Binary Approach Research Articles

Prediction model of factors causing traffic accidents on rural arterial roads: A binary logistic regression approach

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

Rethinking child aggression beyond traditional labels: A ‘shared victimhood’ approach to child-to-child violence in Nigeria

Child-to-child violence is a pervasive problem in schools and communities worldwide. Traditional models for understanding this violence often present a clear dichotomy between aggressive children and passive victims. However, this binary approach has proven largely inadequate in recognizing the power dynamics and potential victimization of both parties involved, resulting in interventions that have little to no success in rehabilitation. Alternatively, this paper proposes a "shared victimhood" approach, arguing that violent behavior may result from a cycle of negative experiences in which both the aggressor and the victim are positioned as products of their environment. In essence, the paper explores the limitations of binary labeling and the potential of the shared victimhood framework. It reviews relevant research on the ecological factors influencing child aggression, highlighting the impact of family background, early childhood experiences, and social learning. Drawing on ecological theory, the paper emphasizes the importance of early intervention and prevention strategies that address the root causes of aggression while fostering empathy and resilience in children. It also recommends that states in Nigeria establish LGA-specific child counseling centers and rehabilitation facilities attached to the health care system that cater exclusively to the needs of abused children. Finally, the paper suggests areas for future research to further develop and refine the shared victimhood perspective.

JND-based multi-module cooperative perceptual optimization for HEVC

Since the just noticeable distortion (JND) reflects the tolerance limit of human visual system (HVS) to coding distortion directly, JND-based perceptual video coding (PVC) plays an increasingly significant role in video compression with the developing explosion of video data. In this paper, we focus on the coupling effect among coding modules and provide a JND-based multi-module cooperative perceptual optimization (JMCPO) scheme for HEVC. The main contribution of the proposed JMCPO scheme includes the following three aspects. (1) Based on quantization distortion estimation, an adaptive perceptual quantization scheme is proposed using the binary search approach on the premise of that the quantization distortion is infinitely close to the estimated JND threshold. (2) The coupling effect among coding modules is first analyzed and a novel perceptual residual filtering scheme is presented based on statistic analysis of coupling strength. (3) The JMCPO scheme is finally developed through collaborative optimization of residual filtering, quantization and rate–distortion optimization. Experimental results show that the proposed JMCPO scheme saves more bitrate with better subjective and objective qualities.

Redefining Significance: Robustness and Percent Fragility Indices in Biomedical Research

The p-value has long been the standard for statistical significance in scientific research, but this binary approach often fails to consider the nuances of statistical power and the potential for large sample sizes to show statistical significance despite trivial treatment effects. Including a statistical fragility assessment can help overcome these limitations. One common fragility metric is the fragility index, which assesses statistical fragility by incrementally altering the outcome data in the intervention group until the statistical significance flips. The robustness index takes a different approach by maintaining the integrity of the underlying data distribution while examining changes in the p-value as the sample size changes. The percent fragility index is another useful alternative that is more precise than the fragility index and is more uniformly applied to both the intervention and control groups. Incorporating these fragility metrics into routine statistical procedures could address the reproducibility crisis and increase research efficacy. Using these fragility indices can be seen as a step toward a more mature phase of statistical reasoning, where significance is a multi-faceted and contextually informed judgment.

Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm

Skin lesion classification plays a crucial role in the early detection and diagnosis of various skin conditions. Recent advances in computer-aided diagnostic techniques have been instrumental in timely intervention, thereby improving patient outcomes, particularly in rural communities lacking specialized expertise. Despite the widespread adoption of convolutional neural networks (CNNs) in skin disease detection, their effectiveness has been hindered by the limited size and data imbalance of publicly accessible skin lesion datasets. In this context, a two-step hierarchical binary classification approach is proposed utilizing hybrid machine and deep learning (DL) techniques. Experiments conducted on the International Skin Imaging Collaboration (ISIC 2017) dataset demonstrate the effectiveness of the hierarchical approach in handling large class imbalances. Specifically, employing DenseNet121 (DNET) as a feature extractor and random forest (RF) as a classifier yielded the most promising results, achieving a balanced multiclass accuracy (BMA) of 91.07% compared to the pure deep-learning model (end-to-end DNET) with a BMA of 88.66%. The RF ensemble exhibited significantly greater efficiency than other machine-learning classifiers in aiding DL to address the challenge of learning with limited data. Furthermore, the implemented predictive hybrid hierarchical model demonstrated enhanced performance while significantly reducing computational time, indicating its potential efficiency in real-world applications for the classification of skin lesions.

