Hybrid Technique Research Articles

Decoding skin cancer classification: perspectives, insights, and advances through researchers’ lens

Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.

Modeling Viral Capsid Assembly: A Review of Computational Strategies and Applications

Viral capsid assembly is a complex and critical process, essential for understanding viral behavior, evolution, and the development of antiviral treatments, vaccines, and nanotechnology. Significant progress in studying viral capsid assembly has been achieved through various computational approaches, including molecular dynamics (MD) simulations, stochastic dynamics simulations, coarse-grained (CG) models, electrostatic analyses, lattice models, hybrid techniques, machine learning methods, and kinetic models. Each of these techniques offers unique advantages, and by integrating these diverse computational strategies, researchers can more accurately model the dynamic behaviors and structural features of viral capsids, deepening our understanding of the assembly process. This review provides a comprehensive overview of studies on viral capsid assembly, emphasizing their critical role in advancing our knowledge. It examines the contributions, strengths, and limitations of different computational methods, presents key computational works in the field, and analyzes milestone studies that have shaped current research.

Osteoporosis Disease Detection using Optimized Elman Recurrent Neural Network based on Hybrid Bacterial Colony Optimization and Tabu Search Algorithm

Bone loss and fragility are indications of osteoporosis, a condition caused by calcium deficiency. The detection of osteoporosis is a significant and difficult diagnostic endeavor. Elman recurrent neural network (ERNN) is a well-known medical disease detection method due to its modeling sequential data and capturing temporal dependencies. ERNN training can be computationally costly and necessitates precise adjustment of hyperparameters. In this research, optimized ERNN is used to predict osteoporosis diseases to achieve high detection accuracy and to improve the global convergence rate. The new hybrid method is used to optimize the hyperparameters of ERNN based on the bacterial colony optimization (BCO) and tabu search (TS) algorithm, which is called IBCO-ERNN. The hybrid technique can efficiently explore the solution space by combining BCO's global exploration capabilities and TS's local exploitation capability, perhaps leading to better solutions to hyperparameter optimization problems. The hybrid BCO-TS strategy trains the ERNN model to prevent local optima and improve convergence rate. The experimental results demonstrated that the proposed IBCO-ERNN obtained high accuracy and fast convergence compared to other detection methods.

Automated Cardiovascular Disease Detection with Hybrid Machine Approach

Cardiovascular ailment (CVD) is a main reason of death and disability globally, posing tremendous challenges for early detection and prevention. A heart condition or the accumulation of fatty deposits inside the arteries, is frequently linked to CVD and raises the risk of blood clots and other consequences. Predicting and diagnosing CVD is critical to mitigating its effect and enhancing patient results. With the appearance of massive data in healthcare, large volumes of patient records are available that may assist in identifying early warning symptoms of coronary heart disorder. This project aims to generate a hybrid machine learning technique which leverages deep learning and traditional machine learning algorithms to automatically locate cardiovascular illnesses. By integrating more than one dataset, we intend to upgrade the model's overall performance in diagnosing coronary heart situations. Numerous machine learning classification models, consisting of Random Forest and Gradient Boosting, might be employed and in comparison, to evaluate their effectiveness in detecting CVD. Each model can be evaluated using overall performance indicators like precision, accuracy, recall, and F1-Score. In previous research, the Random Forest model did 94% accuracy in coronary heart disorder detection, at the same time as the Gradient Boosting model established a balanced overall performance through accuracy, precision, recall, and F1-score with 73% each in detecting cardiovascular diseases via this hybrid technique. We aim to enhance prediction accuracy and offer a greater reliable tool for early CVD detection, in the long run enhancing affected person care and saving lives.

Drone-Based Adaptive Hybrid Techniques for Improving Face Detection and Recognition

With the rapid development of Unmanned Aerial Vehicle (UAV) related technologies, Drones gained the opportunity to search locations that are dangerous or difficult for humans to reach. In this regard, Drones technology has been widely used to detect and recognize humans on the ground. In order to identify their potential role in counter-terrorism operations, the aim of this research study is to design and develop a reliable aerial system capable of identifying people with a high detection rate. Furthermore, it presents a solution to all challenges that degrade the resolution when query images are taken from high altitudes, different angles, and long distances. In the context of improving the accuracy of facial detection and recognition, two adaptive techniques are proposed: The first approach is to adaptively adjust the Drone’s altitude, speed, and attitude based on two parameters: Ground sampling distance (GSD), and confidence level of the recognized face. The second approach is to adaptively adjust the resolution of images captured by drone based on the size of the detected facial area. Face detection and recognition are done using the Haar Cascade classifier and Local Binary Pattern Histogram (LBPH) algorithm. The entire system was developed, implemented, and tested using a Hexacopter Drone and onboard vision system. The testing results corroborate the system's practicality and demonstrate that the prototype may be simply implemented to track dangerous people with criminal records.

