit Error Rate (BER), signal robustness and security threats. Our results demonstrate that well-optimized modulation and coding schemes, and specifically that SAC-OCDMA combined with sophisticated zero cross-correlation coding schemes, dramatically enhance the system security while at the same time enhancing the communication reliability. These results offer practical advice for construction of the next generation of secure, high-performance optical wireless networks.

The Internet of Things (IoT) is an emerging technological revolution, where devices communicate with each other over the internet to receive communications and information. These devices generate massive amounts of information. As industries increasingly rely on IoT devices, the need for technologies to enhance data security and privacy has emerged. This data faces significant security challenges, such as cyberattacks or data tampering. Therefore, it is imperative to develop effective protection for this data. This study aims to review the role of artificial intelligence (AI) and blockchain technology in enhancing IoT security by integrating these two technologies. The combination of AI and IoT represents a tremendous revolution in the rapidly evolving field, given its ability to simplify tasks easily and efficiently. AI analyzes and classifies data, detects threats and malicious attacks, while blockchain technology provides an additional layer of protection for the IoT through decentralized storage that prevents data tampering and ensures its integrity and confidentiality. In this study, we present a structured and systematic review of research published between 2021 and 2025, focusing on the role of AI and blockchain in securing IoT data. The results demonstrate that integrating AI with blockchain technology improves IoT security by detecting attacks early, reducing vulnerabilities, and preventing unauthorized access or data tampering. However, the evolving nature of attacks and challenges calls for further research to find or develop solutions capable of addressing future challenges to ensure security and reliability in data exchange between devices.

Our adaptive multi-layer security lets UAVs communicate live video. This technique improves key scheduling over time by using MDP, chaotic maps, and blockchain technology. The bulk of essential management frameworks use legislation or centralized trust systems. In UAV networks, safe and fast video transmission is in demand, thus we must match encryption strength with operation speed. Reinforcement learning can optimize key generation and distribution. Monitor network trust scores, chaotic entropy, and blockchain update timestamps in MDP state space, and automatically remembered action refresh times in state space. The system may evolve with the network since the reward function handles unpredictability, timeliness, and trustworthiness. The system's adaptability allows this. Non-linear chaotic map framework parameter modifications can be made by using blockchain-based trust signals and entropy measurements. Removal of repeating patterns does not weaken the cryptography technique. A transformer-encoded deep Q-network produces optimal policies in the presented manner. An Old Fault Through Tolerant blockchain consensus, important blockchain modifications may be checked. The experiment shows that GPU-powered chaotic generators can use Hyperledger Fabric to reach refresh rates below one second. In practice, UAV network dynamics prove this. This work evaluates our defenses against replay and key compromise and advances high-throughput video streaming systems.

The sophistication of cyberattacks is on the rise and it demands sophisticated and dynamic intrusion detection systems that will help to detect both the existing and the new threats. This paper is a performance appraisal of intrusion detection models based on artificial intelligence with two current benchmark datasets, namely, UNSW-NB15 and CSE-CIC-IDS2018. It compares classical machine learning techniques with deep learning models, such as Convolutional Neural Networks and Gated Recurrent Units with attention mechanisms and suggests a new hybrid model, consisting of Stacked Autoencoders in combination with Gradient Boosting to learn features and classify better. Strict experimental regimen using leakage-free preprocessing, cross-validation and multi-metric evaluation was used to ensure fairness and reproducibility. The experimental findings prove almost flawless performance in terms of detection in both datasets, with the hybrid Autoencoder-Gradient Boosting model and the attention-based recurrent network being stronger in the face of the imbalance of classes and sophisticated traffic patterns. Besides being highly accurate, the proposed approach is more stable, generalized, and interpretable, which is why it is appropriate to deploy it in practice. The results affirm the fact that hybrid artificial intelligence systems are capable of improving cybersecurity defenses. Further research is needed on scalable, privacy-preserving, and interpretable intrusion detection on new network conditions emerging e.g. Internet of Things and edge computing systems.

