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Multi-dimensional changes in drought patterns across India
Indias hydroclimatic systems are undergoing unprecedented transitions in a warming climate, marked by shifts in temperature extremes, altered precipitation patterns, and increasing drought risk. This study presents a comprehensive assessment of drought trends and hydroclimatic variability across six major geographical zonesWestern, Central, Himalayan, Indo-Gangetic Plain (IGP), Peninsular, and Northeast Indiaduring the period 1971 to 2020. Using a set of advanced climate change metricsStandardized Local Anomalies (SLA), Novel Climate Scores (NCS), and changes in probability of local climate extremes alongside the Standardized Precipitation Evapotranspiration Index (SPEI), we quantify changes in drought conditions and the emergence of non-analogue climates. Changes in climatic extreme are computed using high-resolution daily gridded temperature and rainfall datasets, comparing recent decades against a 19511980 baseline. SLA quantifies deviations from historical variability, highlighting intensified warming over the Indo-Gangetic Plain, western India, and the southern peninsula. NCS reveales the emergence of novel climatescombinations of temperature and precipitation conditions not previously observed, particularly in Southeast India and the Himalayan region. The probability of local climate extremes shows a substantial increase in extreme events across India indicating enhanced climate volatility. These metrics are then integrated with drought analysis using SPEI to incorporate both precipitation and temperature-driven evaporative demand. SPEI trends indicate increasing dryness in Northeast India, the Himalayas, and the Indo-Gangetic Plain, linked to declining monsoonal rainfall and rising temperatures. Meanwhile, Western and Peninsular regions show wetting trends, driven by increased rainfall and convective precipitation events. The rainfall is the dominant drought driver during the monsoon, while high maximum temperatures intensify drought conditions in pre- and post-monsoon seasons by enhancing evaporative demand. Minimum temperature exhibits regional effects, showing a drying influence in the IGP and Himalayas, but a slight moistening signal in Peninsular India. By combining drought indices with climatic extremes metrics, this study offers a comprehensive framework to monitor hydroclimatic shifts and their regional impacts. The findings underscore the need for region-specific adaptation strategies that incorporate early warning systems, sustainable water management, and climate-resilient agriculture to address Indias evolving drought risks. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Multi-dynamics and emission tailored fluoroperovskite-based down-conversion phosphors for enhancing the current density and stability of the perovskite solar cells
State-of-the-art and innovative research is being intensively employed on perovskite solar cells (PSCs) to expand their frontiers further. This study is a successful attempt to drive the limit of photocurrent density (Jsc) beyond conventional PSCs (which typically utilize the visible spectrum alone) through a nonlinear optical phenomenon called down-conversion (DC). The use of DC luminescence to harness the UV region from the solar spectrum is explored by utilizing Eu3+ activated RbCaF3, a fluoroperovskite-based phosphor material. It is observed that PSCs, which used RbCaF3:Eu3+ incorporated TiO2 electron transport layer (ETL), enhanced their Jsc and UV stability compared to those with pristine TiO2-oriented ETL. Such improvement in the aforementioned devices is due to the result of converting high-energy UV photons to effectively absorbable low-energy visible photons for perovskite absorbers. Overall, the DC-aided PSC offered a substantial Jsc of 23.54 mA cm?2 (9.2% superior to the conventional PSC) and boosted its power conversion efficiency (PCE) from 11.2% to 13.3%. It is evident that DC-based PSCs show a much better shelf-life when compared to conventional PSCs. This unique approach for boosting the Jsc with enhanced stability can be utilized for the potential applications of PSCs. 2023 The Royal Society of Chemistry. -
Multi-frame twin-channel descriptor for person re-identification in real-time surveillance videos
Automatic re-identification of people entering the camera network is an important and challenging task. Multiple frames of the same person will be easily available in surveillance videos for re-identification. Dealing with pose variations of the person in the image and partial occlusion issues is major challenge in single-frame re-identification process. The use of more frames from the surveillance videos can generate robust descriptor to tackle issues of pose variations and occlusion. In this paper, we have emphasized on using multiple frames from the same video to generate a multi-frame twin-channel descriptor. The work deals with building a spatial-temporal descriptor which takes advantage of the twin paths to extract features of the person image. Mahalanobis distance metric learning algorithms is used for matching and evaluation. Our descriptor is evaluated on two benchmark datasets and found to surpass the performance of the existing methods. 2017, Springer-Verlag London Ltd. -
Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation in Cloud-SDN Environment
Cloud computing (CC) remains as a promising environment which offers scalable and cost effectual computing facilities. The combination of the SDN technique with the CC platform simplifies the complexities of cloud networking and considerably enhances the scalability, manageability, programmability, and dynamism of the cloud. This study introduces a novel Multi-Layer Ensemble Deep Reinforcement Learning based DDoS Attack Detection and Mitigation (MEDR-DDoSAD) technique in Cloud-SDN Environment. The major aim of the presented technique lies in the recognition of DDoS attacks from the cloud-SDN platform. The MEDR-DDoSAD technique transforms the input data into images and the features are derived via deep convolutional neural network based Xception model. 2022 IEEE. -
Multi-layer Stacking-based Emotion Recognition using Data Fusion Strategy
Electroencephalography (EEG), or brain waves, is a commonly utilized bio signal in emotion detection because it has been discovered that the data recorded from the brain seems to have a connection between motions and physiological effects. This paper is based on the feature selection strategy by using the data fusion technique from the same source of EEG Brainwave Dataset for Classification. The multi-layer Stacking Classifier with two different layers of machine learning techniques was introduced in this approach to concurrently learn the feature and distinguish the emotion of pure EEG signals states in positive, neutral and negative states. First layer of stacking includes the support vector classifier and Random Forest, and the second layer of stacking includes multilayer perceptron and Nu-support vector classifiers. Features are selected based on a Linear Regression based correlation coefficient (LR-CC) score with a different range like n1, n2,n3,n4 a, for d1 used n1 and n2 dataset,for d2 dataset, combined dataset of n3 and n4 are used and developed a new dataset d3 which is the combination of d1 and d2 by using the feature selection strategy which results in 997 features out of 2548 features of the EEG Brainwave dataset with a classification accuracy of emotion recognition 98.75%, which is comparable to many state-of-the-art techniques. It has been established some scientific groundwork for using data fusion strategy in emotion recognition. 2022. International Journal of Advanced Computer Science and Applications. All Rights Reserved. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Multi-lingual Spam SMS detection using a hybrid deep learning technique
Nowadays, the incremental usage of mobile phones has made spam SMS messages a big concern. Sending malicious links through spam messages harms our mobile devices physically, and the attacker might have a chance to steal sensitive information from our devices. Various state-of-the-art research works have been proposed for SMS spam detection using feature-based, machine, and deep learning techniques. These approaches have specific limitations, such as extracting and selecting signifi-cant and quality features for efficient classification. Very few deep learning techniques are only used for classifying spam detection. Moreover, the benchmark spam datasets written in English are mostly used for evaluation. Very few papers have detected spam messages for other languages. Hence, this paper creates a multilingual SMS spam dataset and proposes a hybrid deep learning technique that combines the Convolutional Neural Network and Long Short-Term Memory (LSTM) model to classify the message dataset. The performance of this proposed hybrid model has been compared with the baseline deep learning models using accuracy, precision, recall, and F1-score metrics. 2022 IEEE. -
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 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.