Template-Type: ReDIF-Article 1.0 Author-Name: Acendino Neto Author-X-Name-First: Acendino Author-X-Name-Last: Neto Author-Name: Fernando Romero Author-X-Name-First: Fernando Author-X-Name-Last: Romero Author-Name: Daisy Damasceno Author-X-Name-First: Daisy Author-X-Name-Last: Damasceno Title: An exploratory study on CNC machine technologies, barriers and opportunities on adopting Industry 4.0: a review Abstract: Industry 4.0 represents a complete digital transformation in the way companies operate, incorporating advanced technologies. It includes or integrates new technological advances such as additive manufacturing, artificial intelligence, augmented reality, cyber-physical systems, blockchain, cybersecurity and other technologies. While Industry 4.0 and similar technologies offer many conceivable benefits for production, automated machines are essential for driving industries forward. This article proposes an exploratory investigation in the adoption of Industry 4.0 by CNC machine users, focusing on main barriers and opportunities, exploring the challenges faced by these companies and identifying the potential opportunities arising from adoption. It is based on an exploratory systematic review of the literature. The consolidated factors were grouped into categories to help understand the challenges faced by companies in the transition to Industry 4.0. This research identified several barriers for companies using CNC machines, while also highlighting numerous opportunities related to the adoption of Industry 4.0. Journal: Int. J. of Industrial and Systems Engineering Pages: 24-44 Issue: 1 Volume: 52 Year: 2026 Keywords: computer numerical control; CNC; technology; Industry 4.0; barriers; opportunities; review. File-URL: http://www.inderscience.com/link.php?id=151032 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:24-44 Template-Type: ReDIF-Article 1.0 Author-Name: Soumyanath Chatterjee Author-X-Name-First: Soumyanath Author-X-Name-Last: Chatterjee Title: Towards development of measurement index for supply chain sustainability Abstract: The subject of sustainability is gaining significance in the study of the supply chain. Sustainability is becoming very important in securing a better future for both the operation of the supply chain and the planet. This paper focuses on developing a supply chain sustainability measurement system by exploring fundamental properties of the supply chain sustainability index and devising methods to assess sustainability at various levels. To gain a comprehensive global perspective, the paper employs the multi-regional input-output model to analyse the supply chain's impact beyond its immediate geography. Ultimately, the paper proposes a standardised and generic supply chain sustainability index that can facilitate comparisons across different supply chains. Journal: Int. J. of Industrial and Systems Engineering Pages: 94-137 Issue: 1 Volume: 52 Year: 2026 Keywords: sustainability index; supply chain management; SCM; spatial performance measurement; multi-regional input-output; MRIO; life cycle assessment; LCA. File-URL: http://www.inderscience.com/link.php?id=151036 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:94-137 Template-Type: ReDIF-Article 1.0 Author-Name: Himani Arora Author-X-Name-First: Himani Author-X-Name-Last: Arora Author-Name: P.B. Narendra Kiran Author-X-Name-First: P.B. Narendra Author-X-Name-Last: Kiran Author-Name: Sunil Kumar Author-X-Name-First: Sunil Author-X-Name-Last: Kumar Title: The role of employee competencies in shaping organisational efficiency: perceptions from five-star hotels Abstract: In five-star hotels, achieving optimal organisational efficiency is paramount, with employee competence playing a pivotal role in effective task execution and service delivery. This study explores the significance of employee competencies within this context, rooted in the competency-based view theory. It examines both direct and indirect impacts of employee competency, with knowledge competency mediating and job competency moderating their influence on organisational efficiency. Utilising a sample of 400 employees from various hotel departments, the study reveals a substantial relationship between employee competencies and organisational efficiency. These findings not only validate the CBV theory but also