The selection of hyperparameters is a key factor in the predictive performance and the overall generalization of machine learning models. In real-life scenarios, poor hyperparameter selection tends to result in suboptimal performance, despite the use of sophisticated learning algorithms. Intelligent optimization techniques are commonly adopted when conventional tuning methods, such as grid search and random search, become computationally infeasible. In this paper, an experimental study involving the use of metaheuristic-based hyperparameter optimization for machine learning classification is outlined. The study used Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO) to optimize the hyperparameters of popular classifiers, such as Support Vector Machine, Random Forest, and k-Nearest Neighbors. A hybrid PSO–GWO framework is also proposed to accommodate complementary exploration and exploitation behaviors and improve convergence stability and optimization effectiveness. Experiments on multiple benchmark datasets of the UCI Machine Learning Repository revealed that hyperparameter optimization using metaheuristics consistently outperforms default configurations in classification tasks. In addition, the developed hybrid method is more accurate and exhibits more stable convergence behavior, compared to individual optimizers. These results show that hybrid metaheuristic approaches are relevant and scalable to improve machine learning classification in applied settings.

In an article, the title should cover the whole content with a few important words. Several automated title-generating tools are available in the Internet. This paper is aiming to design and implementation a new model to generate title using a list of keywords. The model using a new dataset that is generated from NIPS dataset with Configure training arguments. The proposed system preprocesses keyword data, trains on a curated dataset, and produces coherent, contextually relevant titles through controlled text generation. The proposed model shows a strong generative capability by accurately producing research titles from list of important keywords. Its efficient fine-tuning strategy enables high performance with minimal training resources. The experimental results show that the proposed fine-tuned T5 title generation model can produce titles that are very close to the original scientific titles. For the paper “Learning to Play the Game of Chess”, the generated title matched the original exactly. As a result, all evaluation metrics reached 1.0, including cosine similarity, ROUGE-L, BERT_F1, and SciBERT_F1, indicating complete lexical and semantic agreement between the generated and reference titles.

Wireless Sensor Networks WSNs are widely adopted and cost-effective way of implementing intelligent solutions in many low-resource settings. Both known Clustering/Routing Protocols such as Low-Energy Adaptive Clustering Hierarchy LEACH and Threshold-Sensitive Energy-Efficient Network Protocol TEEN as well as other related methods such as WOA-LEACH, are severely limited by their inability to support a long term network cycle for use in real world applications. This is because they all experience rapid energy exhaustion because of their inability to adapt to varying levels of energy and traffic demand. In addition, failure to distribute energy usage evenly throughout the network. In order to resolve these issues, we will develop a new Hybrid Approach that combines the Whale Optimization Algorithm WOA for determining energy aware cluster heads, and Deep Reinforcement Learning DRL for providing an adaptive multi-hop routing protocol. The proposed hybrid DRL-WOA solution will make joint optimizations of cluster heads and routing nodes to determine optimized routes to minimize energy use while maximizing energy efficiency through the optimization of hop distances, thereby creating longer lasting and more reliable communication processes. Results from simulations run on a 100 node WSN environment demonstrate the hybrid DRL-WOA solution achieves better performance than LEACH, TEEN, WOA-LEACH and a DQN-based only routing solution, including 22% less total energy consumption, 60% extended First Node Death FND, and PDR improvements of 5-15% in comparison to each of the above mentioned base line protocols. All in all, the experimental results clearly demonstrate that the proposed Hybrid DRL-WOA approach leads to a considerable improvement of the energy efficiency, network lifetime and the reliability of data delivery of static WSNs.

Cardiovascular diseases (CVDs) are the leading cause of mortality in the world. This paper constructs models of heart disease classification using a single-hidden-layer Multilayer Perceptron (1HL-MLP) that is trained using a bi-level model that utilizes Bayesian
Hyperparameter Optimization (HPO) and three variations of evolutionary strategy-based Learner Performance-Based Behavior (LPB): LPB-MLP, aLPB-MLP and mLPB-MLP. Each of the four heart disease datasets was processed in a common pipeline based on schema
alignment, median imputation, one-hot encoding, per-fold Z-score normalization, and Outof-Fold (OOF) threshold tuning and their performance was checked by stratified K-fold and external testing on an independent dataset. Results have shown that the Modified LPB model (mLPB-MLP) performed better, and it has the highest discrimination and calibration (AUC = 0.9782, AUPRC = 0.9732) and the overall accuracy (93.66%), F1-score (93.53%), recall (94%), specificity (93%), and the lowest BCE loss (0.193). These findings indicate consistent optimization processes, reasonable probability tuning, and sensitivity-specificity compromise.
In general, the Bayesian HPO in combination with LPB-family evolutionary training using data will lead to a clinically robust, well-calibrated, and reproducible heart disease risk prediction model.

