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Amyotrophic Lateral Sclerosis: Insights and New Prospects in Disease Pathophysiology, Biomarkers and Therapies
Amyotrophic Lateral Sclerosis (ALS) is a severe neurodegenerative disorder marked by the gradual loss of motor neurons, leading to significant disability and eventual death. Despite ongoing research, there are still limited treatment options, underscoring the need for a deeper understanding of the diseases complex mechanisms and the identification of new therapeutic targets. This review provides a thorough examination of ALS, covering its epidemiology, pathology, and clinical features. It investigates the key molecular mechanisms, such as protein aggregation, neuroinflammation, oxidative stress, and excitotoxicity that contribute to motor neuron degeneration. The role of biomarkers is highlighted for their importance in early diagnosis and disease monitoring. Additionally, the review explores emerging therapeutic approaches, including inhibitors of protein aggregation, neuroinflammation modulators, antioxidant therapies, gene therapy, and stem cell-based treatments. The advantages and challenges of these strategies are discussed, with an emphasis on the potential for precision medicine to tailor treatments to individual patient needs. Overall, this review aims to provide a comprehensive overview of the current state of ALS research and suggest future directions for developing effective therapies. 2024 by the authors. -
An Abstractive Text Summarization Using Decoder Attention with Pointer Network
Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods. 2023 IEEE. -
An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network
Sentiment analysis is one of the most widely used methods for forecasting stock market action from consumer feedback. Most of the methods associated with sentiment analysis are limited due to low accuracy and enhanced error rate. This is addressed by proposing a synergic squeeze deep info convolutional neural network-advanced variable capsule equilibrium auto encoder (SSDCNN-AVCEAE) for sentiment analysis and accurate multiple data-based stock prediction. Stock market data from NSE Nifty 50 (Mar 2, 2020May 10, 2021) and real-time twitter sentiment analysis are pre-processed through data cleaning and sentiment analyzer lexicon processes. Merging features using SSDCNN, optimized with random search algorithm, mitigates overfitting. SSDCNN eliminates redundant features. Selected features undergo classification by AVCEAE, a fusion of advanced capsule auto encoder (ACAE) and variable equilibrium optimization algorithm, enhancing prediction accuracy for rising or falling stock market movements while minimizing errors. Variable equilibrium optimization refines ACAE parameters. The proposed framework demonstrates exceptional performance with F1-Score, accuracy, false alarm rate, sensitivity, precision, specificity, and error rate reaching 98%, 99%, 0.1%, 99%, 99%, and 0.2%, respectively. The measurements highlight the model's ability to handle a variety of issues, making it a reliable option for precise stock prediction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
An Adaptive Cluster based Vehicular Routing Protocol for Secure Communication
In todays scenario, Vehicular Ad-hoc Network (VANET) is one of the modern fields in vehicle communication; it includes a large number of nodes that can be changed arbitrarily with the ability to link or exit the system anytime. Moreover, it has various complexities because of the attacks model in the transmission and communication channel. Besides, most of the attacks are known as black hole attack and wormhole attack. The presence of these attacks causes large damage in the data broadcasting region that ends in data drops or collapses. To defeat these problems, a novel Clustered Vehicle Location protocol for Hybrid Krill Herd and Bat Optimization (CVL-HKH-BO) technique is proposed. Thus, the proposed mechanism of hybrid krill herd and bat optimization is to detect and prevent attacks based on the fitness function. Moreover, secure communication can be enhanced by the proposed technique. Consequently, the solution to energy consumption and packet delay issues are solved using the CVL protocol. The projected strategy is implemented in the Network simulator (Ns-2) platform, and the outcomes show the node energy, overload and delay are minimized by increasing the quantity of packets transmitted in the network. Sequentially, the proposed technique is compared with existing techniques in terms of throughput, packet loss, delay time and data broadcasting ratio. Therefore, the duration of the node can be enhanced and can attain high energy capable data transmission. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
AN ADAPTIVE HYBRID SCHEDULING APPROACH FOR SUSTAINABLE AND RELIABLE CLOUD SERVICES
The modern cloud computing systems have to plan the heterogeneous workloads and balance performance effectiveness, service availability, and sustainability. In this study, an adaptive hybrid scheduling framework is developed Adaptive Ant-guided Min-Max (AAMM) combining ant-guided optimization with dynamic Min-Min and Max-Min in deciding how to allocate cloud tasks as a multi-objective. The scheduler jointly evaluates task completion time, the likelihood of Service Level Agreement violations, energy consumption, and monetary cost within a unified scoring framework, enabling informed trade-offs among competing objectives. AAMM is assessed based on a real disaggregated Deep Learning Recommendation Model workload of 1,544 heterogeneous tasks, running on heterogeneous virtual machines. Comparative experiments are done with Min-Min, Max-Min and ACO-guided Min-Min scheduling strategies. According to experimental findings, the suggested approach has been very effective in reducing energy per task, cost per task, SLA violations are significantly lowered, and flow time stability is enhanced. Though moderate growth in the makespan is witnessed, the accompanying trade-off has created equal distribution of resources and service reliability. 2026 Academy and Industry Research Collaboration Center (AIRCC). All rights reserved. -
An adaptive inertia weight teachinglearning-based optimization for optimal energy balance in microgrid considering islanded conditions