Characterization and decontamination evaluation of carbon composites synthesized via Co(II)-Assisted pyrolysis of phenanthroline, phenylenediamine, and melamine

The application of carbon composite materials (CCMs) spans across various fields, and their preparation primarily involves the non-oxygen pyrolysis of non-volatile macromolecular precursors, which poses challenges in tailoring their composition and microstructure. Here, we propose a binary co-pyrolysis approach for the synthesis of CCMs, utilizing small organic molecules such as Phenanthroline (Phen), o-Phenylenediamine (oPD), and Melamine (Mel) as precursors, along with Co(II) salt as a catalyst. We conducted a thermogravimetric analysis of these three binary systems, aiming to synthesize CCMs at moderate temperatures (330 − 460 °C). Subsequently, we characterized the CCMs in terms of their bulk structure, composition, and surface properties. Finally, we assessed their decontamination performance through catalytic H2O2 degradation of methylene blue and adsorption of Pb(II)/phosphate. The results revealed that Co(II) can promote the preferential formation of thermally stable intermediate polymeric structures and inhibit the direct evaporation of the precursors. The prepared CCM-Phen-Co, CCM-oPD-Co, and CCM-Mel-Co exhibit a lamellar and (N, O, Co)–co-doped amorphous structure. Furthermore, both CCM-Phen-Co and CCM-oPD-Co demonstrate complete removal efficiency for methylene blue at a concentration of 10 mg/L, while the maximum adsorption capacity of CCM-oPD-Co for Pb(II) and CCM-Phen-Co for phosphate are 58.0 mg/g and 13.6 mg/g respectively. The CCMs demonstrate promising efficacy in environmental catalysis and adsorption for decontamination purposes.

The impact of environmental stimuli on the psychological and behavioral compliance of international construction employees.

This study explores the overlooked psychological and behavioral dynamics of employees in compliance management, applying the Stimulus-Organism-Response (SOR) framework to assess environmental stimuli's impact on employees in international construction projects. A scenario-based survey involving 270 international construction employees was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA), focusing on the relationship between environmental stimuli and compliance intentions. Findings categorize environmental influences on compliance into internal and external organizational dimensions, highlighting the significant impact of internal factors on compliance intentions. Key determinants identified for high compliance intention include individual traits and organizational climate, while project pressures, rules and regulations, and cultural differences show variable influence. This study enhances the understanding of the psychological factors driving non-compliant behaviors and introduces a binary micro-ecological approach to compliance management, effectively integrating individual and project organizational elements. In contrast to traditional corporate governance approaches, this strategy emphasizes the role of project organizational micro-ecology in the management of international construction projects. The strategy aims to improve compliance management among international contractors by influencing the psychological and behavioral compliance of frontline employees.

How alloying and processing effects can influence the microstructure and mechanical properties of directly extruded thin zinc wires

Zinc (Zn) in particular has gained attention as biodegradable metal due to its advantageous corrosion rates compared to magnesium (Mg) or iron (Fe). Still, strength and ductility of zinc are found to be unfavorable for many medical applications. Strategies to overcome such issues base on a distinct grain refinement of the respective product. One important condition of the metal is assumed to be in the form of wires, which in the present work stem from a direct extrusion setup and high degrees of deformation, therefore a hot forming procedure as the underlying thermomechanical treatment. A basic binary alloying approach with Mg, manganese (Mn) and copper (Cu) is applied, limiting the content to a solid solution range of the alloys. The processability and the processing ranges are examined as well as their impact on the microstructure development and the resulting mechanical behavior. Higher extrusion speed leads to inhomogeneous material flow during extrusion. Alloying Zn can reduce the influence of process parameters and decrease the average grain sizes of wires which experienced lower temperature impact. The forming ability of pure Zn and ZnMg-alloy remain limited whereas they appear more beneficial for the alloys with Mn and especially Cu.

