Human-machine collaboration often involves constrained optimization problems for decision-making processes. However, when the machine is a dynamical system with a continuously evolving state, infeasibility due to mult...
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Social media users are growing daily, with hundreds of millions of active users per month on certain networking sites. For any administrative institution, the manual method for regulating user content is challenging. ...
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ISBN:
(纸本)9781450398541
Social media users are growing daily, with hundreds of millions of active users per month on certain networking sites. For any administrative institution, the manual method for regulating user content is challenging. There are hundreds of languages through which you can direct your attention on the web. The Urdu language is among the most widely utilized languages in the world. We have proposed a quick way of detecting the content of Urdu language hate using machine learning models. We used the open data set and manually created instances to make this investigation viable on a balanced data set. Our experimental set-up has demonstrated that support vector machine in the detection of Urdu hatred detection is 81.87% accurate. The training time, testing time, and accuracy helped us select the best model for Urdu hate detection on social media sites. We also compared the training and testing times of various methods. Additionally, we demonstrated k and stratified folding via indexing to provide a better understanding of folding in machine learning. Finally, we compared our findings to those of previously published works in the field of Urdu hate detection.
Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic control Units (ECUs) have evolved to become the central control uni...
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ISBN:
(数字)9798350364910
ISBN:
(纸本)9798350364927
Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable; attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks.
One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the...
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In this paper, online game is studied, where at each time, a group of players aim at selfishly minimizing their own time-varying cost function simultaneously subject to time-varying coupled constraints and local feasi...
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This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education ...
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ISBN:
(数字)9798350351507
ISBN:
(纸本)9798350363067
This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research
COVID-19 is the contagious disease transmitted by *** majority of people diagnosed with COVID-19 may suffer from moderate-tosevere respiratory illnesses and stabilize without preferential *** who are most likely to ex...
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COVID-19 is the contagious disease transmitted by *** majority of people diagnosed with COVID-19 may suffer from moderate-tosevere respiratory illnesses and stabilize without preferential *** who are most likely to experience significant infections include the elderly as well as people with a history of significant medical issues including heart disease,diabetes,or chronic breathing *** novel Coronavirus has affected not only the physical and mental health of the people but also had adverse impact on their emotional *** months on end now,due to constant monitoring and containment measures to combat COVID-19,people have been forced to live in isolation and maintain the norms of social distancing with no community *** ties,experiences,and partnerships are not only integral part of work life but also form the basis of human ***,COVID-19 brought all such communication to a grinding *** interactions have failed to support the fervor that one enjoys in face-to-face *** COVID-19 disease outbreak has triggered dramatic changes in many sectors,and the main among them is the software *** paper aims at assessing COVID-19’s impact on Software *** impact of the COVID-19 disease outbreak has been measured on the basis of some predefined criteria for the demand of different software applications in the software *** the stated analysis,we used an approach that involves the application of the integrated Fuzzy ANP and TOPSIS strategies for the assessment of the impact of COVID-19 on the software *** of this research study indicate that Government administration based software applications were severely affected,and these applications have been the major apprehensions in the wake of the pandemic’s ***,COVID-19 has had a considerable impact on software industry,yet the damage is not irretrievable and the world’s societies can emerge out
Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Mental healthcare chatbots are artificially intelligent systems that have been built with the intent of simulating human conversation and provide emotional support to one’s user. They are also widely used as tools to help individuals cope with the stress, anxiety, and other mental health challenges they have been facing, including easily accessible and affordable solutions. These chatbots use NLP and emotion recognition technology to assist users in understanding their feelings and responding accordingly. This review unpacks the latest advancements of recently developed chatbots, such as GPT-4-based models, BERT and MBERT based models, sentiment analysis, and the study of multimodal systems combining text, voice, and facial expression recognition. Also addressed are important challenges, including privacy concerns, cultural inclusivity, and clinical validation. Finally, the review identifies opportunities to improve chatbots, including supporting multi-language and primary telehealth platforms and customizing user interaction with chatbots to create more inclusive and effective mental health support worldwide.
Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and ...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Real-time Sign Language interpretation is among the fastest-growing fields that can potentially minimize the communication gap between deaf people and the hearing population and prepare the ground for inclusivity and accessibility throughout social, educational, and professional contexts. The review discusses the development of technologies for sign languages interpretation in real time, having the main emphasis that have to be met such as high interpretation accuracy, low latency, and computational efficiency. This review methodology, which compared machine learning and deep learning algorithms and discussed different model architectures, analyzed the trends and emerging methodologies that have shaped the current state of the field and identified significant gaps in the literature. In particular, the tendency is such that the multimodality involving hand, face, and body movement participation is becoming increasingly more effective; the pre-trained models get adapted to perform sign language tasks. Most importantly, diversity in datasets promotes model robustness across languages and dialects. A bit further, practical problems are discussed with real-time interpretation deployment on mobile devices and other resource-limited platforms. The following review is designed to point out the likely best directions for future research and development that would generalize, through the advancement of real time sign language interpretation, to become an accessible and reliable technology for everyday use.
An existing infrastructure can be linked to billions of devices, or "things," through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. M...
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ISBN:
(数字)9798350389609
ISBN:
(纸本)9798350389616
An existing infrastructure can be linked to billions of devices, or "things," through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. Massive volumes of data are being generated due to the proliferation of devices globally, which permeates every facet of everyday life. Thus, new problems are emerging as a result of the development and use of existing technologies, and these problems pertain to new applications, regulations, cloud computing, security, and privacy. With its decentralized nature, the blockchain has the potential to protect users' and data's privacy. This study introduces a novel method that makes use of lightweight blockchain technology to significantly lessen the computing load usually seen in traditional blockchain systems. Implementation time and computational complexity can be significantly reduced by connecting this lightweight blockchain with IoT devices. Improved safety is achieved by the use of CA-LSTM (channel attention long short-term memory) technology for attack detection. In order to improve the CA-LSTM algorithm's solution, the suggested system uses the RMSSO algorithm as a hyperparameter tuning technique. Similarly, to found very little power consumption and physical memory utilization; for example, Raspberry Pi devices used 0.2 GB of memory and NVIDIA Jetson devices used 0.42 GB. With the deployed model, power consumption increased by an average of 15% per device. This technology makes it easier to build certain types of business models and gives decentralized apps the ability to securely compute on encrypted data without compromising user privacy. The findings confirm that the proposed system is very secure, on par with conventional blockchain implementations.
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