Browse Items (16488 total)
Sort by:
-
Multi-metal phytoremediation using Salvinia molesta: the role of EDDS and SDS in enhancing metal removal efficiency
Significant environmental damage to aquatic ecosystems is caused by heavy metals, and the situation necessitates strategies against the contaminants. The present study was intended to explore Salvinia molestas potential for the phytoremediation of contaminating water to remove three metals: chromium (Cr), nickel (Ni), and cadmium (Cd), with an emphasis on the influence of chemical amendments, ethylene diamine disuccinic acid (EDDS) and sodium dodecyl sulfate (SDS), applied independently. Plants were treated for a period of 60 days with single and combined metal solutions supplemented with EDDS (0.050.2%) and SDS (0.52%), and responses were measured through morphological factors and biochemical indicators, including bioaccumulation factor (BAF) with translocation factor (TF) used cautiously due to the floating habit of S. molesta. It was observed that S. molesta was capable of substantial heavy metal accumulation, with the highest accumulation recorded under EDDS amended and SDS amended treatments at elevated metal concentrations. EDDS treatments primarily enhanced metal bioavailability and uptake while maintaining plant growth and physiological stability under moderate metal stress, whereas SDS treatments, particularly at higher concentrations, resulted in increased metal accumulation accompanied by reductions in biomass, chlorophyll content and protein levels, indicating stress driven accumulation linked to altered membrane permeability. The application of EDDS or SDS resulted in higher metal uptake compared to untreated controls, with BAF values reaching 3.8 for Cr, 4.2 for Ni, and 3.5 for Cd; however, maximum accumulation under SDS treatments did not consistently correspond to biologically sustainable phytoremediation performance. Statistical analysis showed significant differences (p < 0.05) between treatments and control in metal bioavailability following amendment application, highlighting a dose-dependent tradeoff between metal uptake efficiency and plant health. This study represents the first integrated evaluation of EDDS and SDS under multi-metal (CrNiCd) conditions in S. molesta, addressing a major gap in chemical-assisted phytoremediation research. Future work should be aimed at determining the optimum concentrations of these chemical amendments to facilitate the scale-up of phytoremediation projects. 2026 Taylor & Francis Group, LLC. -
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024. -
Multi-objective ANT lion optimization algorithm based mutant test case selection for regression testing
The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing. 2021 Scientific Publishers. All rights reserved. -
Multi-objective Deep Reinforcement Learning Approach for Multiple -Input/Multiple-output Routing in WSN
The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Multi-Objective Optimization Approaches for Solar Photovoltaic Inverter Control and Energy Balance in A Smart Grid Environment
Placement of distributed generation in electrical distribution system is a critical newlineaspect of optimizing grid performance and ensuring effcient integration of renewable energy sources. Renewable based sources must be properly positioned and sized to avoid bidirectional power and#64258;ows, voltage/frequency and#64258;uctuations and performance degradation. Solar Photovoltaic Systems and Wind Turbines are potentially becoming the preferred renewable energy based, distribution generation sources. Precise control mechanisms like advanced inverter strategies and direct load control are crucial for regulating voltage, frequency and reactive power output, thereby optimizing grid operation and maximizing integration benefts from these sources. However, optimizing the allocation and operation of these systems in grid connected and islanded modes, particularly in radially confgured systems, requires addressing algorithmic challenges, problems related to nonlinear optimization, newlinevariable generations and load variations. To effectively allocate these systems in the newlineelectrical distribution system, advanced optimization techniques capable of newlinehandling multi-objective, nonlinear problems are needed. Similarly, optimizing the power factor of the distributed generation sources and optimizing the load factor in these systems demand adaptive algorithms that can manage nonlinear objectives and dynamic system conditions. In response to the above research questions, this study focuses on determining the optimal placement and sizing of the distributed generation sources in the electrical distribution system with the objective to minimize real power loss and improve voltage stability. Learning enthusiasm based teaching learning based optimization algorithm has been employed for location selection and sizing optimization. The effectiveness of the proposed approach is validated on standard IEEE 33-bus and newline69-bus test systems, demonstrating decreased distribution losses and improved voltage stability. -
Multi-Objective Reinforcement Learning With Physics-Aware Vehicle Dynamics for Safe and Efficient Adaptive Cruise Control