offer practical implications for continuous employee development to enhance efficiency in five-star hotels. The research contributes to theoretical advancements and provides the perceptions for hotel managers and human resource professionals seeking to optimise employee competencies and drive organisational success in the competitive hospitality industry landscape. Journal: Int. J. of Industrial and Systems Engineering Pages: 138-156 Issue: 1 Volume: 52 Year: 2026 Keywords: organisational efficiency; employee competency; knowledge competency; job competency; five-star hotel. File-URL: http://www.inderscience.com/link.php?id=151037 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:138-156 Template-Type: ReDIF-Article 1.0 Author-Name: B. Srivani Author-X-Name-First: B. Author-X-Name-Last: Srivani Author-Name: N. Sandhya Author-X-Name-First: N. Author-X-Name-Last: Sandhya Author-Name: B. Padmaja Rani Author-X-Name-First: B. Padmaja Author-X-Name-Last: Rani Title: Big data stream classification in Apache Spark platform using adaptive dragonfly moth search algorithm Abstract: Big data streaming is done using two phases, offline and online, which is carried out based on the proposed optimisation algorithm, named adaptive-DMS algorithm. In the offline phase, the input text data is initially classified as sub-sets and provided as the input to individual slave nodes. In the slave nodes, the pre-processing is done to remove the unwanted data's present in the input using stop word removal and stemming. After pre-processing, TF-IDF is applied for extracting the best features and then classification is done. The same process is repeated for online phase. The error is determined based on the resulted features obtained from online phase and offline phase. If the error is maximal, the final classified data is determined by remodel the classifier by setting the boundary weights. Journal: Int. J. of Industrial and Systems Engineering Pages: 1-23 Issue: 1 Volume: 52 Year: 2026 Keywords: big data classification; Apache Spark; TF-IDF; stemming; stop word removal; deep RNN. File-URL: http://www.inderscience.com/link.php?id=151038 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:1-23 Template-Type: ReDIF-Article 1.0 Author-Name: Kamakshamma Vasepalli Author-X-Name-First: Kamakshamma Author-X-Name-Last: Vasepalli Author-Name: Bharati Kodangalkar Fakeerappa Author-X-Name-First: Bharati Kodangalkar Author-X-Name-Last: Fakeerappa Title: Student academic performance prediction with MapReduce using the optimised deep belief network Abstract: The emergence of learners count in the e-learning infrastructures has inspired researchers to carry out data-driven learning assessments for learning performance enhancement among students. A new technique, namely chicken squirrel search algorithm-based deep belief network (CSSA-DBN) is devised with MapReduce for predicting the student academic performance. Here, the details of students are attained, which performs pre-processing with log transformation to make it suitable for improved processing. Moreover, the mappers perform the selection of features with a correlation-based Tversky index wherein the Pearson correlation coefficient and Tversky index are integrated to choose imperative features. In addition, the reducers perform student performance prediction with a deep belief network (DBN), trained using the proposed chicken squirrel search algorithm (CSSA). The proposed CSSA is devised by blending chicken swarm optimisation (CSO) and squirrel search algorithm (SSA). Journal: Int. J. of Industrial and Systems Engineering Pages: 65-93 Issue: 1 Volume: 52 Year: 2026 Keywords: MapReduce; deep belief network; DBN; log transformation; student academic performance; Tversky index. File-URL: http://www.inderscience.com/link.php?id=151041 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:65-93 Template-Type: ReDIF-Article 1.0 Author-Name: Debjani Guha Author-X-Name-First: Debjani Author-X-Name-Last: Guha Author-Name: Kirti Nilesh Mahajan Author-X-Name-First: Kirti Nilesh Author-X-Name-Last: Mahajan Title: A descriptive study on impact of artificial intelligence determinants among the talent acquisition management in IT companies Abstract: This research underwent an empirical investigation to examine the application of AI in the talent acquisition process