1D-CNN: Classification of normal delivery and cesarean section types using cardiotocography time-series signals

Abstract Cardiotocography (CTG) is considered the gold standard for monitoring fetal heart rate (FHR) during pregnancy and labor to estimate the danger of oxygen deprivation. Visual interpretation of CTG traces is complex and frequently results in high rates of false positives and false negatives, leading to unfavorable and unwanted outcomes such as fetal mortality or needless cesarean surgery. If the data are well-balanced, which is uncommon in medical datasets, machine learning techniques can be helpful in interpretation. This study is designed to determine classification performance under various data balance approaches. We propose a robust methodology for the automated extraction of features that use a deep learning model based on the one-dimensional convolutional neural network (1D-CNN). We used a public database containing 552 intrapartum CTG recordings. Due to the imbalance in the dataset, the experiments were conducted under a variety of conditions such as (i) an unbalanced dataset, (ii) undersampling, (iii) a weighted binary cross-entropy approach, and (iv) oversampling utilizing the synthetic minority oversampling technique (SMOTE). We found an excellent sensitivity (99.80% for the unbalanced dataset, 96.25% for the weighted binary cross-entropy approach, and 99.81% with SMOTE) except for the under sampling situation, in which the sensitivity was 85.71%. Moreover, the 1D-CNN model incorporating SMOTE yielded promising results in 88% specificity, 93.72% quality index (QI), and 95.10% area under the curve. The model exhibited excellent performance in terms of sensitivity in every scenario except for undersampling. The oversampling of training data with SMOTE yielded a decent level of specificity, demonstrating the model’s strong predictive capacity. In addition, the SMOTE scenario resulted in fewer training epochs, which is another accomplishment.

PredCoffee: A binary classification approach specifically for coffee odor

Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.

Urban-Rural Differences in the Prevalence of Diabetes Among Adults in Haryana, India: The ICMR-INDIAB Study (ICMR-INDIAB-18).

Diabetes is a multifactorial disease with far-reaching consequences. Environmental factors, such as urban or rural residence, influence its prevalence and associated comorbidities. Haryana-a north Indian state-has undergone rapid urbanisation, and part of it is included in the National Capital Region (NCR). The primary aim of the study is to estimate the prevalence of diabetes in Haryana with urban-rural, NCR and non-NCR regional stratification and assess the factors affecting the likelihood of having diabetes among adults. This sub-group analysis of the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study (a nationally representative cross-sectional population-based survey) was done for Haryana using data from 3722 participants. The dependent variable was diabetes, while residence in NCR/non-NCR and urban-rural areas were prime independent variables. Weighted prevalence was estimated using state-specific sampling weights and standardized using National Family Health Survey-5 (NFHS-5) study weights. Associations were depicted using bivariate analysis, and factors describing the likelihood of living with diabetes were explored using a multivariable binary logistic regression analysis approach. Overall, the weighted prevalence of diabetes in Haryana was higher than the national average (12.4% vs. 11.4%). The prevalence was higher in urban (17.9%) than in rural areas (9.5%). The prevalence of diabetes in rural areas was higher in the NCR region, while that of prediabetes was higher in rural non-NCR region. Urban-rural participants' anthropometric measurements and biochemical profiles depicted non-significant differences. Urban-rural status, age and physical activity levels were the most significant factors that affected the likelihood of living with diabetes. The current analysis provides robust prevalence estimates highlighting the urban-rural disparities. Urban areas continue to have a high prevalence of diabetes and prediabetes; rural areas depict a much higher prevalence of prediabetes than diabetes. With the economic transition rapidly bridging the gap between urban and rural populations, health policymakers should plan efficient strategies to tackle the diabetes epidemic.