We introduce a smart multi-objective optimization framework towards the performance enhancement of the 4G LTE network using an integrated Artificial Intelligence (AI) and Whale Optimization Algorithm (WOA) setup. This approach aims to integrate tested regression-based KPI prediction along with decision-variable-based meta-heuristic optimization in a reproducible experimental environment within MATLAB R2023b. A weighted multi-objective formulation for optimizing the performance of 4 key indicators, i.e., throughput, latency, packet loss ratio, and energy efficiency, is jointly executed. By comparison against existing baseline LTE configuration, we observe a 25.3% throughput improvement, 28.9% reduction in latency, 38.7% reduction in packet loss ratio, and 23.4% improvement in energy efficiency. We validate robustness with weight sensitivity analysis (±10% and ±20%), population scalability testing (N = 20, 30, 50), decay-strategy comparison (linear vs. exponential), and Pareto-front approximation (2,960 non-dominated solutions from 3,000 samples). The above results corroborate that, after approximately 70 iterations, convergence is stable and performance is consistent over the span of 30 independent runs. Thus, the proposed AI–WOA construction offers an organized and replicable process to fine-tune LTE performance optimization, a basis for downstream implementations towards a more distributed and next-generation wireless environment.

Malware detection is one of the most important cybersecurity issues because the traditional signature-based methods cannot resist polymorphic threats and obfuscated ones. This paper explores the dynamic API call sequences as behavioral characteristics and contrasts the two representation methods, integer-based feature encoding into the traditional machine learning models and graph-based models using Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Unlike prior studies, this paper conducts a systematic head-to-head comparison of these approachs and introduces a newly collected balanced dataset of 2,000 malware and 2,000 benign sampels, for this paper Two datasets were employed, one of which was a large public dataset with 42,797 malware and 1,079 benign samples, and the other was a novel developed dataset consisting of 2,000 malware and 2,000 benign samples that were collected according to this research by means of sandboxed execution. To facilitate 10-fold cross-validation, API calls were pre-encoded into fixed length sequences of integers and call graphs directed to allow fair evaluation. The findings indicate that ensemble and tree-based models achieved competitive results (≈92% on the public dataset and ≈90% on the novel dataset), but the graph-based ones were more accurate with GCN coming to 98.76% and GAT at 98.33%. Because graph neural networks can capture relational dependence and contextual patterns in API call behavior, they generate a richer representation and stronger categorization than integer encodings also the best feature of graph-based models is that they learn not only features but also the connectivity of API calls, which gives much richer and more accurate representation than integer-only encodings. Unlike prior studies, this work conducts a systematic head-to-head comparison of these approaches and introduces a newly collected balanced dataset of 2,000 malware and 2,000 benign samples.

Biometric recognition systems have evolved from uni-modal systems to multi-modal frameworks, improving both accuracy and robustness. Deep learning has been at the forefront of this evolution, and Convolutional Neural Networks CNNs have been the foundation for biometric fusion systems due to their ability to tap the spatial features of the input data. However, CNN architectures have inherent challenges in handling long-distance dependencies, as well as inter-modal dependencies, within the biometric modalities. In this narrative review, the architectural development of deep learning models used in multimodal biometric fusion systems will be critically discussed, from the use of CNN-based models and Recurrent Neural Networks RNNs, including Long Short-Term Memory LSTM architectures, towards the development of hybrid and transformer models. This article will also discuss the different levels of biometric fusion, including fusion at the sensor, feature, score, and decision levels, and will synthesize the challenges associated with multimodal biometric fusion systems. Through the analysis of research gaps in existing studies, as well as the motivations behind the transition to new architectures, this literature review points to the combination of CNNs and Transformers as an area of great promise for the development of scalable, transparent, and robust multimodal biometric fusion systems.