Adaptive Cruise Control (ACC) enhances safety and comfort in autonomous vehicles by maintaining appropriate inter-vehicular distance and speed regulation. Traditional ACC systems based on PID or Model Predictive Control (MPC) often struggle to handle complex, unforeseen traffic scenarios such as sudden braking, pedestrian crossings, or lane changes. Reinforcement Learning (RL) offers an adaptive alternative by enabling policy learning through environment interaction. However, existing RL-based ACC methods frequently suffer from poor smoothness and energy inefficiency under emergency conditions. This work proposes an enhanced RL-based ACC framework that integrates a physics-informed, multi-objective reward function to jointly optimize safety, ride comfort, and energy efficiency. The reward components are normalized and dynamically weighted based on the current driving context, allowing the agent to adaptively prioritize objectives. Vehicular dynamics are explicitly incorporated into the learning process to improve real-world applicability. The system is trained using the DDPG algorithm, which supports continuous control and stable policy convergence. Extensive MATLAB-based simulations were conducted across diverse urban driving scenarios including stopgo traffic, traffic signals, lane changes, and pedestrian interactions. Comparative analysis against PID and MPC-based ACC controllers demonstrates that the proposed framework achieves superior performance in maintaining safe inter-vehicular distance, reducing jerk, and improving energy efficiency. This study validates the feasibility of deploying a computationally efficient, model-free RL-based ACC for robust and safe autonomous driving in dynamic traffic environments. The Author(s), under exclusive licence to ITS Japan 2026. -
MULTI-REFERENCE SKIP-LOT SAMPLING OF TYPE 3 (MR-SkSP-3)
In the current industrial sector, the rate of defective products present in the lots has been decreasing and most of the products keeps up a good history of quality throughout the production also. Skip-lot sampling plans are the suitable acceptance sampling plan for the situations where the series of products shows a stable and excellent quality. The skip-lot sampling plans are still widely used because of its reduced sampling cost and efforts, because the plan only needs to inspect a fraction of the lots submitted after a continues series of lots with excellent quality. This approach makes the skip-lot plan more cost-effective than the other sampling plans, thus making it an economically important plan. The current study incorporated a modification on the skip-lot sampling of type 3 and designated it as multi-reference skip lot sampling of type 3. The proposed plan has the provision of having multiple reference plans in normal and skipping inspection of a skip-lot sampling plan, unlike the traditional skip-lot plans which has the same reference plan in all phases. The performance measures of the proposed plan are derived using the power series approach. A designing methodology to determine the optimal parameters for the plan using the unity value approach is also described with the help of a numerical illustration. Behaviour of the operating characteristic curves for varying set of parameters are also analysed for the plan. Comparison of the proposed plan is done between the conventional plans using performance measure values and graphical representations. This analysis shows that the new plan is able to effectively optimize the preferences of producer and consumer simultaneously, where the traditional plans fail to. The analysis is supported with the help of graphical representations and tabulated values. 2025, Gnedenko Forum. All rights reserved. -
Multi-stage fuzzy swarm intelligence for automatic hepatic lesion segmentation from CT scans
Segmentation of liver and hepatic lesions using computed tomography (CT) is a critical and challenging task for doctors to accurately identify liver abnormalities and to reduce the risk of liver surgery. This study proposed a novel dynamic approach to improve the fuzzy c-means (FCM) clustering algorithm for automatic localization and segmentation of liver and hepatic lesions from CT scans. More specifically, we developed a powerful optimization approach in terms of accuracy, speed, and optimal convergence based on fast-FCM, chaos theory, and bio-inspired ant lion optimizer (ALO), named (CALOFCM), for automatic liver and hepatic lesion segmentation. We employed ALO to guide the FCM to determine the optimal cluster centroids for segmentation processes. We used chaos theory to improve the performance of ALO in terms of convergence speed and local minima avoidance. In addition, chaos theory-based ALO prevented the FCM from getting stuck in local minima and increased computational performance, thus increasing stability, reducing sensitivity in the iterative process, and allowing the best centroids to be used by FCM. We validated the proposed approach on a group of patients with abdominal liver CT images, and the results showed good detection and segmentation performance compared with other popular techniques. This new hybrid approach allowed for the clinical diagnosis of hepatic lesions earlier and more systematically, thereby helping medical experts in their decision-making. 2020 Elsevier B.V. -
Multi-stage spatial temporal ensemble model with integrated learning methods for robust deepfake detection
