While U.S. companies contend with a shortage of EHS professionals and skilled laborers, a global look at current and expected hiring reveals a complex picture. According to ManpowerGroup's first-quarter 2013 Manpower Employment Outlook Survey, the majority of employers in the global labor market are less confident about adding staff than they were at the start of 2012, suggesting a more difficult time ahead for job seekers in some countries.

Variety Awards Circuit section is the home for all awards news and related content throughout the year, featuring the following: the official predictions for the upcoming Oscars, Emmys, Grammys and Tony Awards ceremonies, curated by Variety senior awards editor Clayton Davis. The prediction pages reflect the current standings in the race and do not reflect personal preferences for any individual […]

Variety Awards Circuit section is the home for all awards news and related content throughout the year, featuring the following: the official predictions for the upcoming Oscars, Emmys, Grammys and Tony Awards ceremonies, curated by Variety senior awards editor Clayton Davis. The prediction pages reflect the current standings in the race and do not reflect personal preferences for any individual […]

Historian and political scientist Allan Lichtman said Tuesday on NewsNation's "Cuomo" that conservative media disinformation and billionaire Elon Musk were why his election prediction that Vice President Harris would win the presidency was incorrect.

The post Lichtman Blames Bad Election Prediction on Conservative Media ‘Disinformation’ and Elon Musk appeared first on Breitbart.

Chris "The Bear" Fallica delivers his best bet for NFL Week 11. Read why he's backing the Eagles to win and cover against the Commanders on Thursday Night Football.

Gambling expert Chris “The Bear” Fallica and former NFL Offensive Lineman Geoff Schwartz are joined by friends and analysts Will Hill and Sam Panayotovich in The Gambling Group Chat to discuss the best bets and current wagers for Tennessee vs. Georgia in week 12 of college football.

1. Kanguva Box Office Day 1 Prediction: Start Of Over 20 Crores Is Locked But It Might Struggle To Beat Kamal Haasan’s Indian 2!  Koimoi
2. Review : Kanguva – High-voltage action drama that works only in parts  123telugu
3. Kanguva first reviews: Suriya ‘shed his blood and sweat’ but the movie ‘is tiring’, say fans  Hindustan Times
4. Kanguva Movie Review Rating and Release LIVE Updates: Netizens Call Suriya Film 'Pure One Man Show'  Times Now
5. Kanguva Movie Review: An earnest Suriya gives his all for a Siva film that doesn’t give him enough  The Indian Express

Allan Lichtman, often dubbed the "Nostradamus" of US Presidential Polls, is blaming the "explosion" of disinformation and billionaire Elon Musk for his incorrect prediction that Kamala Harris would win the 2024 election.

1. Sri Lanka vs New Zealand Match Prediction - Who will win today’s 1st ODI match between SL vs NZ?  CricTracker
2. Mendis, Fernando and spinners star as Sri Lanka clinch rain-affected ODI opener  Cricbuzz
3. Kusal Mendis 143 and Avishka Fernando 100 put Sri Lanka 1-0 up  ESPNcricinfo
4. Sri Lanka beat New Zealand by 45-runs to go 1-0 up in ODI series  The Times of India
5. Sri Lanka vs New Zealand Highlights: Sri Lanka beat New Zealand by 45 runs (DLS method)  Mint

1. Horoscope 14th November, 2024: Check astrological prediction for Virgo, Sagittarius and other signs  The Indian Express
2. Horoscope Today: November 14, 2024  VOGUE India
3. Love and Relationship Horoscope for November 14, 2024  Hindustan Times
4. Horoscope Today, November 14, 2024: Read your today's astrological predictions  The Times of India
5. Aries Daily Horoscope Today (Mar 21 – April 19), November 14, 2024: Relationships will be comfortable!  India Today

Nifty 50 November futures can fall to 23,400

Bank Nifty futures is now testing the support at 51,000

Kanguva is set to take third biggest opening in Tamil Nadu this year after Thalapathy Vijay's The Greatest of All Time and Rajinikanth's Vettaiyan.