The energy balance in islanded microgrids is a complex task due to various operational constraints. This paper proposes a new approach to multi-objective optimization for achieving energy balance in aMicrogrid(MG) in both islanded and normal modes. Optimal load control (OLC)is achallenge, due to a lack of capacity to generate the global optimum after each run. The latest variant of Teaching Learning Based Optimization (TLBO), known as Adaptive-TLBO, includes both modifications during exploitation and exploration stages (ATLBO). The results achievedwith the proposed method are exceptional on a modified IEEE 33-bus system. In addition to the improvement of the voltage profile and the decrease of the distribution losses, the energy balance improves with the method. The proposed ATLBO algorithm overrides any proposed other algorithm, as shown by comparison with PSO, base TLBO, Backtrackingsearch algorithm (BSA) and cuckoo search algorithms, etc. (CSA). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. -
An Adaptive Scriptless Behavior-Driven Development Automation Framework with Self-Healing Intelligence for Evolving Software Applications
Background: The high rate of user interface (UI) and source code changes in contemporary software development resulted in automated testing failures that augmented maintenance expenses and decreased the usefulness of automated testing. The current tools need regular updating by manual means, which is ineffective and expensive. Purpose: To present the Adaptive Scriptless Behavior-Driven Development (BDD) Automation Framework with Self-Healing Intelligence, which is an artificial intelligence (AI) and machine learning (ML)-driven framework of automatic test failure detection and resolution based on UI drift, broken locators, or timing. Approaches: The framework uses dynamic locator approaches, adaptive test generation, and reinforcement learning to allow updating test scripts in response to application changes. Such a self-healing feature will minimize human intervention and reduce maintenance expenses. An experimental case study was conducted in order to assess the performance of the framework in a practical context. Findings: The framework demonstrated significant advances in automated testing, such as a 30% drop in maintenance speed, reduced number of resources to update tests, a 25 % reduction in total cost of testing since less manual effort is needed, and a 40 % rise in stability of the test suites, which can execute its tests more reliably and with greater accuracy despite the presence of changes to the application. Conclusions: The Adaptive Scriptless BDD Automation Framework with Self-Healing Intelligence goes a long way to improving the flexibility, scalability, and efficiency of automated testing. It enhances the speed of testing, saves costs, and adds confidence in the quality of software, and is therefore valuable for ensuring high-quality standards in dynamic software landscapes. 2026, Innovative Information Science and Technology Research Group. All rights reserved. -
An adept approach to ascertain and elude probable social bots attacks on twitter and twitch employing machine learning approach
There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents: A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection. And Harris Hawk optimization with Bi-LSTM for social bot prediction. Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset. 2023 -
An Adoptable Multi-Criteria Decision-Making Analysis to Select a Best Hair Mask Product-Extended Weighted Aggregated Sum Product Assessment Method
Hair masks (HMs) act as one of the solutions for most of the hair problems like dandruff, frizziness, breakage, premature- greying and so on. Due to its various benefits, HM products are acquiring more popularity among the individuals. As there are different varieties of HM products available in the market, the confusion arises in choosing a HM which suits the individuals hair profile and causes less side effects. Here, we have employed multi-criteria decision-making (MCDM) combined with fuzzy set theory to obtain better results. We used the extended Weighted Aggregated Sum Product Assessment (WASPAS) method based on trapezoidal interval type-2 fuzzy set (TIT2FS) in this research paper to handle vagueness and complexity in real-world problems. For determining the objective weights of the criteria, we used the entropy method of weight finding. An example of selecting a hair mask product (HMP) among four alternatives based on five criteria is provided to illustrate the applicability of the proposed method. In comparison to other MCDM methods, the approach yielded more practical results. By doing a sensitive study, the methods stability is also assessed. 2021, The Author(s). -
An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. The task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. The main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
An advanced variable temperature refrigerator for preservation and management of food items
All food items will have shelf life period. The main aim of food preservation is to maximize the shelf life period and preservation of nutrients for a long period. One of the preservation methods is refrigeration. Each food item will have its own optimum storage temperature to maximize the shelf life period. Normal refrigerators have fixed temperature. The work proposes a refrigerator with six compartments which is equipped with temperature sensors to maintain the fixed temperature for that compartment and with weighing sensors to monitor the depleting food items with the help of a controller. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
An Adversarial-Resilient Multi-Agent AI Framework for Autonomous Robotic Warfare Defense