in software technology parks and IT hubs. The research was primarily focused on the following topics: 1) talent acquisition management; 2) AI support in pre-selection; 3) AI support in telephone interviews; 4) AI support in training and development; 5) AI support in organisational culture; 6) appropriate teams. This is a clear-cut policy that can be examined through this research effort since it plays a significant role. The proposed approach was implemented in two stages. The first step began with the collection of data, which explicitly examined the applications of artificial intelligence technology. The data was collected using a structured questionnaire comprised of 30 questions. The evaluation was carried out for the collected data using regression analysis. Journal: Int. J. of Industrial and Systems Engineering Pages: 45-64 Issue: 1 Volume: 52 Year: 2026 Keywords: artificial intelligence; talent acquisition; information technology; human resource manager; training and development. File-URL: http://www.inderscience.com/link.php?id=151042 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:1:p:45-64 Template-Type: ReDIF-Article 1.0 Author-Name: R. Ramya Author-X-Name-First: R. Author-X-Name-Last: Ramya Author-Name: T. Brindha Author-X-Name-First: T. Author-X-Name-Last: Brindha Title: Performance analysis on cluster head selection approaches for WSN-IoT Abstract: This paper presents an approach driven by numerous optimisation techniques for the selection of cluster heads (CHs) in WSNassisted IoT. The process starts with the simulation of IoT nodes during configuration. Moreover, this paper analyses and justifies various cluster head selection (CHS) techniques. Here, the comparative analysis is done by comparing the performance of several optimisation models developed for CHS. Also, the performances of the approaches are calculated with several measures, like energy, LLT, trust, QoS and throughput. Here, the experimentation was analysed by comparing approaches, like Glowworm swarm with FruitFly algorithm (FGF), fitness averaged-rider optimisation algorithm (FA-ROA), improved sunflower optimisation algorithm (ISFO), fuzzy-based energy-efficient CHS algorithm (FEECS), and particle-water wave optimisation (P-WWO) for CHS in WSN-IoT. The overall analysis states that the P-WWO model performed better than other models, with values of 0.927 for energy, 0.492 for LLT, 0.934 for trust, 0.796 for QoS, and 0.923 for throughput. Journal: Int. J. of Industrial and Systems Engineering Pages: 173-189 Issue: 2 Volume: 52 Year: 2026 Keywords: WSN; IoT; energy efficiency; trust; LLT; throughput. File-URL: http://www.inderscience.com/link.php?id=151662 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:173-189 Template-Type: ReDIF-Article 1.0 Author-Name: Sunil B. Bhoi Author-X-Name-First: Sunil B. Author-X-Name-Last: Bhoi Author-Name: Jayesh M. Dhodiya Author-X-Name-First: Jayesh M. Author-X-Name-Last: Dhodiya Title: Double parametric scheme-based multi-objective student project assignment problem by fuzzy programming technique with linear membership function Abstract: This paper presents the mathematical model of multi-objective student project allocation problem based on double parametric form of fuzzy preferences and its solution by fuzzy programming technique with linear membership function. Fuzzy preferences are utilised due to fuzzy nature of internal assessment of students by supervisors and feedback analysis of supervisors provided by students. Double parametric scheme is applied to transform this fuzzy model into crisp model and then crisp model is solved by fuzzy programming technique with linear membership functions for different values of <i>α</i> and <i>β</i>. The results are obtained using LINGO software. The model providing the efficient solutions which can be used by decision makers to allocate projects to students with a better degree of satisfaction for students and supervisors. To check the strength and efficiency of the proposed model, a numerical data-based case is studied and results are discussed. Journal: Int. J. of Industrial and Systems Engineering Pages: 234-252 Issue: 2 Volume: 52 Year: 2026 Keywords: student project assignment; multi-objective optimisation; 0-1 integer programming; double parametric scheme; fuzzy programmingor technique. File-URL: http://www.inderscience.com/link.php?id=151663 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:234-252 Template-Type: ReDIF-Article 1.0 Author-Name: Taquiuddin Z. Quazi Author-X-Name-First: Taquiuddin Z. Author-X-Name-Last: Quazi Author-Name: Vivek Sunnapwar Author-X-Name-First: Vivek Author-X-Name-Last: Sunnapwar Author-Name: Nilesh P. Ghongade Author-X-Name-First: Nilesh P. Author-X-Name-Last: Ghongade Title: Redefining energy sustainability by circularising coal: evaluating the challenges for coal-fired thermal power plants Abstract: Coal-fired thermal power plants must implement circular economy practices to overcome environmental issues associated with their emissions. There are various challenges in the path of the adoption of the circular economy concept. This study examines the challenges in the multi-criteria environment to explore the cause-and-effect relationships between them. Also, the challenges were ranked based on their significance. The findings of the study highlighted causal factors, namely 'regulatory difficulties', 'technological challenges', and 'energy efficiency' as the significant ones, whereas 'cultural shift' and 'socio-economic factors' were found to be dependent factors. The results also offer implications for academics, researchers, and practitioners. Adopting circularity concepts helps to reduce the negative impact on the ecosystem and creates jobs which addresses the social dimension of sustainability. Journal: Int. J. of Industrial and Systems Engineering Pages: 275-299 Issue: 2 Volume: 52 Year: 2026 Keywords: circular economy; coal-fired thermal power plant; sustainability; barriers; environmental performance. File-URL: http://www.inderscience.com/link.php?id=151664 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:275-299 Template-Type: ReDIF-Article 1.0 Author-Name: Monika Author-X-Name-First: Author-X-Name-Last: Monika Author-Name: Pratima Manhas Author-X-Name-First: Pratima Author-X-Name-Last: Manhas Title: Optimising vehicle safety: fuzzy logic-controlled automatic braking systems Abstract: The alarming rise in road accidents underscores the critical need for effective vehicle brake systems. Vehicles lacking such systems are highly susceptible to accidents, resulting in devastating consequences. Human errors during driving, including delayed reaction times and distractions, contribute significantly to these incidents. An automatic braking system serves as a vital solution to maintain vehicle stability, prevent wheel lock, and avert collisions with obstacles. This study aims to achieve several objectives: design an ultrasonic sensor-based obstacle detection model, create a model for an antilock braking system (ABS). Results from the simulations indicate a substantial 22 percentage improvement in braking torque. The stopping time is improved by 30% by using fuzzy controller in place of PI controller. Consequently, this improvement results in notably shorter stopping times and distances compared to standard PID control, signifying promising advancements in vehicle safety. Journal: Int. J. of Industrial and Systems Engineering Pages: 157-172 Issue: 2 Volume: 52 Year: 2026 Keywords: slip ratio; fuzzy logic; ultrasonic sensor; model; antilock braking system; ABS; wheel lock. File-URL: http://www.inderscience.com/link.php?id=151665 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:157-172 Template-Type: ReDIF-Article 1.0 Author-Name: Gaurav Kumar Bisen Author-X-Name-First: Gaurav Kumar Author-X-Name-Last: Bisen Author-Name: Sana Ashraf Author-X-Name-First: Sana Author-X-Name-Last: Ashraf Author-Name: Priya Yadav Author-X-Name-First: Priya Author-X-Name-Last: Yadav Author-Name: Vipin Vihari Ram Tripathi Author-X-Name-First: Vipin Vihari Ram Author-X-Name-Last: Tripathi Title: Exploring the digital marketing strategies that drive business performance: a focus on technological capabilities as a mediator Abstract: The global business paradigm has been greatly impacted by the emergence of digital marketing, which has supplanted traditional marketing. From a business-level viewpoint, the study provides a framework to describe how the change in digital marketing affects business performance in the 'new normal' setting. Additionally, it examines how technology skills function as a mediator in explaining company success. The