The newest human rights: binary approaches to their definition

While for medical professionals, a scientific breakthrough in medicine the like of the ability for transplantation, cloning, and the active implementation of reproductive technologies for addressing infertility is a reason for celebration, then, for legal professionals, it primarily serves as a basis for fruitful work. The issue of newest human rights belongs to the realm of global theoretical and legal issues that can no longer be ignored. Specific constitutional decisions regarding the fourth-generation human rights should be preceded by their scientific contemplation within the framework of legal theory. We believe that such scientific research should be comprehensive, and specific legal conclusions, due to the specific nature of the rights under research, may be supplemented by arguments of philosophical, medical, or religious nature. On the one hand, progress in science and technology positively impacts the realization of already enshrined human rights, opening up new opportunities for their enforcement, protection, and the creation of new areas for international cooperation in this field, opens up new human needs and opportunities, and on the other hand, quite unexpectedly, it can also have a negative impact, creating new threats to the realization of the interests of individuals, society, or the state. Ensuring and protecting the fourth-generation human rights are essential tasks for all national governments, human rights organizations, and the international community as a whole. We emphasize the need to improve the legal framework for ensuring all the newest human rights. The level of recognition and enforcement of such rights indicates the essence of the state and can serve as one of the criteria for recognizing it as a rule of law and democracy. We stress that the research of the fourth-generation human rights is important for several reasons, as they are inextricably linked to other human rights, such as the right to life, health, personal integrity, the right to safe living conditions, etc. Understanding the specifics of newest rights will help ensure the physical, emotional and informational security of a person, and secure their freedom of action based on respect for the personalities of others. The article distinguishes somatic rights, defining them as those that provide a person with additional opportunities in the field of health care. We emphasize that somatic rights should be viewed as an independent group of human rights that meets the interests and needs of modern person. We characterize them as personal subjective human rights, which realize the freedom of action of a person in relation to his or her body.

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer.

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Toward Inclusivity: Rethinking Islamophobic Content Classification in the Digital Age

In this paper, we implement a comprehensive three class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of IslamicTeachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to Islam, providing valuable insights into the complexities of digital discourse. This research contributes to the fields of quantitative social science by offering an improved method for understanding and moderating online discussions on sensitive religious and cultural subjects.

Hierarchical multistep approach for intrusion detection and identification in IoT and Fog computing-based environments

Special security techniques, such as intrusion detection mechanisms, are indispensable in modern computer systems. With the emergence of the Internet of Things they have become even more important. It is important to detect and identify the attack in a category so that countermeasures specific to the threat category can be resolved. However, most existing multiclass detection approaches have some weaknesses, mainly related to detecting specific categories of attacks and problems with false positives. This article addresses this research problem and advances state-of-the-art, bringing contributions to a two-stage detection architecture called DNNET-Ensemble, combining binary and multiclass detection. While the benign traffic can be quickly released on the first detection, the intrusive traffic can be subjected to a robust analysis approach without causing delay issues. Additionally, we propose the DNNET binary approach for the binary detection level, which can provide more accurate and faster binary detection. We present the proposal of a federated strategy to train the neural model of the DNNET method without sending data to the cloud, thus preserving the privacy of local data. The proposed Hybrid Attribute Selection strategy can find an optimal subset of attributes through a wrapper method with a lower training cost due to pre-selection using a filter method. Furthermore, the proposed Soft-SMOTE improvement allows operating with a balanced dataset with a minor training time increase, even in scenarios where there are a large number of classes with a large imbalance among them. Results obtained from experiments on renowned intrusion datasets and laboratory experiments demonstrate that the approach can achieve superior detection rates and false positive performance compared to other state-of-the-art approaches.

Local Means Binary Networks for Image Super-Resolution.

The success of modern single image super-resolution (SISR) algorithms is inspired by the development of deep convolutional neural networks (CNNs). However, these CNN-based methods require considerable computation and complexity, making it impossible for these methods to perform real-time calculations in edge devices. Thus, lightweight model design has become a development trend in the super-resolution field, including pruning, quantization, and other methods. The 1-bit quantization is an extreme lightweight method which can reduce the calculation amount of the model in an extreme manner and is friendly to hardware such as edge devices. Most existing binary quantization approaches lead to a large information loss during forward propagation, especially in detailed color information (e.g., edge, texture, and contrast). The loss of color information makes modern binary methods unsuitable for SISR tasks. We think the loss occurs because these methods typically utilize a uniform threshold to quantize the weights and activations. Thus, in this article, we thoroughly analyze the difference between normal classification tasks and SISR tasks, and present a binarization scheme based on local means. The proposed method can maintain more detailed information in feature maps using dynamic thresholds during quantization. Specifically, each value in the full precision activations has a corresponding threshold during the quantization process, and those thresholds are determined by the full precision values of the surroundings. In addition, a gradient approximator is introduced to adaptively optimize the gradient for updating binary weights. We then verify the effectiveness of our method for training binary networks on several SISR benchmarks including VDSR and SRResNet. Experimental results show that the proposed method can outperform the state-of-the-art algorithms to obtain binary networks for image super-resolution with better peak signal-to-noise ratio (PSNR) values and visual quality.

