Enhancement Techniques Research Articles

Security enhancement for NOMA-PON with 2D cellular automata and Turing pattern cascading scramble aided fixed-point extended logistic chaotic encryption

The non-orthogonal multiple access-passive optical network (NOMA-PON) is facing the dual security threats of primary user interference and unauthorized third-party user eavesdropping, so efficient data security enhancement techniques are crucial. To solve these problems, we propose a fixed-point extended (FE)-logistic chaotic mapping to reduce the computational complexity while employing a two-dimensional (2D) cellular automata (CA) and Turing pattern (TP) cascading scramble (CA-TPCS) encryption algorithm to further improve the sensitivity of the NOMA-PON system. The CA-TPCS consists of 2D-CA dynamic bit encryption and Turing symbol substitution (TSS). By using FE chaos to construct 2D-CA and adopting index sort to extract the TSS matrix, dynamic diffusion of bits and scrambling of a 2D symbol matrix are achieved. To ensure the key privacy, we employ a dual key mechanism, and uplink data is introduced as the private key. To verify the feasibility of the proposed method, a simulation validation is built on a 17.6 Gb/s power division multiplexing-orthogonal frequency division multiplexing (PDM-OFDM) NOMA-PON system transmitted over 25 km standard single mode fiber (SSMF). The results show that the proposed scheme has no effect on the optimal power allocation rate (PAR) values and the values are all 3. Meanwhile, the receiver sensitivity gains of 0.2 and 0.3 dB are obtained for high-power and low-power users after encryption. The ciphertext has good diffusion and statistical properties, and the key space is flexibly controlled by the FE precision f, the length l of the transmitted bit, and the size T of the TP, with the value of 22f+l+T×T. The results show that the proposed scheme is not only very compatible with PDM technology but also can realize the dual defense of internal aggression and external aggression. It has a good application prospect in the future NOMA-PON.

Supervised single-channel dual domains speech enhancement technique using bidirectional long short-term memory

Supervised single-channel dual domains speech enhancement technique using bidirectional long short-term memory

Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images

Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of 98% F1-accuracy score.

A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

COVID-19 CT Image Segmentation Based on Modified U-Net

Abstract. This paper presents a COVID-19 CT image segmentation method based on a modified U-Net model. In the study and treatment of COVID-19, the segmentation of CT images is crucial for understanding the virus's impact on lung tissues. Traditional image segmentation methods have limitations when dealing with the complex lung lesions caused by COVID-19. Therefore, we optimized the U-Net model's structure and introduced an efficient loss function along with image enhancement techniques to improve segmentation accuracy and computational efficiency. Experiments were conducted on the Medseg and Radiopaedia datasets. The results demonstrate that the modified U-Net model outperforms traditional methods in terms of segmentation accuracy and computational efficiency. Additionally, we discuss the advantages and limitations of the model and propose directions for future research.

Wildfire Identification Based on an Improved MobileNetV3-Small Model

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a wildfire dataset is constructed and subsequently expanded through image enhancement techniques. Secondly, an efficient channel attention mechanism (ECA) is utilised instead of the Squeeze-and-Excitation (SE) module within the MobileNetV3-Small model to enhance the model’s identification speed. Lastly, a support vector machine (SVM) is employed to replace the classification layer of the MobileNetV3-Small model, with principal component analysis (PCA) applied before the SVM to reduce the dimensionality of the features, thereby enhancing the SVM’s identification efficiency. The experimental results demonstrate that the improved model achieves an accuracy of 98.75% and an average frame rate of 93. Compared to the initial model, the mean frame rate has been elevated by 7.23. The wildfire identification model designed in this paper improves the speed of identification while maintaining accuracy, thereby advancing the development and application of CNNs in the field of wildfire monitoring.

Analysis of Gain Enhancement Techniques in Integrated Circuit Design

Abstract. Today, the size of semiconductor devices is still decreasing, which makes the circuit more integrated, faster computing speed, and lower power consumption. But for a single transistor, some performance degrades. One of the important properties is voltage gain, which must be compensated with some special methods in the design. This paper will focus on the discussion and analysis of four methods, which are able to improve the voltage gain. These are cascode amplifier, cascade amplifier, gain boosting, and bootstrapping. In this letter, their respective advantages and disadvantages will be discussed. Furthermore current applications and possible external development of these methods are also important part of this paper. In this part, the discussion will be further expanded and summarized on the basis of previous studies. Hopefully, these studies can help others gain a more comprehensive understanding of these technologies and provide a theoretical basis for future high-performance analog integrated operational amplifier research and design.