Recent multimodal deep learning approaches are changing how healthcare systems process and integrate widely different types of patient information. Based on this, in this review we investigate the Convolutional Neural Network CNN-based architectures in Healthcare Recommender Systems HRS, which incorporate a variety of data from different sources, such as medical imaging, Electronic Health Records EHRs, wearable sensor streams, clinical narratives. At the core of this ecosystem, CNNs learn hierarchical representations from high-dimensional visual and other unstructured medical data and are increasingly the feature-extraction backbone of modern healthcare recommendation pipelines. We first discuss CNN-based architectures for HRS and then examine their combination with traditional recommendation schemes such as collaborative filtering, hybrid recommendation techniques and other multimodal fusion methods. The review furthermore covers new research developments to leverage CNN-based HRS, including vision–language models, reinforcement learning, causal inference, fairness-aware optimization, federated learning and edge/IoMT deployment. This review reports methodological advances and recent challenges. These challenges include generalization, interpretation , privacy preserving, and clinical integration. Taken together, these analyses summarizes the progress of recent research and also provide open challenges. The goal is to guide the design of transparent and scalable systems. These systems should be also ethically sound. They focus on CNN-based healthcare recommender systems for next-generation personalized medicine.

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings. Experiments were conducted using the Kaggle Brain Tumor MRI dataset and Mendeley Data distributed across five simulated institutions. Within the evaluated experimental setup, the proposed framework achieved approximately 92% accuracy under IID conditions and 91.5% under non-IID settings, with an F1-score of approximately 0.90. Client-level evaluation demonstrated the model’s ability to handle data heterogeneity, while convergence analysis indicated stable training behavior across communication rounds. In addition, Grad-CAM visualization was employed to provide visual interpretability, showing that the model focuses on clinically relevant anatomical regions during prediction. Overall, the results demonstrate that combining federated learning with heterogeneous multi-source MRI data can preserve privacy, maintain robustness and interpretability, and achieve competitive classification performance, highlighting the potential of federated deep learning as a practical and scalable solution for privacy-aware medical image analysis in realistic clinical environments.

Industrial surface defect classification is an important part of automated quality inspection systems. For these systems to work, they need to be able to detect surface defects accurately and efficiently to improve product reliability and reduce manufacturing costs. Traditional manual inspection methods are often time-consuming, subjective, and not suitable for fast-paced industrial environments. This study proposes a lightweight convolutional neural network (CNN)-based system for classifying industrial surface defects. The model was made to work well for classification and still be fast enough for real-world use. Keras Tuner was used in the proposed method to find the best hyperparameters. The proposed model was evaluated on the Northeastern University (NEU) Surface Defect Database using 5-fold stratified cross-validation. The results obtained from the experiments are promising since the system yields stable performance on all folds with accuracy scores of 99.72%, 99.17%, 100.00%, 99.17%, and 98.61% respectively. The mean accuracy score is calculated as 99.33%. Also, Grad-CAM visualization revealed that the network focuses on defective regions when processing an input image which supports the reliability and interpretability of the classification process.

Over the past decade, the rapid digital transformation of infrastructures to digital forms, such as cloud computing, Internet of Things (IoT), and large-scale interconnected network systems, has made the threat of cybercrime much more pronounced. Of those, Zero-Day attacks are regarded as the most serious since they are previously unseen and so the traditional signature-based intrusion detection systems are useless. This paper presents an in-depth overview of machine learning (ML) and deep learning (DL) methods of detecting Zero-Day attacks. The methodology is based on reviewing, analyzing, and synthesizing recent literature, which is applied to ML, DL, and hybrid methods, threat intelligence integration, and real-time intrusion detection systems. The findings suggest that both ML and DL methods have high detection accuracy but have a number of weaknesses including high computational complexity, data imbalance, scarce availability of labeled data, and susceptibility to adversarial attacks. Moreover, this review shows some of the main gaps in research, especially in coping with the unknown attack patterns, the development of lightweight and real-time detection models, and the enhancement of the generalization abilities. Finally, the research paper shows that it is crucial to establish adaptive, scalable, and hybrid intelligent systems to improve the detection of Zero-Day attacks. To enhance actual cybersecurity applications in the future, future studies should focus on efficient learning mechanisms, strong adversarial defenses, and data-efficient models.