Demand Forecasting for Agro Tech Technical Textiles

Demand forecasting is vital in the agrotech industry, merging technology with agriculture to drive innovation and efficiency. Accurate forecasting helps companies predict market trends, optimize resource use, and enhance operations. By anticipating future demand for agricultural products and technological solutions, businesses can make informed decisions on production, inventory, and marketing. However, demand forecasting in agrotech faces challenges such as seasonal variations, limited data, technological complexity, and market uncertainties. Factors like weather patterns, regulatory changes, and shifting consumer preferences add to the difficulty. To address these challenges, methodologies such as statistical techniques, data analytics, and machine learning are employed. Common methods include time series analysis, regression analysis, and hybrid techniques, which use historical data to forecast future trends. Effective demand forecasting improves resource planning, supply chain management, and customer satisfaction. Aligning production with expected demand and optimizing inventory can cut costs, reduce waste, and boost competitiveness. Demand forecasting is essential for strategic planning in agrotech, enabling businesses to predict market trends, meet customer needs, and achieve sustainable growth in a dynamic environment.

Definitive Evidence for the Identification and Function of Renin-Expressing Cholinergic Neurons in the Nucleus Ambiguus

BACKGROUND: The importance of the brain renin-angiotensin system in cardiovascular function is well accepted. However, not knowing the precise source of renin in the brain has been a limitation toward a complete understanding of how the brain renin-angiotensin system operates. METHODS: Highly sensitive in situ hybridization techniques and conditional knockout mice were used to address the location and function of renin in the brainstem. RESULTS: We identified novel renin-expressing cholinergic neurons in the nucleus ambiguus (NuAm), a major vagal cardioinhibitory center in the brainstem. The expression of renin-angiotensin system genes was relatively abundant in the NuAm, implying that angiotensin II might mediate an important regulatory role in this nucleus and other regions with neural connectivity to the NuAm. Then, we generated conditional knockout mice lacking the classical renin isoform (Ren-a ChAT-KO ), specifically in cholinergic neurons. Ablation of Ren-a in cholinergic neurons abrogated renin expression in the NuAm. Moreover, studies using radiotelemetry, heart rate variability analyses, and pharmacological approaches revealed that the parasympathetic nervous system is depressed in Ren-a ChAT-KO males while augmented in the Ren-a ChAT-KO females. Subsequently, transcriptomic approaches were used to infer putative genes and signaling pathways regulated by renin within the NuAm. CONCLUSIONS: This study revealed that renin in cholinergic neurons plays a fundamental role in preserving autonomic balance and cardiovascular homeostasis in a sex-dependent manner. These findings define the NuAm as an endogenous, local source of renin with biological function and serve as conclusive evidence for the presence and functionality of the brain renin-angiotensin system.

Comparative analysis of the effectiveness of instrumentation with manual stainless steel and NiTi files in the endodontic treatment

The aim of this study was to assess the effectiveness in instrumentation and the main differences between the manual systems K and M. Material and methods: Forty Type IV Vertucci standardized lower molars, considering the degree of curvature, were divided into 2 groups (n=10): M Files and K-File Files. After biomechanical preparation, the teeth were filled using the Tagger Hybrid technique. Digital radiographs were taken in the buccolingual and mesiodistal directions, and the quality of the filling was determined by three calibrated and blinded evaluators using a 4-point scoring system. The instrument fracture index was also recorded. The obtained data were subjected to Mann-Whitney tests (p<0.05). Results: In the analysis conducted by experienced professionals, a higher radiographic quality of obturation was observed in the M file group compared to the K-file group (p<0.05). Regarding the perception of undergraduate students regarding the difficulty of procedures, a greater difficulty was observed in preparing molars with K-Files compared to M Files (p<0.05). A higher percentage of fractured K-Files was observed compared to M Files. Conclusion: M Files provided less difficulty in preparing molars for undergraduate students, had a lower incidence of fracture, and favored better radiographic quality of obturation.

Hybrid Hysterectomy Versus Laparoscopic and Open Approaches: A Propensity Score-Matched Comparison of Outcomes.