A robust approach for balancing the color and light integrity of underwater images

Abstract As light travels under the deep water, it scatters and is absorbed, resulting in a loss of intensity and altered color perception—a phenomenon known as underwater light attenuation. Images captured under these low light conditions suffered from color distortions, as you go deeper, colors fade in this order: red, orange, and yellow, while green and blue become more prominent. The red channel experiences significant attenuation due to the scattering properties of light under the deep water. As a consequence, deep water images often display noticeable color casts. Researchers encounter various challenges when enhancing low-light underwater images, such as reduced contrast, detail loss, artifacts, noise, and color distortion. In this paper, we present a novel Adaptive Color and Light Correction (ACLC) method for color correction and an Intuitionistic Fuzzy Generator (IFG) for enhancing low-light underwater images. The proposed Adaptive Color and Light Correction (ACLC) method tackles color casts on individual pixels by considering the scene depth. The proposed Intuitionistic Fuzzy Generator (IFG) method balances the image contrast by computing an intuitionistic fuzzy image representation using the proposed IFG approach. Experimental results reveal that the proposed approach significantly improves the color quality and contrast of the output image. The proposed ACLC and IFG methods exceed existing underwater color correction and low-light image enhancement techniques in visual and quantitative evaluations, as evidenced by extensive experimentation on well-established underwater image datasets, such as UIEB, Ocean dark, and LSUI.

Detection of Ovarian Tumors Using TVUS Imaging and Segmentation Techniques

Ovarian most cancers remains a giant health subject due to its regularly asymptomatic development until superior stages, where treatment options are confined. Early detection is critical for enhancing affected person outcomes and survival costs. This observe proposes a comprehensive technique leveraging superior photograph processing techniques for the early detection and category of ovarian tumors the usage of trans vaginal ultrasound (TVUS) snap shots. The method starts with preprocessing steps which include median filtering to do away with noise artifacts inclusive of salt and pepper noise from ultrasound photos. Ultimately, picture enhancement techniques are applied to enhance contrast and intensity, observed through binary sample evaluation to signify texture features vital for tumor identification. Photo segmentation is performed the use of okay- manner clustering and gray degree Co-prevalence Matrix (GLCM) analysis to isolate and analyze areas of interest within the ovarian photos. A Convolutional Neural community (CNN) is then hired for correct classification of ovarian tumors as regular, benign, or malignant primarily based on the extracted functions. This approach not handiest enhances the diagnostic accuracy but also minimizes affected person publicity to ionizing radiation, making it appropriate for ordinary medical applications. The proposed methodology represents a considerable advancement within the discipline of ovarian cancer detection, presenting a non-invasive and green way to evaluate ovarian health and facilitate timely intervention. Destiny research directions include refining the set of rules’s performance with large datasets and exploring additional imaging modalities for complete ovarian fitness evaluation.

Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

ABSTRACTEarly diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite breast thermogram images having high quality, doctors in developing countries often struggle with early diagnosis due to difficulties in interpreting subtle details. Implementing a Computer‐Aided Diagnosis (CAD) system can assist doctors in accurately analyzing these details. This article presents an innovative approach to breast cancer diagnosis using thermal images. The proposed method enhances the quality and clarity of relevant features while preserving sharp and curved edges through U‐Net‐based segmentation for automatic selection of the ROI, advanced hybrid image enhancement techniques, and a machine learning classifier. Subjective analysis compares the processed images with five conventional enhancement techniques, demonstrating the efficiency of the proposed method. The quantitative analysis further validates the effectiveness of the proposed method against five conventional methods using four quality measures. The proposed method achieves superior performance with PSNR of 15.27 for normal and 14.31 for malignant images, AMBE of 6.594 for normal and 7.46 for malignant images, SSIM of 0.829 for normal and 0.80 for malignant images, and DSSIM of 0.084 for normal and 0.14 for malignant images. The classification phase evaluates four classifiers using 13 features from three categories. The Random Forest (RF) classifier with Discrete Wavelet Transform (DWT) based features initially outperformed other classifier features but had limited performance, with accuracy, sensitivity and specificity of 81.8%, 88.8%, and 91%, respectively. To improve this, three categories of features were normalized and converted into two principal components using Principal Component Analysis (PCA) to train the RF classifier, which then showed superior performance with 97.7% accuracy, 96.5% sensitivity, and 98.2% specificity. The dataset utilized in this article is obtained from the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, India. The entire proposed model is implemented in a Jupyter notebook.