cross-sectional study method was employed to collect data from 365 officials of SMEs in India. Following the application of the AMOS using SEM and the linear regression method through SPSS, it was found that online advertising, search engine marketing, and social media networking have a direct impact on business performance. Also, the outcomes indicate that technological capability is a mediator influencing the link between digital marketing and business performance. Finally, based on these findings, several interesting directions for further study as well as implications are proposed. Journal: Int. J. of Industrial and Systems Engineering Pages: 212-233 Issue: 2 Volume: 52 Year: 2026 Keywords: digital marketing; business performance; technology; social media marketing; business transformation. File-URL: http://www.inderscience.com/link.php?id=151666 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:212-233 Template-Type: ReDIF-Article 1.0 Author-Name: Gujanatti Rudrappa Author-X-Name-First: Gujanatti Author-X-Name-Last: Rudrappa Author-Name: Nataraj Vijapur Author-X-Name-First: Nataraj Author-X-Name-Last: Vijapur Title: Survey on the research of various machine learning and deep learning techniques for precipitation forecasting Abstract: A detailed survey is elaborated in this paper on precipitation forecasting for weather forecasting. The reviews are gathered from 50 research papers and the techniques are classified into three types, such as deep learning (DL), machine learning (ML) and other methodologies. The analysis uses the techniques adopted for precipitation forecasting, publication year, utilised tools, employed dataset, and evaluation metrics. From the analysis, it is proven that the DL-based technique is the highly utilised technique for data protection. The papers that were mostly obtained in 2023 were taken into account for this research. The techniques utilised in most of the research papers were evaluated based on the root mean square error (RMSE), and the most utilised dataset for developing the model in this survey paper is NOAA. Moreover, MATLAB is a frequently employed tool for implementation. Journal: Int. J. of Industrial and Systems Engineering Pages: 190-211 Issue: 2 Volume: 52 Year: 2026 Keywords: machine learning; deep learning; precipitation forecasting; recurrent neural networks; long short-term memory; LSTM. File-URL: http://www.inderscience.com/link.php?id=151667 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:190-211 Template-Type: ReDIF-Article 1.0 Author-Name: Chandan Raj Author-X-Name-First: Chandan Author-X-Name-Last: Raj Author-Name: Vivekanand Singh Author-X-Name-First: Vivekanand Author-X-Name-Last: Singh Title: Optimised NN-based flood prediction on river morphological changes in the Ganga River Abstract: Floods have become a geo-environmental hazard, which has turned out to be a disaster as it has a destructive effect on the economy and society. Nowadays, several flood prediction systems have evolved that consider the previous flood events, from which the upcoming flood events can be predicted. This work develops a new flood prediction model on river morphological changes, particularly in the Ganga River. The suggested design is divided into two main parts: 1) feature extraction; 2) classification. At first, the input is given for the feature extraction phase, where the vegetation index features and water index features are extracted. After this, the extracted vegetation and water index features are classified, where an optimised deep neural network (DNN) is deployed. Furthermore, the DNN weights are adjusted using the self-improved lion algorithm (SI-LA) to increase the created approach's accuracy. Journal: Int. J. of Industrial and Systems Engineering Pages: 253-274 Issue: 2 Volume: 52 Year: 2026 Keywords: flood prediction; river morphology; vegetation index; deep neural networks; DNN; self-improved lion algorithm; SI-LA. File-URL: http://www.inderscience.com/link.php?id=151668 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:2:p:253-274 Template-Type: ReDIF-Article 1.0 Author-Name: Mostafa Jafari Author-X-Name-First: Mostafa Author-X-Name-Last: Jafari Author-Name: Amir Hossein Akbari Author-X-Name-First: Amir Hossein Author-X-Name-Last: Akbari Title: Efficient algorithms for cellular manufacturing systems with