சென்னை: சூர்யா நடிப்பில் பிரம்மாண்ட படமாக 300 கோடிக்கும் அதிகமான பட்ஜெட்டில் உருவாகியுள்ள கங்குவா படத்தின் டிக்கெட் புக்கிங் தொடங்கி சூடுபிடிக்கத் தொடங்கியுள்ளது. தமிழ்நாட்டில் பல்வேறு தியேட்டர்களில் அதிக எண்ணிக்கையிலான டிக்கெட் புக்கிங் நடைபெற்று வருகிறது. ஆந்திராவிலும் கங்குவா படத்துக்கு நல்ல வரவேற்பு டிக்கெட் புக்கிங்கில் தெரிகிறது. ஸ்டூடியோ க்ரீன் தயாரிப்பில் உருவாகியுள்ள இந்த படத்தை சிறுத்தை

A community of small business owners was polled to find out what they thought the top industries would be for next year. Technology, at 42%, is predicted to be the most successful industry in 2015, followed by health care 28%, retail 14%, and finance 14%.

complete article

From the ever-changing technology sphere to ideas about the economy and business growth, here are 50 big ideas, predictions and trends to look out for in the small business world in 2015.

complete aricle

For nearly 40 years, The Simpsons has entertained audiences worldwide and gained a reputation for uncanny predictions. Here are some predictions that came true.

A scientist and astrologer, the famous Nostradamus left many predictions in his Prophecies, which have continued to fascinate people for centuries. So, what did he predict for the year 2025?

Larne defender Tomas Cosgrove gives his predictions for this weekend's Irish Premiership games.

In order to solve the problems of long prediction consumption time and many prediction iterations existing in traditional prediction models, an e-commerce growth prediction model based on grey Markov chain is proposed. The Scrapy crawler framework is used to collect a variety of e-commerce data from e-commerce websites, and the feedforward neural network model is used to clean the collected data. With the cleaned e-commerce data as the input vector and the e-commerce growth prediction results as the output vector, an e-commerce growth prediction model based on the grey Markov chain is built. The prediction model is improved by using the background value optimisation method. After training the model through the improved particle swarm optimisation algorithm, accurate e-commerce growth prediction results are obtained. The experimental results show that the maximum time consumption of e-commerce growth prediction of this model is only 0.032, and the number of iterations is small.

It is inevitable for basketball players to have physical injury in sports. Reducing basketball injury is one of the main aims of the study of basketball. In view of this, this paper proposes a monocular vision and FMS injury prediction model for basketball players. Aiming at the limitations of traditional FMS testing methods, this study introduces intelligent machine learning methods. In this study, random forest algorithm was introduced into OpenPose network to improve model node occlusion, missed detection or false detection. In addition, to reduce the computational load of the network, the original OpenPose network was replaced by a lightweight OpenPose network. The experimental results show that the average processing time of the proposed model is about 90 ms, and the output video frame rate is 10 frames per second, which can meet the real-time requirements. This study analysed the students participating in the basketball league of the College of Sports Science of Nantong University, and the results confirmed the accuracy of the injury prediction of college basketball players based on FMS scores. It is hoped that this study can provide some reference for the research of injury prevention of basketball players.

Food quality is defined as a collection of properties that differentiate each unit and influences acceptability degree of food by users or consumers. Owing to the nature of food, food quality prediction is highly significant after specific periods of storage or before use by consumers. However, the accuracy is the major problem in the existing methods. Hence, this paper presents a BEFA_DRNN approach for accurate food quality prediction using dairy products. Firstly, input data is fed to data normalisation phase, which is performed by min-max normalisation. Thereafter, normalised data is given to feature fusion phase that is conducted employing DNN with Canberra distance. Then, fused data is subjected to data augmentation stage, which is carried out utilising oversampling technique. Finally, food quality prediction is done wherein milk is graded employing DRNN. The training of DRNN is executed by proposed BEFA that is a combination of BES and FA. Additionally, BEFA_DRNN obtained maximum accuracy, TPR and TNR values of 93.6%, 92.5% and 90.7%.