The role of hubs and economies of scale in network expansion

Freight distribution often operates on the basis of consolidation, which is achieved through the use of hub facilities that allow for economies of scale. Freight networks need to be expanded to meet future demand, to cater for new markets, and to accommodate trends in global supply chains, for which strategic decisions need to be made. These decisions mainly entail the number and location of new hubs to be established. As network expansions require significant capital, striking a balance between the benefits afforded by the new hubs and the expansion costs is crucial. This paper investigates a hub network expansion problem where the configuration of the resulting network is determined by the trade-off between the fixed costs of locating new hubs and new links, as well as routing costs of shipping commodities, and the cost reductions achieved through economies of scale, without imposing a predetermined network structure. This paper also describes a mixed integer programming formulation of the problem and a Benders decomposition algorithm that uses several enhancement techniques to efficiently solve the model to optimality. The application of the algorithm on a real-life case study arising in the expansion of the Indonesian freight transport network yields several managerial insights. In particular, expanding the network with additional hubs and links can yield substantial cost savings, averaging at 47.6%, although at the expense of an increase in the length of the commodity paths. Failing to operate the network at the selected level of economies of scale can result in an increase in the routing costs by up to 58.8%. Expanding the network with no additional hubs leads to a rise in total costs of up to 20.9%. Finally, lower economies of scale leads to an increase in the length of commodity paths, with the routing cost being identified as the most sensitive parameter.

A robust multi-label feature selection based on label significance and fuzzy entropy

Multi-label feature selection is one of the key steps in dealing with multi-label classification problems in high-dimensional data. In this step, label enhancement techniques play an important role. However, it is worth noting that many current methods tend to ignore the intrinsic connection between inter-sample similarity and inter-label correlation when implementing label enhancement learning. The neglect may prevent the process of label enhancement from accurately revealing the complex structure and underlying patterns within data. For this reason, a fuzzy multi-label feature selection method based on label significance and fuzzy entropy is proposed. An innovative label enhancement technique that considers not only the intrinsic connection between features and labels, but also the correlation between labels was first devised. Based on this enhanced label representation, the concept of fuzzy entropy is further defined to quantify the uncertainty of features for multi-label classification tasks. Subsequently, a feature selection algorithm based on feature importance and label importance was developed. The algorithm guides the feature selection process by evaluating how much each feature contributes to multi-label classification and how important each label is to the overall classification task. Finally, through a series of experimental validation, the proposed algorithm is proved to have better performance for multi-label classification tasks.

Development of Adaptive Gaussian Filter Based Denoising as an Image Enhancement Technique

Development of Adaptive Gaussian Filter Based Denoising as an Image Enhancement Technique

Machine learning enabled compact flexible full ground UWB antenna for wearable applications

Abstract This work introduces a novel compact ultra-wideband (UWB) antenna designed for wearable applications, employing a bioinspired structure and machine learning (ML) techniques to achieve exceptional performance in the 3.10–10.42 GHz range. The antenna is fabricated by positioning conductive fabric on a polydimethylsiloxane polymer of 1 mm thickness to augment high flexibility and durability. Additionally, it pioneers integrating a complete ground plane to mitigate back radiation toward the human body, presenting a compact (35.5 × 30.5 × 1 mm3) UWB antenna design compliant with IEEE 802.15.6 standards. The design methodology includes using bandwidth enhancement techniques such as chamfering edges, slots, and adding stubs in the feed, along with applying ML to optimize the antenna’s dimensions for desired return loss characteristics. The proposed antenna demonstrates exceptional resilience to human body loading and physical deformation. The simulation and measurement results have good agreement. The K-nearest neighbour method beat the other ML algorithms maximum accuracy of 99.62% to predict the S11. According to the author’s best knowledge, this is the first compact UWB antenna with full ground specified by IEEE.802.15.6 with ML reported.

Selection of Optimum Enhancement Technique for the Discrimination of Alkali Granites and Syenites around Suryamalai Batholith of Central Tamil Nadu, India

Image processing techniques such as Band Ratio, Principal Component Analysis, Supervised, Unsupervised classification and Reclassification techniques are generally used as an enhancement technique for alteration zone demarcation and lithological discrimination in geosciences environment. In the present study, Landsat OLI satellite imagery has been utilized to produce various combinations of image enhancement to discriminate alkali syenite and alkali granite of Proterozoic age. These techniques were also used to differentiate three phases of granites reported in Suryamalai Batholith which is very difficult to map by conventional method due to its rugged topographic nature. 12 types of enhancement techniques were applied to differentiate rock alkali syenite from granite, however only few methods such as Kaufmanns ratio, ratio of 5/7, 3/1 and 3/5 and supervised classification were found to be suitable for the discrimination of those rocky types. Band ratio 5/7, 3/1 and 3/5, Crosta analysis and thermal reclassification techniques significantly brought to light the difference between the metamorphic rocks such as fissile hornblende biotite gneiss and hornblende biotite gneiss of Archaean age. Similarly, phase I and phase III granites were perfectly discriminated from all the image enhancement techniques. It is differentiated mainly based on its grain size and mineral variation between these two rock types. Study revealed that standard digital image processing technique can be effectively used for lithological mapping.