deterioration effect and inventory (case study: stone paper factory) Abstract: This research investigated the order acceptance and scheduling problem in CMS with tardiness cost, the deterioration effects of machines, and the cost of ordering and holding raw materials. Received orders have revenue, processing time, due date, tardiness cost, and decisions about acceptance or rejection are made. We have proposed a linear programming mathematical modelling with objective profit maximisation. Due to the NP-hard nature of the problem, a meta-heuristic algorithm based on a genetic algorithm is introduced to solve large dimensions. The proposed model has been tested in four modes: CODH, COFH (removal of the deterioration effect of the machine), CODI (the removal of holding and ordering raw), and CSDH (removal of acceptance and scheduling of all orders). The results show the performance of the proposed algorithm. The authors' findings help managers to make better decisions, improve supply chain actions, enhance competitive advantage, enhance customer satisfaction and attract more customers. Journal: Int. J. of Industrial and Systems Engineering Pages: 345-383 Issue: 3 Volume: 52 Year: 2026 Keywords: cellular manufacturing system; CMS; order acceptance and scheduling; deterioration effect; meta-heuristic algorithm; holding and ordering; stone paper. File-URL: http://www.inderscience.com/link.php?id=152157 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:3:p:345-383 Template-Type: ReDIF-Article 1.0 Author-Name: Tabassum Rasul Author-X-Name-First: Tabassum Author-X-Name-Last: Rasul Author-Name: Koena Mukherjee Author-X-Name-First: Koena Author-X-Name-Last: Mukherjee Title: Modelling of disturbances and their influence on physical systems Abstract: The effect of disturbance is a common phenomenon in any physical system. Although the nature of the disturbance can vary from system to system, the effect remains the same and leads to errors in measurement as well as control of the system. Thus, to control a system effectively, sufficient study on disturbance modelling is required. The paper reviews pertinent literature on the effects of disturbances in various systems. Different signals are used to model disturbances in different systems. This paper explores and discusses these characteristic signals and equations used to describe disturbances in different systems. The study also includes a survey of the efficient control techniques offered in relevant literature to limit the effects of disturbances. Furthermore, the effectiveness of disturbance observers in disturbance attenuation is demonstrated via simulation using MATLAB/Simulink. Journal: Int. J. of Industrial and Systems Engineering Pages: 301-316 Issue: 3 Volume: 52 Year: 2026 Keywords: disturbance; continuous stirred tank reactor; CSTR; electric grid; hydro-servomotor; manipulator; disturbance observer; DOB. File-URL: http://www.inderscience.com/link.php?id=152158 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:3:p:301-316 Template-Type: ReDIF-Article 1.0 Author-Name: Mehrnaz Bathaee Author-X-Name-First: Mehrnaz Author-X-Name-Last: Bathaee Author-Name: Arash Apornak Author-X-Name-First: Arash Author-X-Name-Last: Apornak Author-Name: Mohammad Reza Pourhassan Author-X-Name-First: Mohammad Reza Author-X-Name-Last: Pourhassan Title: Designing a humanitarian supply chain network in dynamic conditions for transferring of relief items under demand uncertainty Abstract: One of the challenges in crisis situations after the incident is meeting the demands of the victims. The main purpose of this article is to minimise the amount of unmet demand based on the priority of demand points. Due to the computational complexity of the problem, which is NP-hard, a super-innovative algorithm called genetic algorithm was designed to solve the real-world problem in large scale (Kermanshah earthquake in Iran) and finally the efficiency of the model was evaluated through sensitivity analysis. In addition, the results obtained from the robust approach compared to the traditional approach showed that in the best and the worst scenario, the unmet demands in the robust approach compared to the traditional approach was respectively 43% and 21% less than the traditional approach, which indicates the efficiency of the robust approach compared to the