Next-generation AI-enabled defense is essential to deter enemy drones, swarms, and autonomous vehicles in contested and deceptive environments, with modern battlefields becoming increasingly dependent on autonomous robot platforms. To discover and dissect and eliminate hostile autonomous threats in real-time, this study presents an integrated Adversarial-Resilient Swarm Defense AI Framework (AR-SDAI) with a Spatio-Temporal Transformer, a Multi-Agent Reinforcement Learning Countermeasure Module, and a Hybrid Graph Attention Network. To identify hidden or counterfeit threats and improve defense against enemy attacks, the system begins with applying a Transformer-based situational awareness model to merge multi-sensor battlefield data to fuse. Autonomous defense drones are then controlled by a multi-agent reinforcement learning framework to perform actions of dynamic electronic jamming and optimal interception maneuvers in a swarm environment. Finally, the system can find unusual patterns and create human-understandable counter-strategies for human-in-the-loop control with the help of a graph-based explainability layer that models the interactions of adversary swarms as dynamic graphs. Compared to traditional rule-based and CNN-RNN baselines using experiments on a simulated Red-Blue drone warfare test benchmark, the suggested AR-SDAI is better by 23% in threat detection accuracy, 31% in swarm interception success rate, and 19% in response latency. With its provision of robust, explainable, and flexible AI capability for next-generation robotic warfare settings, the paper in general enhances the state of autonomous defense operations. 2026 Saurav Mallik, Sandeep Kumar Mathivanan, Basu Dev Shivahare. -
An AHP-TOPSIS Approach for Optimizing the Mechanical Performance of Natural Fiber-Based Green Composites
Natural fibers have emerged as an effective replacement for synthetic fibers in the fabrication of green composites to be used for producing various components in automotive, aerospace, and other applications. In this proposed study, the mechanical properties of banana and coir fiber-based green composites have been optimized by using a hybrid AHP-TOPSIS approach. Corn starch along with glycerol has been used as the matrix material for fabricating the green composites. The mechanical properties such as tensile strength, flexural strength, and impact strength of the developed green composite have been optimized with a focus on the utilization of this composite in automotive and aerospace applications. Three different weight percentages (0%, 5%, and 10%) of banana and coir fibers was considered for the fabrication of green composites. The constituents of the green composite have been taken as the input variables whereas the mechanical properties of the green composite are considered as the output variables for designing the experiment. The design of the experiment consisted of nine different combinations of input and output variables. Results of the study revealed that 5 wt.% of banana fiber, 10 wt.% of coir fiber, and 85 wt.% of corn starch provide the optimum mechanical performance of the developed green composites. 2022 A. N. Shankar et al. -
An AI Approach to Pose-based Sports Activity Classification
Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models. 2021 IEEE -
An AI-Based Forensic Model for Online Social Networks
With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An AI-Driven Framework for Computational Literary Analysis: Bridging English Literature and Technology
Artificial Intelligence is emerging to be significantly used in the field of digital humanities, yet the focus of achieving interpretability and predictive level of performance simultaneously in the field of literary analysis is challenging. This paper highlights the study of a newly proposed effective model that is the Interpretable and Pedagogical Artificial Intelligence Framework (IPAF), a novel model that is designed for the analysis of poetry, prose and drama, while suitable outputs are provided by the model that are explainable in the field of education and literature research. The traditional models, such as the Random Forest, XG Boost and the Light BGM model, were used as base learners and have been integrated into the IPAF model by using Term Frequency-Inverse Document Frequency (TF-IDF) and the BERT embedding towards feature-level representations. The evaluation of this framework is carried out by utilising standard levels of classification metrics such as precision, accuracy level, F1 score and the SHAP-based explainability, which is applied for identifying the significant features of text that are influencing predictions. The results show that the IPAF model significantly outperforms the existing baseline models, by achieving a model with high accuracy, stability and robustness, that provides actionable insights towards educators and literary researchers. The interpretability of the framework allows users towards linking the computational outputs of the model alongside stylistic and theme-based patterns, by bridging the need for qualitative analysis along critical understanding of the literature. The future scope of the work extends the IPAF model to explore multi-modal levels of literary analysis by integrating data for audio, text and image together for digital archives for the further enhancement of cross-genre interpretation and applications of pedagogy. 2025 IEEE. -
An AI-enabled research support tool for the classification system of COVID-19
The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM). Copyright 2023 Tiwari, Bhattacharjee, Pant, Srivastava and Snasel. -
An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach
We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE. -
An algorithm for IoT based vehicle verification system using RFID
The verification of vehicle documents is an important role of transport department which is rising day by day due to the mass registration of the vehicles. An automated vehicle verification system can improve the efficiency of this process. In this paper, we propose an IOT based vehicle verification system using RFID technology. As a result, the vehicle checking which is done now manually can be replaced by automation. There is a loss of a significant amount of time when the normal vehicle checking is done manually. The proposed system will make this process automated. The present verification process is using inductive loops that are placed in a roadbed for detecting vehicles as they pass through the loop of the magnetic field. Similarly, the sensing devices spread along the road can detect passing vehicles through the Bluetooth mechanism. The fixed audio detection devices that can be used to identify the type of vehicles on the road. Other measurements are fixed cameras installed in specific points of roads for categorising the vehicles. But all these mechanisms cannot verify the documents and certificates of the vehicles. In our work, we have suggested an algorithm using RFID technology to automate the documentation verification process of the vehicles like Pollution, Insurance, Rc book etc with the help of RFID reader placed at road checking areas. This documents will be updated by the motor vehicle department at specific periods. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.