Deepfake detection remains a significant challenge as modern generative models increasingly minimize visible artefacts, and many existing approaches rely solely on either spatial or temporal cues, which limits their robustness and generalization. Many existing hybrid approaches integrate mature learning models in linear or stacked pipelines, which often suffer from error propagation, reduced interpretability, and suboptimal generalization. Unlike prior hybrid approaches that primarily stack spatialtemporal learners, the proposed multi-stage hybrid Integrated Learning Method (ILM) introduces a validation-aware dual-detection mechanism, an independent dual-path spatial-temporal learning design, and a decision-level nonlinear ensemble fusion strategy, explicitly mitigating face mislocalization, temporal dilution, and false-positive propagation observed in existing deepfake detection pipelines. The ILM framework structurally coordinates facial region localization and validation using YOLOv5 and Haar Cascade, deep spatial feature extraction using ResNet-50, frame-level spatial classification via LightGBM, and temporal sequence modeling using LSTM networks. The outputs from the spatial and temporal pathways are subsequently fused using a Random Forest classifier, enabling nonlinear aggregation of complementary evidence while preserving interpretability. Experimental results on the FaceForensics + + and Celeb-DF (v2) benchmark datasets show that ILM achieves 98.30% accuracy, 97.90% precision, and 98.70% recall, outperforming recent state-of-the-art CNNLSTM, ViT-based, and CNNTransformer models by 16%. Ablation studies confirm that each module contributes incrementally to performance stability and false-positive reduction, demonstrating the importance of ILMs multi-stage architecture rather than the individual algorithms alone. Overall, ILM provides a modular, accurate, and computationally efficient solution suitable for deployment in digital forensics, media authentication, and AI governance. Future work will extend ILM with transformer-based global encoders and explainable AI techniques to further improve interpretability and robustness against emerging deepfake models. The Author(s) 2026. -
Multi-variate LSTM with attention mechanism for the Indian stock market
The advent of attention mechanism has surpassed numerous benchmarks and enabled widespread progress in the realm of natural language processing (NLP). Nevertheless, they have not been adequately leveraged in a time-series context. Accordingly, this paper aims to address this issue by proposing a hybrid, deep-learning model that integrates attention mechanisms and multi-variate long short-term memory (LSTM) for financial forecasting in the Indian stock market. Our model yields superior results as compared to baseline and state-of-the-art models evaluated using MAE and RMSE. Moreover, we employed a modern evaluation criterion based on the methodology advocated by DieboldMariano, known as the DieboldMariano test (DM test), as a new criterion for evaluation based on statistical hypothesis tests. DM test has been applied in this study to distinguish the significant differences in forecasting accuracy between LSTM with attention and other models. From the results and according to DM-test it is observed that the differences between the forecasting performances of models are significant and that attention mechanism could enhance the accuracy in predicting stock prices by allowing the model to prioritize and concentrate on the most important features and patterns in the data while avoiding overfitting and noise. 2025 The Author(s) -
Multi-view video summarization
Video summarization is the most important video content service which gives us a short and condensed representation of the whole video content. It also ensures the browsing, mining, and storage of the original videos. The multi- view video summaries will produce only the most vital events with more detailed information than those of less salient ones. As such, it allows the interface user to get only the important information or the video from different perspectives of the multi-view videos without watching the whole video. In our research paper, we are focusing on a series of approaches to summarize the video content and to get a compact and succinct visual summary that encapsulates the key components of the video. Its main advantage is that the video summarization can turn numbers of hours long video into a short summary that an individual viewer can see in just few seconds. Springer India 2016. -
Multicomponent Synthesis Strategies, Catalytic Activities, and Potential Therapeutic Applications of Pyranocoumarins: A Comprehensive Review
Fused coumarins, because of their remarkable biological and therapeutic properties, particularly pyranocoumarins, have caught the interest of synthetic organic chemists, leading to the development of more efficient and environmentally friendly protocols for synthesizing pyranocoumarin derivatives. These compounds are the most promising heterocycles discovered in both natural and synthetic sources, with anti-inflammatory, anti-HIV, antitubercular, antihyperglycemic, and antibacterial properties. This review employed the leading scientific databases Scopus, Web of Science, Google Scholar, and PubMed up to the end of 2022, as well as the combining terms pyranocoumarins, synthesis, isolation, structural elucidation, and biological activity. Among the catalysts employed, acidic magnetic nanocatalysts, transition metal catalysts, and carbon-based catalysts have all demonstrated improved reaction yields and facilitated reactions under milder conditions. Herein, the present review discusses the various multicomponent synthetic strategies for pyranocoumarins catalyzed by transition metal-based catalysts, transition metal-based nanocatalysts, transition metal-free catalysts, carbon-based nanocatalysts, and their potential pharmacological activities. 2023 The Authors. Chemistry & Biodiversity published by Wiley-VHCA AG, Zurich, Switzerland. -