In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as <i>ResNet18</i> and <i>DenseNet121</i> to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

This paper proposes a method for predicting college students' performance based on learning behaviour data mining. The method addresses the issue of limited sample size affecting prediction accuracy. It utilises the K-means clustering algorithm to mine learning behaviour data and employs a density-based approach to determine optimal clustering centres, which are then output as the results of the clustering process. These clustering results are used as input for an attention encoder-decoder model to extract features from the learning behaviour sequence, incorporating an attention mechanism, sequence feature generator, and decoder. The characteristics derived from the learning behaviour sequence are then used to establish a prediction model for college students' performance, employing support vector regression. Experimental results demonstrate that this method accurately predicts students' performance with a relative error of less than 4% by leveraging the results obtained from learning behaviour data mining.

Using information criteria is a common method for making a decision about which model to use for forecasting. There are many different methods for evaluating forecasting models, such as MAE, RMSE, MAPE, and Theil-U, among others. After the creation of AIC, AICc, HQ, BIC, and BICc, the two criteria that have become the most popular and commonly utilised are Bayesian IC and Akaike's IC. In this investigation, we are innovative in our use of exponential weighting to get the log-likelihood of the information criteria for model selection, which means that we propose assigning greater weight to more recent data in order to reflect their increased precision. All research data is from the major stock markets' daily observations, which include the USA (GSPC, DJI), Europe (FTSE 100, AEX, and FCHI), and Asia (Nikkei).

Aim/Purpose: Any system that aims to address the task of modeling social media communication need to deal with the usage of emojis. Efficient prediction of the most likely emoji given the text of a message may help to improve different NLP tasks. Background: We explore two tasks: emoji identification and emoji prediction. While emoji prediction is a classification task of predicting the emojis that appear in a given text message, emoji identification is the complementary preceding task of determining if a given text message includes emojies. Methodology: We adopt a supervised Machine Learning (ML) approach. We compare two text representation approaches, i.e., n-grams and character n-grams and analyze the contribution of additional metadata features to the classification. Contribution: The task of emoji identification is novel. We extend the definition of the emoji prediction task by allowing to use not only the textual content but also meta-data analysis. Findings: Metadata improve the classification accuracy in the task of emoji identification. In the task of emoji prediction it is better to apply feature selection. Recommendations for Practitioners: In many of the cases the classifier decision seems fitter to the comment content than the emoji that was chosen by the commentator. The classifier may be useful for emoji suggestion. Recommendation for Researchers: Explore character-based representations rather than word-based representations in the case of morphologically rich languages. Impact on Society: Improve the modeling of social media communication. Future Research: We plan to address the multi-label setting of the emoji prediction task and to investigate the deep learning approach for both of our classification tasks

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

DSSP stands as a foundational tool in the domain of protein secondary structure prediction, yet it encounters notable challenges in accurately annotating irregular structures, such as β-turns and γ-turns, which constitute approximately 25%-30% and 10%-15% of protein turns, respectively. This limitation arises from DSSP's reliance on hydrogen-bond analysis, resulting in annotation gaps and reduced consensus on irregular structures. Alternatively, PROMOTIF excels at identifying these irregular structure annotations using phi-psi information. Despite their complementary strengths, previous methodologies utilised DSSP and PROMOTIF separately, leading to disparate prediction methods for protein secondary structures, hampering comprehensive structure analysis crucial for drug development. In this work, we bridge this gap using an annotation fusion approach, combining DSSP structures with beta, and gamma turns. We introduce IRNN-SS, a model employing deep inception and bidirectional gated recurrent neural networks, achieving 77.4% prediction accuracy on benchmark datasets, outpacing current models.