Research on Techniques for Enhancing the Speed of Low-Power Operational Amplifier

In the context of low utilization, this paper explores various techniques for enhancing the speed of operational amplifier (OA). The main text delves into three methods to gain equilibrium: fully differential operational amplifier (FDA), twostage operational transconductance amplifier (OTA), and a novel high-speed calculation system architecture employing a “rhombus crystal tube.” The FDA improves speed by minimizing noise and enhancing bandwidth and output voltage swing, without increasing power consumption. The design of the two-stage OTA combines the characteristics of a differential amplifier and a telescopic amplifier, optimizing gain and slew rate through Miller compensation and noise gain manipulation, thereby achieving high-speed performance. A novel high-speed operational amplifier structure employs diamond transistors to supply substantial current for capacitor charging, enhancing the slew rate and overall speed. Throughout the text, these methods are presented as a means to jointly promote high speed, low power consumption, and the handling of a significant number of transistors. To further increase the speed, the size of the microcrystalline tube is crucial. The research direction is outlined, and while the design equipment is in the foreground, there remains room for progress, especially in applications for large-scale electrical models. Future research aims to enhance the power efficiency of circuits, making them more practical and efficient. This study provides valuable insights into balancing power consumption and speed in advanced electronic devices.

Status and Enhancement Techniques of Plastic Waste Degradation in the Environment: A Review

Plastic waste has been gradually accumulating in the environment due to rapid population growth and increasing consumer demand, posing threats to both the environment and human health. In this overview, we provide a comprehensive understanding of the degradation of plastics in real environments, such as soil, aquatic environment, landfill, and compost. Both conventional and biodegradable plastics exhibit limited degradation in real environments, except for biodegradable plastics during industrial composting with high thermophilic temperatures. Meanwhile, we also review techniques for enhanced degradation of plastics such as physical technologies (e.g., photocatalysis, mechanical degradation, and pyrolysis), chemical technologies (e.g., hydrolysis, alcoholysis, ammonia, strong oxidation, and supercritical fluids), and biotechnologies (e.g., microorganisms, microfauna, and microalgae). The future research directions for the enhancement of plastic degradation are also discussed, such as the establishment of equivalency standards, adoption of internal control techniques, the control of precise recycling of plastic products, and the ecotoxicology of their degradation products. Therefore, this review comprehensively summarizes the state of plastic degradation in real environments and proposes methods to improve plastic degradation, providing a theoretical basis for the future control and disposal of plastics.

Myocardial scar in end-stage hypertrophic cardiomyopathy: correlation with systolic function and prognostic significance