traditional approach. Journal: Int. J. of Industrial and Systems Engineering Pages: 317-344 Issue: 3 Volume: 52 Year: 2026 Keywords: relief supply chain; uncertainty; robust optimisation; genetic algorithm. File-URL: http://www.inderscience.com/link.php?id=152159 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:3:p:317-344 Template-Type: ReDIF-Article 1.0 Author-Name: Senthilnathan Chidambaranathan Author-X-Name-First: Senthilnathan Author-X-Name-Last: Chidambaranathan Author-Name: Faizan Ahmad Author-X-Name-First: Faizan Author-X-Name-Last: Ahmad Author-Name: Vijayakumari Rodda Author-X-Name-First: Vijayakumari Author-X-Name-Last: Rodda Author-Name: Roopa Chandrika Rajappan Author-X-Name-First: Roopa Chandrika Author-X-Name-Last: Rajappan Author-Name: R. Venkateswaran Author-X-Name-First: R. Author-X-Name-Last: Venkateswaran Author-Name: Sangeetha Subramaniam Author-X-Name-First: Sangeetha Author-X-Name-Last: Subramaniam Title: Hybrid optimisation for early Parkinson's disease detection in federated learning Abstract: Recently, a rapidly developing neurodegenerative disorder is Parkinson's disease (PD) which commonly affects the population of elder persons of individuals over 50 years. There still has been no medication for PD. Despite that, in the early stages, detecting PD is challenging. Thus, earlier detection is required to maximise the life of patients. In this research, the major intention is to project FedL_WFSA_ResneXt-DNN for identifying PD detection. Two entities are involved such as nodes and servers, whereas the models that exist in FL are training and global models. By acquiring the input image from the given dataset, PD detection takes place inside the local training model and the input image is pre-processed using a weighted median filter. The essential features are extracted and then, PD detection is performed by ResNeXt-DNN and the hyperparameters tuning is conducted utilising the proposed WFSA. Journal: Int. J. of Industrial and Systems Engineering Pages: 384-415 Issue: 3 Volume: 52 Year: 2026 Keywords: weighted median filter; ResNeXT; waterwheel plant algorithm; WPA; flamingo search algorithm; FSA; deep neural network; DNN. File-URL: http://www.inderscience.com/link.php?id=152160 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:3:p:384-415 Template-Type: ReDIF-Article 1.0 Author-Name: Mrudula Owk Author-X-Name-First: Mrudula Author-X-Name-Last: Owk Author-Name: Gubbala Kumari Author-X-Name-First: Gubbala Author-X-Name-Last: Kumari Author-Name: Srividya Kotagiri Author-X-Name-First: Srividya Author-X-Name-Last: Kotagiri Author-Name: Mary Sowjanya Alamanda Author-X-Name-First: Mary Sowjanya Author-X-Name-Last: Alamanda Title: Hybrid optimisation enabled hierarchical deep learning for Facebook sentiment review analysis Abstract: Sentiment analysis is an important study nowadays due to sites of social networking, in which online users convey their thoughts, feelings and expressions freely on a particular topic. In this work, Facebook sentiment analysis is done by hierarchical deep learning for text (HDLTex). Here, Facebook reviews are first processed for bidirectional encoder representations from transformers (BERT) tokenisation. Also, many features, like negation, all caps, question marks, elongated units, hashtags, emoticons, number of words, and sentences in reviews, punctuation, a bag of units, and numerical words are extracted. Further, sentiment classification is carried out by HDLTex which is trained by the proposed Tasmanian devil driving training optimisation (TDDTO). Moreover, TDDTO is a combination of driving training optimisation (DTO) as well as Tasmanian devil optimisation (TDO). Furthermore, the performance of the proposed TDDTO_HDLTex provides an improved performance regarding the evaluation metrics like precision, recall, and F1-sore of 92.6%, 95.8%, and 94.2%. Journal: Int. J. of Industrial and Systems Engineering Pages: 416-446 Issue: 3 Volume: 52 Year: 2026 Keywords: driving training optimisation; DTO; hierarchical deep learning for text; HDLTex; bidirectional encoder representations from transformers; Tasmanian devil optimisation; TDO; Tasmanian devil driving training optimisation. File-URL: http://www.inderscience.com/link.php?id=152192 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijisen:v:52:y:2026:i:3:p:416-446