Prediction of route based on historical trip observation of users is widely employed in location-based services. This work concentrates on building a route prediction system using Lempel-Ziv technique applied to a historical corpus of user travel data. Huge continuous logs of historical GPS traces representing the user's location in past are decomposed into smaller logical units known as trips. User trips are converted into sequences of road network edges using a process known as map matching. Lempel-Ziv is applied on road network edges to build the prediction model that captures the user's travel pattern in the past. A two-phased model is proposed using a map reduce framework without losing accuracy and efficiency. Model is then used to predict the user's end-to-end route given a partial route travelled by the user at any point in time. The objective of the proposed work is to build a Route Prediction system in which model building and prediction both are horizontally scalable.

Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Mental illnesses are common among college students as well as their non-student peers, and the number and severity of these problems are increasing. It can be difficult to identify people suffering from mental illness and get the help they need early. So in this paper, the SqueezeR_MobileNet method is proposed. It performs feature fusion and early mental health prediction. Initially, outliers in the input data are detected and removed. After that, using missing data imputation and Z-score normalisation the pre-processing phase is executed. Next to this, for feature fusion, a combination of the Soergel metric and deep Kronecker network (DKN) is used. By utilising bootstrapping data augmentation is performed. Finally, early mental health prediction is done using SqueezeR_MobileNet, which is the incorporation of residual SqueezeNet and MobileNet. The devised approach has reached the highest specificity of 0.937, accuracy of 0.911 and sensitivity of 0.907.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

Whole Foods Market’s summer condiment trends predictions come at a time when customers are turning to condiments to elevate their meals more than ever before. According to Mintel, sales for the condiment, marinade and dressing category are expected to hit $2.9 billion by 2024, showing growth of more than 5% since 2020.

The explosion of plant-base meat and dairy analogs has created a paradigm shift in the ways food product developers are considering fats and oils. There’s plenty of innovation around plant-based meat alternatives, especially as they expand beyond burgers, sausages, and poultry into mimics of bacon, pork, and even such specific items as turkey burgers.

In a few weeks, we will begin publishing predictions for 2025. Prior to sharing our outlook for the year ahead, we wanted to review the predictions we made for 2024 to determine what we got right and what might not have fully taken shape.

Prior to sharing our outlook for 2025, we wanted to review the predictions we made for 2024 to determine what we got right and what might not have fully taken shape. Here, we've collected capsule information for each section of our 2024 Predictions, in areas such as sustainability, foodservice and global flavors. Within each capsule, you will find our 2024 predictions and assessments of their accuracy.

Read up on the bits and pieces of flavor trends and product innovation in this installment of Nuts & Bolts

Originally broadcast on November 2, 2012.

As the nation heads into election day on November 6, Basic Black considers the relevance of polls and the persistence of predictions. And what does it say about the candidates and this country that the race is so close?

In conversation:
- Latoyia Edwards, anchor, New England Cable News
- Phillip Martin, senior reporter, 89.7 WGBH Radio
- Peniel Joseph, professor of history Tufts University; Du Bois Fellow, Harvard University
- Robert Fortes, Republican strategist


(Photo: Early voting, Ohio 2012. Source: Associated Press.)

World War Three that may put an end t your existence as a human civilisation, will set off on its destructive march from the Middle East. This is what a number of prominent figures, as well as seers and mystics predicted. Perhaps the most famous modern forecast on the subject came from the late leader of the Liberal Democratic Party Vladimir Zhirinovsky, authors of AZ numerology project said while collecting predictions about the Middle East conflict. Speaking on Vladimir Solovyov Live in 2019, Zhirinovsky voiced an opinion that elections in Ukraine were its last, as "such a country simply will not remain on the map by 2024.” Moreover, the crisis in the Middle East will be so intense everyone will completely forget about Ukraine. 

Sinch, the company developing the way the world communicates through its Customer Communications Cloud, has released its predictions for the 2024 Black Friday and Cyber Monday (BF/CM) shopping season.

Added October 19, 2006:

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure generation methods.

The results of the seventh blind test of crystal structure prediction are presented, focusing on structure ranking methods.