Multidisciplinary Perspectives in Social Cognition: Recent Advances and Future Questions
This book introduces social cognition from multidisciplinary perspectives, exploring in detail how individuals perceive, interpret, and respond to social stimuli throughout different stages of life. The book is organized into five parts, and each chapter provides a concise introduction to a specific topic within social cognition, followed by an in-depth exploration of prominent areas of research, the impact of technology, and potential future questions. The book covers core principles of social cognition alongside application in clinical interventions, with topics including how social cognition evolves throughout the lifespan, the impact of technology and social media on social cognition, and how conditions like autism, schizophrenia, and dementia affect social processing. It links social cognition with critical cognitive domains, including attention, memory, and decision-making, highlighting how these processes interact in shaping social behaviour. The book also combines cross-cultural research with relevant studies to show the differences and similarities in social cognition across geographical distances. Each part concludes with guided questions and answers for students to test their understanding, knowledge, and learning outcomes. By synthesizing current research and identifying future directions, this book offers valuable insights and potential solutions to the complex challenges in the field of social cognition. Bringing together foundational concepts with advances in the field, the book is ideal reading for students of social psychology, cognitive psychology, social neuroscience, and neuropsychology. It will also be of interest to students and researchers of clinical psychology, providing a foundation for future research in applied settings. 2025 K. Jayasankara Reddy and Bhasker Malu. -
Multidrug Resistant Bacteria: The Fatal Menace in Healthcare
Mapana Journal of Sciences, Vol-11 (1), pp. 31-47. ISSN-0975-3303 -
Multifaceted Anticancer Potential of Trigonella foenum-graecum
The past decade saw a revolution in the discovery of genetic and epigenetic factors paving way for various types of cancers. With a better understanding of the causes, comes a chance of wider possibility of targeting the root causes of cancer. Nature is a storehouse of natural anticancer molecules, many yet to be explored. Trigonella foenumgraecum (fenugreek) is one such plant, having a huge potential for modulating prophylactic and therapeutic aspects of cancer. Cuisines world over make uses of this legume in multiple ways. This small herb has been found to be loaded with many secondary metabolites like diosgenin, coumarin, trigonelline and so on that reduce inflammation, promote apoptosis, act as antioxidants, regulates cell proliferation, etc., thereby reducing the effects of various hallmarks of cancer. Components of T. foenum-graecum extracts have been found to be effective in alleviating both solid tumours and blood cancers. The milieu of phytochemicals present in T. foenum-graecum has already been shown to have antimicrobial, antioxidant and neuroprotective properties by several studies done in different parts of the world. The current chapter attempts to have a comprehensive look at the potential of various bioactive principles in Trigonella foenum-graecum to be used for the prevention and treatment of cancer. In today's global scenario, where cancer incidences are alarmingly rising, such natural remedies would indeed go a long way in preventing various types of cancer and imparting a better quality of life. 2021 Nova Science Publishers, Inc. -
Multifaceted Destination Personality Traits: A Short Communication on Understanding from Tourists Perspective
This short communication is an extract from a major research work on destination branding, and this cull out of analysis focused on the multifaceted destination personality traits that the destinations possess and perhaps how such perceptions of tourists differ based on the selected personal factors. Though there are many studies in the destination branding literature, the evidence regarding the personality traits is still at the stage of progression, and approaches referring to multifaceted personality traits are unseen. After the pilot testing, a structured questionnaire was floated to 400 tourists who visited the selected destinations a district in Tamil Nadu, India, between June 2019 and February 2020, where 327 responses were finalized. The questionnaire had statements measuring the destinations personality traits and other questions on tourists characteristics. Combined mean calculation and multivariate results revealed that two personality traits, welcoming and friendly, were emphasized by the tourists and perceived in common. Also, personality traits such as spiritual and charming were found to be commonly perceived. The mean values also indicated the existence of multifaceted destination personality traits some inherent and some perceived. Marketers and others thereof have been recommended on the branding and advertising strategies based on the outcome of this communication. The limitations and scope of this research have been indicated. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Multifarious pigment producing fungi of Western Ghats and their potential