To evaluate the surgical outcomes of hybrid hysterectomy for enlarged uteri, compared to laparoscopic and open approaches. Matched case-control study SETTING: Tertiary-care academic medical center. Patients who underwent hybrid hysterectomy between January 1, 2010, and December 31, 2021, were included. For comparison, a matched case-control approach was used with two propensity score-matched groups: patients who underwent laparoscopic hysterectomy and those who underwent open hysterectomy. Hybrid hysterectomy, laparoscopic hysterectomy, and open hysterectomy. A total of 76 patients underwent hybrid hysterectomy for enlarged uteri that could not be removed intact through a colpotomy. Two comparison groups were identified by propensity-score matching the hybrid cohort to laparoscopic (n=3020) and open cohorts (n=340) based on the following criteria: age, BMI, uterine weight, year of surgery, surgeon type, hysterectomy subtype, and indication for the procedure. Operative time for the hybrid group (152 min) was comparable to the open group (148 min), but longer than the laparoscopic group (112 min, P<0.001). Compared to the hybrid group (with EBL of 50 ml), the open group had a significantly higher median EBL (200 ml, P<0.001), and the laparoscopy group had a significantly lower median EBL (27.5 ml, P=0.015). Median length of hospital stay for the hybrid group (1 day) was shorter than the open group (3 days, P<0.001), and longer than the laparoscopic group (0 days, P<0.001). Postoperative opioid administered to the hybrid group (55.0 MME) was significantly lower than the open group (91.9 MME, P=0.012), and significantly higher than the laparoscopic group (23.6 MME, P<0.001). Our results indicate an advantage of the hybrid technique over an exclusively open approach when morcellation of the specimen is not appropriate. The hybrid approach is associated with less blood loss, shorter length of hospital stay, and decreased postoperative pain, with comparable operative time, and complication rates compared to the open approach.

CircRNA-RBAC induces cardiac repair by promoting ribosome biogenesis and cell cycle progression in cardiomyocytes

Ribosome biogenesis (RiBi) is an essential process that controls the protein synthesis rate, but its function in regulating endogenous cardiac regeneration remains unknown. Herein, we investigated the function and underlying mechanism of RiBi-associated circRNAs in cardiomyocyte (CM) proliferation and cardiac regeneration. We used high-throughput sequencing, quantitative PCR and in situ hybridization techniques to identify an adult downregulated circRNA, RiBi-associated circRNA (RBAC), in CMs. A functional study further revealed that RBAC overexpression increased ribosome biogenesis activity and cell cycle progression in CMs, while silencing RBAC decreased ribosome biogenesis activity and cell cycle progression. Moreover, RBAC overexpression induced adult CM proliferation and improved cardiac function after myocardial infarction in adult mice. Mechanistically, RBAC controlled ribosome biogenesis and cell proliferation by regulating the proteasome-dependent degradation of Ddx21, thereby altering the localization of Rps14 and reducing Rb expression. Our findings indicate that RBAC upregulation might be a plausible therapeutic strategy to induce endogenous cardiac regeneration.

Assessment of cutting force coefficient identification methods and force models for variable pitch and helix bull-nose tools

The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average Fx, Fy and Fz cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.

GIS-based modeling of the environmental vulnerability of the Amazon region to the upstream oil and gas activities

ABSTRACT To improve the security of investors in the petroleum industry and to bestow transparency and reliability to its licensing process, the Brazilian Government has recently implemented a Strategic Environmental Assessment (SEA) to evaluate the potential environmental effects of oil and gas exploration and production activities before the concession of areas. To provide robustness to SEA’s analysis stage, this paper aims to propose an innovative methodological approach to map the environmental vulnerability of the area under study, considering selected environmental receptors. This approach combines two techniques, one analytical and the other spatial: environmental risk analysis and spatial analysis. To better illustrate it, this paper presents a case study of the Solimões Sedimentary Basin. Findings show that the approach was able to indicate the more suitable areas inside the SEA region for the development of the O&G upstream activities, as well as to map the vulnerability of each analyzed environmental receptor, to the complete chain of activities, from the exploration for resources until the decommissioning of facilities. This hybrid technique proved to be suitable to the levels of detail required by SEAs and may allow the Government’s goals with it to be achievable, including the environmental issues earlier, in its sectorial PPPs.

