Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to buy different items. The rapid rise of social networking sites has rendered them essential tools for eval...
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ISBN:
(数字)9798331508685
ISBN:
(纸本)9798331519476
Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to buy different items. The rapid rise of social networking sites has rendered them essential tools for evaluating consumer sentiments and forecasting a wide range of societal and economic patterns. This study gathered statistics from a variety of social media channels. This study examined data to anticipate user activity on social networking sites. This study took into account user statistics from Twitter, LinkedIn, YouTube, Facebook, Instagram, and Pinterest, among other platforms. because social media networks provide a wide range of statistics at elevated speeds and volumes, predicted big data techniques were applied in this study. This investigation studied user conduct on social networking sites using specific metrics and variables. This study examined user perception and attitudes towards social networking platforms. This study preprocessed data to remove outliers, disturbances, errors, and duplicate records, resulting in high-quality results. As a result, in this paper, an integrated system combining sentiment evaluation and machine learning (ML) methods is constructed. This study utilized computational models and ML to anticipate user conduct on social networking platforms. The framework predicts user conduct on social networking platforms. Eighty percent of data has been employed for training, with twenty percent for testing. After testing many conventional and ensemble ML classifiers, decision trees outscored every other method.
Modern technology is the best thing to be used in every commercial sector. The devices are well equipped to understand the problems of the users. The offices are now been maintained by technology the devices for daily...
Modern technology is the best thing to be used in every commercial sector. The devices are well equipped to understand the problems of the users. The offices are now been maintained by technology the devices for daily monitoring even the electrical appliances are been monitored and maintained by technology. In previous times the best method of this work is to make it via cloud computing, but now as technology has evolved and evolved new adding devices are making system malfunction. The fault of cloud computing needs to be identified, So the best method is to automate the office with the introduction of iot. it is a platform equipped with all the facilities for monitoring and maintaining the devices inside the office, even the working capacity of employees is also measured via this method. The study is showing the effectiveness of the iot to identify the faults of cloud computing related to maintaining the working method of the office. The data collected for this study is secondary data like articles, books, journals and many more. Then gathered data is analyzed by qualitative data analysis. The results are quite positive about the use of it to automate the office and its working procedure.
作者:
Amal MathewKaushik DaivPolkumpally Rohan GoudPiyush TalrejaSai Sanjana Reddy VatteB.tech
Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E.
Computer Science MIT College of Engineering Pune Maharashtra India B.E
Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E
Computer Science Maharashtra Institute of Technology Pune Maharashtra India
Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, b...
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ISBN:
(纸本)9781665494465
Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT
Air pollution has become major cause of global concern in recent times. Global warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urbanization, defore...
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ISBN:
(纸本)9781665420884
Air pollution has become major cause of global concern in recent times. Global warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urbanization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases by industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testbed. This work proposes a model in which Machine Learning algorithms are applied to measure the future AQI data from the current and past available AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives best prediction with limited number of data entries and Random Forest gives best result with larger dataset.
brain oscillations are essential to math and spatial skills. Cognition, attention, memory processing and language are influenced by these oscillations. Neurological explanations proposed to account for the fluctuation...
brain oscillations are essential to math and spatial skills. Cognition, attention, memory processing and language are influenced by these oscillations. Neurological explanations proposed to account for the fluctuations in brain activity help us understand how important the relation of rhythmicity is within a neural network. Desynchronization refers to the sharp and irregular changes of the oscillations in brain waves called rapid-eye movement sleep spindle activity during sleep. It is the disruption of interactions between resting oscillations of brain regions typically producing rhythmic neuronal firing that occur simultaneously in different frequency bands like alpha or beta. The beta band is associated with attention and self-focused activities like perceiving, thinking, judging, and problem solving. In this paper, the authors develop a neural model of nonlinear oscillators that transforms stimuli into characteristic oscillatory activity of the beta band.
Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most comm...
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To overcome this issue, use Low-Density Parity-Check (LDPC) architecture-based encryption and decryption, as well as analyses the bus links using Error Correcting Codes (ECC) for data transmission signals. In this met...
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Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively because of dangerous surroundings and challenging terrain features. An AI-controlled robotic system e...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
Search and rescue (SAR) operations designed to save lives in disaster zones face difficulties executing effectively because of dangerous surroundings and challenging terrain features. An AI-controlled robotic system exists to deal with existing challenges. The system combines a reinforcement learning-based navigation system that integrates multiple sensors including LiDAR with thermal cameras and RGb sensors for detecting victims to work with decentralized task distribution frameworks. Real-time simulations tested disaster navigation datasets showing an overall detection accuracy rate of 95.6%, navigation accuracy at 92.8%, while obstacle avoidance performed at 97.2%. This deployed system achieved 3.2-minute average response times and processed requests with 85.4% resource efficiency. The system proves superior performance over existing SAR methods while demonstrating speed enhancement, accuracy improvement, and autonomous operational value in SAR operations. Future disaster response will change thanks to AI-powered robotics because this technology enables scalable efficient reliable solutions for future deployments.
In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach...
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ISBN:
(数字)9798350369083
ISBN:
(纸本)9798350369090
In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms. Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. by integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text. One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms. In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare. In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactiv
Detection of White blood Cell (WbC) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), and Myeloma is a complex task in medical field because they are sudden in onset. Our proposed ...
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