Concerns about the negative impacts of synthetic colorants on both con-sumers and the environment have sparked a surge of interest in natural col-orants. This has boosted the global demand for natural colorants in the food, cosmetics and textile industries. Pigments and colorants derived from plants and microorganisms are currently the principal sources used by mod-ern industry. When compared to the hazardous effects of synthetic dyes on human health, natural colors are quickly degradable and have no negative consequences. In fact, fungal pigments have multidimensional bioactivity spectra too. Western Ghats, a biodiversity hotspot has a lot of unique eco-logical niches known to harbor potential endophytic pigment-producing fungi having enumerable industrial and medical applications. Most of the fungi have coevolved with the plants in a geographical niche and hence the endophytic associations can be thought to bring about many mutually ben-eficial traits. The current review aims to highlight the potential of fungal pigments found in the Western ghats of India depicting various methods of isolation and screening, pigment extraction and uses. There is an urgent need for bioprospecting for the identification and characterization of ex-tremophilic endophytic fungi to meet industry demands and attain sustain-ability and balance in nature, especially from geographic hotspots like the Western Ghats. 2022 Horizon e-Publishing Group. All rights reserved. -
Multifarious Potential of Biopolymer-Producing Bacillus subtilis NJ14 for Plant Growth Promotion and Stress Tolerance in Solanum lycopercicum L. and Cicer arietinum L: A Way Toward Sustainable Agriculture
Diverse practices implementing biopolymer-producing bacteria have been examined in various domains lately. PHAs are among the major biopolymers whose relevance of PHA-producing bacteria in the field of crop improvement is one of the radical unexplored aspects in the field of agriculture. Prolonging shelf life is one serious issue hindering the establishment of biofertilizers. Studies support that PHA can help bacteria survive stressed conditions by providing energy. Therefore, PHA-producing bacteria with Plant Growth-Promoting ability can alter the existing problem of short shelf life in biofertilizers. In the present study, Bacillus subtilis NJ14 was isolated from the soil. It was explored to understand the ability of the strain to produce PHA and augment growth in Solanum lycopersicum and Cicer arietinum. NJ14 strain improved the root and shoot length of both plants significantly. The root and shoot length of S. lycopersicum was increased by 3.49 and 0.41cm, respectively. Similarly, C. arietinum showed a 9.55 and 8.24cm increase in root and shoot length, respectively. The strain also exhibited halotolerant activity (up to 10%), metal tolerance to lead (up to 1000?g/mL) and mercury (up to 100?g/mL), indicating that the NJ14 strain can be an ideal candidate for a potent biofertilizer. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Multifractal analysis of volatility for detection of herding and bubble: Evidence from CNX Nifty HFT
This study delves into the herding and bubble detection in the volatility domain of a capital market underlying. Furthermore, it focuses on creating heuristics, so that common investors find it relatively easy to understand the state of the market volatility. Hence, it can be termed that this study is focused on the specific financial innovation regarding bubble and herding detection coupled with investor awareness. The traces of possible volatility bubble emerge when it is positioned against its own lags (both lag1 and lag2). The volatility trigger indicated clear traces of herding and an embedded parabola function. Continuous and repetitive parabola function hinted at a subtle presence of "fractals". Firstly, the detrended fluctuation analysis has been used with its multifractal variant. Secondly, the regularized form of Hurst calculation and analysis have been used. Both tests reveal the traces of nascent bubble formation owing to prominent herding in CNX Nifty HFT environment. They also indicate a clear link with Hausdorff topological patterns. These patterns would help to create heuristics, enabling investors to be aware of possible bubble and herd situations. Bikramaditya Ghosh, Emira Kozarevic, 2019. -
Multifunctional biosensor activities in food technology, microbes and toxins A systematic mini review
Biosensors have its significant applications in various fields, its use in food processing, food safety and food technology has helped to enhance the overall health of the society as it can successfully determine the presence and concentration of different microorganisms including Escheichia coli, Vibrio cholera, Clostridium spp. etc., and also determination of various toxins present in food like acrylamides, benzene, ethylbenzene, toluene, xylene, nitrosamines, Benzo[a]pyrene (BaP) which are carcinogenic. The preface of biosensors has assisted food industries for monitoring and verification of raw materials, food processing, and composition of the food and assessment of product freshness. Symbolic biosensors have been developed in recent years and yet there is much immediate need for the development of more reliable, cost-effective, sensitive and novel biosensors for rapid detection and identification of food borne pathogens and toxins. Extensive review recapitulates overall food-pathogen testing research market trends, as well as commercialization of biosensors for the food safety industry as legislation creates novel standards for microbial monitoring. Furthermore, the world's concern about the food safety and human's healthcare, the one and only biosensor's exclusive demand is nothing but an alternative in real time diagnosis of diseases causing pathogens. 2022 Elsevier Ltd