A Survey on Hybrid Deep Learning and Neural Fuzzy Inference Systems for Early Coronary Heart Disease Detection

coronary heart disease (CHD) continues to be a primary cause of global mortality, highlighting the critical necessity for precise and early detection techniques to enable prompt management and prevention. Recent breakthroughs in ML and DL have demonstrated potential in improving diagnosis accuracy; yet, challenges remain regarding interpretability, computational complexity, and the management of ambiguous or unclear medical data. This survey examines advanced methodologies and investigates the possibility of hybrid frameworks that combine deep learning models with neural fuzzy inference systems (NFIS) for the identification and prevention of CHD. Hybrid techniques, which integrate the feature extraction and learning capabilities of deep learning with the interpretability and uncertainty management of neuro-fuzzy inference systems, provide a robust framework for enhancing early coronary heart disease diagnosis and risk evaluation. We offer an extensive comparison of modern machine learning, deep learning, and hybrid models, evaluating their performance across multiple measures, including accuracy, sensitivity, and computing demands. This paper examines upcoming topics such as transfer learning, multi-modal data integration, and explainable AI, emphasising the prospective applications of these systems in clinical environments. Our research indicates that hybrid DL-NFIS models possess considerable potential for improving CHD identification and, thus, augmenting patient outcomes in preventive healthcare.

The Efficient Robust Conformable Methods for Solving the Conformable Fractional Cahn-Allen Equation

This study focuses on the novel conformable methods employed to obtain new numerical solutions for the Cahn-Allen equation with conformable fractional derivatives. One of the two distinct methods put forth is the Cq-HATM, a hybrid technique that integrates the q-homotopy analysis transform method with the Laplace transform, utilizing the definition of conformable derivative. The CHPETM is a hybrid technique that combines the homotopy perturbation method with the Elzaki transform (ET). New numerical solutions of the conformal fractional differential Cahn-Allen equation were obtained using CHPETM and Cq-HATM. The computer simulations have been conducted in order to provide validation for the efficacy and reliability of the proposed methods. Upon performing a comparative analysis between the exact solutions and the solutions obtained through the novel methods, it becomes evident that both of these approaches exhibit simplicity, efficacy, and proficiency in addressing nonlinear conformable time-fractional coupled systems.

Optimal power utilization in hybrid microgrid systems with IoT-based battery-sustained energy management using RSA-PFGAN approach

The quantity of power electronics converters that interface with the various components of a hybrid microgrid system has a major impact on its efficiency. Minimizing power conversion stages and increasing system efficiency requires integrating a photovoltaic system with micro grids while maximizing the number of converters. This paper presents a hybrid approach for utilizing power in microgrid system with an Internet of Things (IoT) based battery sustained energy management scheme. The proposed hybrid technique combines the Reptile Search Algorithm (RSA) and Progressive Fusion Generative Adversarial Network (PFGAN). Thus, it is referred to as the RSA-PFGAN technique. The principal aim of the proposed approach is to minimize operating costs, improve voltage profiles, and reduce computation time and errors. The discharging and charging strategy of the battery is optimized by the RSA approach. The load demand is predicted using the PFGAN approach. Using MATLAB, the proposed method is evaluated and contrasted to other existing methods. The proposed approach determines better outcomes contrasted to existing techniques such as Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO) and Seeker Optimization Algorithm (SOA). The proposed method achieves an efficiency of 85 %, significantly higher than the PSO's 55 %, WHO's 65 %, and SOA's 75 %. Additionally, the proposed approach exhibits a computation time of just 0.21 s, demonstrating its efficiency compared to PSO at 2.95 s, WHO at 0.87 s, and SOA at 0.43 s. These results indicates that the RSA-PFGAN method offers better performance in terms of cost, efficiency, and computation time for hybrid microgrid systems.

The function of HgLac in Heterodera glycines and its potential as a control target

The soybean cyst nematode (SCN; Heterodera glycines) is one of the most devastating pathogens for soybean production. The second stage juvenile (J2) invades the host root, develops and form white females which then become brown cysts enter the soil. The brown cyst wall plays a key role in protecting inside eggs from adverse environmental conditions. However, the function of cyst wall tanning (sclerotization and pigmentation) in nematodes is not clear. A browning-related gene discovered from the whole-genome sequencing was cloned and characterized in this study, the gene was confirmed to be the laccase gene and was named HgLac. HgLac mRNA and HgLac protein was detected in the epidermis of juveniles using in situ hybridization and immunolocalization techniques. The HgLac expression level was greater in fourth-stage juveniles (J4s) than in the other stages. Knockdown of HgLac by in vitro RNA interference (RNAi) significantly decreased the infectivity, development and reproduction of J2s but had no effect on cyst wall tanning. Further research revealed that HgLac expression in nematodes was significantly suppressed by 35.41–59.17 % through in planta RNAi, 52.96–58.19 % females could not tan successfully, and the female wall was very soft and fragile, with a low egg hatching rate (1.33 %), which was significantly lower than that of normal females (68.85 %). These results indicate that HgLac plays a key role in cyst wall tanning and suppressing the development and reproduction of the SCN, which provides new ideas for the use of this gene as a target to control SCN.