Abstract Background Hypertrophic cardiomyopathy with systolic impairment, often referred to as end-stage HCM (ES-HCM), has a poor prognosis. Scar is a significant contributor but the patterns and prognostic significance are unknown. Aim To understand role of scar in ES-HCM and investigate potential predictors of outcome. Methods A retrospective 4 centre study of ES-HCM (LVEF≤55%) who had undergone CMR between 2006 and 2022 for unexplained LVH. Exclusion criteria were: scaring therapies (septal reduction), phenocopies (amyloidosis, storage) and dual pathologies (severe aortic valve disease, previous infarction). CMR followed standardized protocols. All images were core-lab analysed de-novo with scar quantified using the full width at half maximum technique for late gadolinium enhancement (LGE) quantification in grams (LGEm) and percentage of total LV mass (LGE%). Cox models used to identify risk factors for all-cause mortality. Results From 3810 HCM CMR scans, 128 were ES-HCM pts (3%), 25(20%) were excluded due to known infarction. 103 (male n=82, 81%; age 57yrs[IQR51–68]) formed the study cohort. Of the 54% genotyped, 62% carried a (likely)pathogenic sarcomere mutation: MYBPC3 (n=20), MYH7 (n=7), TNNT2 (n=4), TMP1 (n=2),ALPK3 (n=1). Heart morphology was: 38(37%) isolated basal septal hypertrophy, 30(29%) reverse septal curvature, 7(7%) mid-cavity LVH with apical aneurysm, 3(3%) apical HCM, 24%other. The median EF was 46%(IQR 39-50). In only one third (34%) the ventricle was dilated (LVEDd mean 97ml/m2 IQR 79-112). The RV was impaired in 19(18%). Median max wall thickness was 17mm(IQR15-20) and LVmass 74mg/Kg(IQR65-90). Scar was present in 94(93%) and was typically extensive: median LGE% 23%(IQR 13-33), LGEm 31g(IQR 16-45) – although 7% had no scar. The LGE pattern was diffuse in 38%, patchy in 35%, ring-like in 9%, and infarct-like in 7% (all without coronary narrowing at invasive/non-invasive coronary artery assessment). LGE and cardiac function were effectively independent with low or no correlation between scar and LVEF% or global longitudinal strain (r2=0.015,p=0.227 or r2= 0.048,p=0.028 for LGE% and LGEm against LVEF; r2=0.059,p=0.021 or r2=0.196,p<0.001 for LGE% and LGEm against GLS) with poor correlation also with indexed stroke volume. During a 5 year (IQR 3-7) follow-up (516patient-years), 30 deaths occurred: mean age at death was 62(52-74). Using multivariate Cox regression analysis EF and demographics such as age were not predictive, but LGE% (HR 1.046, 95%IC 1.007-1.086, p=0.019 and GLS (HR 1.172, 95%IC 1.004-1.368, p=0.044) were independent all-cause mortality predictors (C-index 0.708; table). Conclusion Myocardial scar is almost always present in ES-HCM and is typically extensive – but with some unexpected features: 7% of patients have no LGE, dual pathology(infarction) is not rare, and ringlike LGE (ALVClike) may occur. LGE correlates poorly with any measure of LV impairment but is a strong predictor of mortality, as is GLS.

Exploring Pointer Enhancement Techniques for Target Selection on Large Curved Display

Large curved displays are becoming increasingly popular due to their ability to provide users with a wider field of view and a more immersive experience compared to flat displays. Current interaction techniques for large curved displays often assume a user is positioned at the display's centre, crucially failing to accommodate general use conditions where the user may move during use. In this work, we investigated how user position impacts pointing interaction on large curved displays and evaluated cursor enhancement techniques to provide faster and more accurate performance across positions. To this effect, we conducted two user studies. First, we evaluated the effects of user position on pointing performance on a large semi-circular display (3m-tall, 3270R curvature) through a 2D Fitts' Law selection task. Our results indicate that as users move away from the display, their pointing speed significantly increases (at least by 9%), but accuracy decreases (by at least 6%). Additionally, we observed participants were slower when pointing from laterally offset positions. Secondly, we explored which pointing techniques providing motor- and visual-space enhancements best afford effective pointing performance across user positions. Across a total of six techniques tested, we found that a combination of acceleration and distance-based adjustments with cursor enlargement significantly improves target selection speed and accuracy across different user positions. Results further show techniques with visual-space enhancements (e.g., cursor enlargement) are significantly faster and more accurate than their non-visually-enhanced counterparts. Based on our results we provide design recommendations for implementing cursor enhancement techniques for large curved displays.

Self-assembled nanoparticles of alginate and paclitaxel-triphenylphosphonium for mitochondrial apoptosis targeting.

Paclitaxel (PTX), an antimitotic drug from the taxanes group, prevents the proliferation of breast cancer cells through mitosis arrest and activation by a cascade of signaling pathways that lead to apoptosis. Mitochondria is one of the important signaling routes for inducing apoptosis. For mitochondria targeting, triphenylphosphonium (TPP) with a delocalized charge and hydrophobic nature was utilized as a moiety to facilitate penetration through a phospholipid membrane of mitochondria. PTX-TPP was synthesized via pH-sensitive ester bond between hydroxyl groups of PTX and carboxylic acid of (4-carboxybutyl) TPP. Then PTX-TPP prodrug encapsulated in alginate nanoparticles, which were self-assembled by the ionotropic complexation technique for enhancement of mitochondrial apoptosis in breast cancer cells. The loading of PTX-TPP conjugation in self-assembled alginate nanoparticles was 16.5% and the particle size of nanoparticles was 123nm with zeta potential around -25.8 Mv. The in vitro cytotoxicity and IC50 of PTX-TPP nanoparticles in the growth of MCF7 cancer cell increased 6.3-fold higher than free PTX. The early apoptotic cells and the late apoptotic/necrotic cells for PTX-TPP nanoparticles were 11.6 and 3.9-fold higher than free PTX. This study indicated this mitochondrial-targeted self-assembled nanoparticles can inhibit the tumor cell growth of breast cancer.