Agriculture worldwide, particularly in arid and semi-arid regions, is severely affected by water scarcity, which has become a major global challenge, this paper describes development of the proposed artificial intelligence-based system, Smart Drop that optimizes irrigation water to address this issue. The system was tested and deployed through a 4.25-kilometer (internal diameter: 800 millimeters) pipe network irrigating 147 hectares of farmland at the design flow rate of 0.25 cubic meters per second as described in Case Study. The combination of machine learning models with genetic algorithms and particle swarm optimization was applied for prediction of irrigation needs and optimized irrigation water supply. Three models were established through the use of machine learning: random forest (R²=0.9965; RMSE= 0.0491 millimeters per day), Gradient Boosting (R² = 0.9976, and RMSE = 0.0487 millimeter/day) as well as neural networks models (R2 = 0.9943, and MSE = 0.0514). Compared with the traditional irrigation methods, this system reduced water use by 23.42% in irrigation (797,731) cubic meters over a period of 1,000 days), maintaining optimal crop conditions, the savings to total cost was estimated at US$121,036.62 (23.4%), indicating that the combination of reduced water use along with reduced energy consumption produced an economically viable alternative. The hydraulic performance results show acceptable pressure distribution through pipeline and overall losses within system are 1.0 meter drops across each section 9.78 kilopascals. This research is a data-driven precision irrigation solution for sustainable application in different agricultural environments.

The title should concisely encapsulate the overall content; however, producing a strong academic title that reflects the core contribution remains challenging. This paper proposes and evaluates a pipeline-based model for academic paper title generation: abstracts collected from the NIPS dataset are preprocessed, condensed via extractive summarization, and then Standard YAKE is applied to extract weighted keywords. Titles are generated from a fixed Top-6 keyword budget while preserving the YAKE ranking order; in this work, the proposed model refers to the end-to-end pipeline configuration rather than a newly trained neural architecture. We compare four summarization algorithms (Luhn, LSA, Edmundson, and KL) based on their YAKE-weighted keywords, and adopt Luhn as default because it produces more topic-relevant YAKE-weighted keywords than the other summarizers. We illustrate the pipeline by using three qualitative examples and compare three generator-Ateeqq/keywords-title-generator, KeyToText (k2t), and GPT-2 (gpt2)-under identical Top-6 constraints. Experiments on 300 papers using both lexical and semantic similarity metrics (ROUGE-L, TF-IDF cosine similarity, BERTScore-F1, and SciBERTScore-F1) indicate that the adopted Luhn→YAKE→Top-6→LLM pipeline produces the most semantically aligned titles under identical input constraints.

The traditional networking topologies of Internet of Things (IoT) systems are based on explicit topology building, neighbor discovery, and routing state maintenance. Although they are effective in less dense and stable networks, these methods are inefficient in dense, dynamic, and unstructured IoT networks because they increase the control overhead, energy usage, and scalability. This study proposes a topology-free networking paradigm that does not require topology awareness, routing tables, node identifiers, or neighbor discovery. Rather, data propagation is realized via implicit coordination of nodes as decided by locally visible cues, such as signal strength, reception timing, and channel contention. An opportunistic forwarding based on a suppression strategy is presented, in which each node calculates a forwarding score based on these normalized parameters and applies opportunistic forwarding to the node. The proposed method was evaluated through simulations under varying node densities and traffic demands. The performance is evaluated by means of several metrics, such as packet reachability, end-to-end delay, transmission redundancy, scalability, control overhead, and energy consumption. The findings indicate that the proposed topology-free approach offers reliable data delivery, with a substantially reduced overhead and high efficiency in dense IoT contexts. These results suggest the promise of topology-free networking as a scalable and energy saving alternative to large-scale and resource-constrained IoT systems in the future.