A novel approach to fractional differential equations via JSN transform coupled fractional residual power series method

Abstract In this study, we introduce a novel modified general integral transform known as the JSN transform, which offers several advantages over the Laplace and other integral transforms with exponential kernels. Fundamental results of the JSN transform of the Caputo fractional derivative are discussed. Furthermore, we develop a novel hybrid technique called the JSN Fractional Residual Power Series Method (JSN FRPSM). This new technique incorporates the JSN transform with the existing Residual Power Series Method. To demonstrate the efficiency of the proposed hybrid technique in solving fractional differential equations, we apply it to various fractional differential equations encountered in science and engineering. Statistical and error analyses are conducted to validate the results obtained through the proposed method. Additionally, the series solutions obtained via the proposed method are illustrated graphically.

Construct, merge, solve and adapt

AbstractThe CMSA algorithm for combinatorial optimization is a hybrid technique based on repeatedly solving sub-instances to the original problem instance. The incumbent sub-instance is extended at each iteration by the probabilistic generation of valid solutions to the original problem instance and by adding the components found in these solutions to the sub-instance. In addition, the incumbent sub-instance is reduced at each iteration by removing seemingly useless solution components. In recent years the usefulness of the CMSA algorithm has been shown by a range of applications to different combinatorial optimization problems. In this work, we provide a gentle introduction to CMSA by describing the application to the so-called minimum global domination problem as an example.

Adaptive Dual-Layer Resource Allocation for Maximizing Spectral Efficiency in 5G Using Hybrid NOMA-RSMA Techniques

The unprecedented growth in data demands for 5G communication systems necessitates advanced techniques to maximize spectral efficiency while ensuring user fairness and low latency. This study proposes Adaptive Dual-Layer Resource Allocation (ADLRA), a novel hybrid technique combining Non-Orthogonal Multiple Access (NOMA) and Rate-Splitting Multiple Access (RSMA). The ADLRA framework introduces dynamic user pairing, hierarchical beamforming, and adaptive power and rate allocation strategies to optimize resource utilization. Key features include dynamic user pairing, leveraging machine learning algorithms for efficient group formation based on channel conditions, and hierarchical beamforming, which prioritizes high-priority users in the NOMA layer while effectively managing shared resources in the RSMA layer. Interference mitigation is achieved through spatial filtering and multi-user diversity techniques, ensuring minimal intra-cell and inter-cell interference. Simulation results demonstrate significant performance gains Spectral efficiency improved by 32%, compared to traditional NOMA. Latency reduced by 18%, ensuring seamless communication for ultra-reliable low-latency applications. Achieved a 94% fairness index, reflecting equitable resource allocation among users. Enhanced throughput, with an average gain of 28%, compared to RSMA-only systems. These results highlight the potential of ADLRA to meet the stringent requirements of next-generation 5G systems, offering a scalable and efficient solution for diverse communication scenarios. The proposed method sets a foundation for future hybrid access strategies in wireless communication networks.

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

The increase in global power demand has caused most of today's power networks to become overloaded especially in Sub-Saharan Africa. The increased load demand can be met through expansion of existing generation and transmission system. However, construction of new power infrastructure is limited by financing and technical constraints. Thus, power networks have been left to operate at overload conditions with high power losses and many power quality (PQ) problems. Flexible AC Transmission System (FACTS) devices can improve the power transfer capability of the existing transmission networks without the need of constructing new power infrastructure. In this paper, a multi-objective function comprising of minimization of power loss (PL), voltage deviation (VD) and operational cost (OC) was formulated and solved using a novel algorithm. A novel Genetic Algorithm-Improved Particle Swarm Optimization (GA-IPSO) technique is proposed in this paper for optimization of size and location of FACTS devices. Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC) are the three FACTS devices considered. The proposed technique was validated using IEEE-33 Bus Test System, which is a popular benchmark Radial Distribution System (RDS). The three FACTS devices were optimized separately and also in a combined manner. Under the separate optimization, the size and location of individual FACTS devices were optimized. For combined optimization, the sizes and locations of more than one device were optimized in the same test system. For separate optimization, UPFC produced the best results by reducing the active power losses by 38.44 % and OC from $1.59×105 to $ 1.15×105. Under the combined optimization, combination of TCSC, STATCOM and UPFC gave better results by achieving active power loss reduction of 56.09 % and reducing OC from $1.59×105 to $ 1.03×105. Comparison of GA-IPSO technique with other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Improved Grey Wolf Optimization (IGWO) and Differential Evolution Algorithm (DEA) showed that the proposed hybrid technique was superior and more efficient in solving the FACTS optimization problem.