Estudio del índice de gravedad del síndrome metabólico como factor predictivo de un episodio cardiovascular mayor en mujeres premenopáusicas con lupus eritematoso sistémico

BackgroundPatients with systemic lupus erythematosus (SLE) have an increased risk of metabolic syndrome (MS) and cardiovascular (CV) disease. MS is evaluated binary, limiting the understanding of each component's severity individually. Therefore, severity scores for MS that evaluate them separately have been developed. This study aims to determine the prognosis between MS severity and the occurrence of major adverse cardiovascular events (MACE) in SLE patients. MethodsTen-year follow-up cohort study. Premenopausal>18-year-old women with a previous diagnosis of SLE were included. Patients with recent CV events, pregnancy, thyroid disease, and liposuction were excluded. The variables of interest were CV events; the confounding variables, and the MS severity indexes were examined. Hazard ratios and Kaplan-Meier survival curves were estimated through Cox regression. ResultsA total of 238 women were analyzed: 22 presented MACE, and 216 did not. MS prevalence, measured according to consensus and ATP-III criteria, was higher in MACE patients (50 and 40,95%, respectively). The MetSx-IMC severity index was higher within the MACE group. Cox analysis showed an increase in the MetSx-IMC associated with the risk of suffering MACE in a 1.107 ratio. ConclusionsThe MetSx-IMC severity index, contrary to the binary approaches, is recommended to evaluate MS as a predictor of MACE in SLE patients. Offering improved and more accurate prognosis in patients at risk of developing MCE.

Toward Practical Superconducting Accelerators for Machine Learning Using U-SFQ

Most popular superconducting circuits operate on information carried by ps-wide, μV-tall, single flux quantum (SFQ) pulses. These circuits can operate at frequencies of hundreds of GHz with orders of magnitude lower switching energy than complementary-metal-oxide-semiconductors (CMOS). However, under the stringent area constraints of modern superconductor technologies, fully-fledged, CMOS-inspired superconducting architectures cannot be fabricated at large scales. Unary SFQ (U-SFQ) is an alternative computing paradigm that can address these area constraints. In U-SFQ, information is mapped to a combination of streams of SFQ pulses and in the temporal domain. In this work, we extend U-SFQ to introduce novel building blocks such as a multiplier and an accumulator. These blocks reduce area and power consumption by 2\(\times\)and 4\(\times\)compared with previously proposed U-SFQ building blocks and yield at least 97% area savings compared with binary approaches. Using these multiplier and adder, we propose a U-SFQ Convolutional Neural Network (CNN) hardware accelerator capable of comparable peak performance with state-of-the-art superconducting binary approach (B-SFQ) in 32\(\times\)less area. CNNs can operate with 5–8 bits of resolution with no significant degradation in classification accuracy. For 5 bits of resolution, our proposed accelerator yields 5\(\times\)to 63\(\times\)better performance than CMOS and 15\(\times\)to 173\(\times\)better area efficiency than B-SFQ.

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

Network intrusion detection is an essential component of contemporary cybersecurity strategies, and the development of efficient techniques to accurately identify malicious activities has become a priority. This study investigates the performance of various conventional machine learning algorithms, including decision trees, naive Bayes, naive Bayes trees, random forest, random trees, MLP, and SVM, in detecting network intrusions using binary and multi-classification approaches. Furthermore, the study proposes a deep learning method, CNN-LSTM-SA, which consistently outperforms conventional machine learning techniques in terms of precision, recall, F1 score, and overall accuracy for network intrusion detection. Specifically, the proposed method combines CNN and LSTM with SA in machine learning theory to extract more optimized, strongly correlated features. The proposed method is evaluated using the benchmark NSL-KDD database. The results indicate that the CNN-LSTM-SA method holds great potential in enhancing the efficacy of network intrusion detection systems.