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Teachers are used to hearing about new ideas in education — changes in instruction, technology and curriculum that are going to fix what’s broken.
The trouble is, these changes are so difficult to trust. Many changes are based on ideas that have gained traction through very limited and poorly researched beginnings. One district might see success with a “program,” and soon superintendents and principals are sent scrambling to duplicate that approach in their own district, without a full understanding of both data and circumstance.
On the flipside, other changes are based entirely on “data,” products of number-crunching from funded studies that keep telling us what we already know — technology makes new things possible, socioeconomic status matters, and literacy skills are everything. Changes here produce clinical, lifeless curricula that mean well but lack the ambition to reach for students’ imagination.
Redressing Learning TrendsOf course, there are liars, damned liars and statisticians, as Mark Twain put it, and veteran educators who have seen education trends come and go understand this. They recognize that many “new” ideas are repackaged approaches they’ve seen before. What’s old is new again, and minor redressing of previous strategies can limit credibility with teachers and administrators alike — affecting what many call “buy-in.”
The standards movement, the social-emotional learning movement, the literacy movement, the whole child movement, the testing movement, and now the technology movement all indirectly undermine their own success due to both the frequency with which they arrive and their divergence from what came before. Unifying it all would require the intellectual, professional and human leadership that we continue to lack.
But now, in late 2013, we have a significant challenge unlike those seen in the past. While education struggles to agree on what needs changing and how to make it happen (and why, it should be asked, do we have to agree?), the culture around us has exploded, detonated by technology.
Technology as a Permanent DisruptorWith the rise of technology in culture, students connect to data, to media and to one another in ways that would have been hard to imagine even a decade ago. And it’s not just the way students interact. It’s the scale and frequency with which they send a text, watch a video, listen to a song, or share a link via social media. This constant barrage of stimuli has created a student that is wired to survey, connect, evaluate ever so briefly, and then delete.
And this connect-and-delete approach to data interaction collides rather spectacularly with an education system that has been trained to resist change, often for good reason. That leaves us at a bit of an impasse, with technology as perhaps a permanent disruptor in education.
So what’s the takeaway for us, as teachers?
Well, the students have already changed. Learning trends are no longer about preparation, but about mitigation, about reducing the erosive effect of pairing connected students with disconnected learning environments. Coming to terms with that is important for both teachers and other change agents. We’re chasing, not leading.
This would seem to suggest the need for either incredibly powerful and compelling singular leadership, or diversity — a million different approaches that all play their role.
This would require abandoning the pursuit of a “best way” to educate — whether it’s a “program,” a scripted curriculum, or even a set of preferred instructional strategies — in favor of a mosaic of pedagogical and heutagogical approaches to learning that begin with the student, and work backward from there.
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Car Price Prediction – Machine Learning Vs Deep Learning
This article was published as a part of the Data Science Blogathon
1. ObjectiveIn this article, we will be predicting the prices of used cars. We will be building various Machine Learning models and Deep Learning models with different architectures. In the end, we will see how machine learning models perform in comparison to deep learning models.
2. Data UsedHere we have used the data from a hiring competition that was live on chúng tôi Use the below link to access the data and use it for your analysis.
3. Data InspectionIn this section, we will explore the data. First Let’s see what columns we have in the data and their data types along with missing values information.
We can observe that data have 19237 rows and 18 columns.
There are 5 numeric columns and 13 categorical columns. With the first look, we can see that there are no missing values in the data.
‘Price‘ column/feature is going to be the target column or dependent feature for this project.
Let’s see the distribution of the data.
4. Data PreparationHere we will clean the data and prepare it for training the model.
‘ID’ columnWe are dropping the ‘ID’ column since it does not hold any significance for car Price prediction.
df.drop('ID',axis=1,inplace=True) ‘Levy’ columnAfter analyzing the ‘Levy’ column we found out that it does contain the missing values but it was given as ‘-‘ in the data and that’s why we were not able to capture the missing values earlier in the data.
Here we will impute ‘-‘ in the ‘Levy’ column with ‘0’ assuming there was no ‘Levy’. We can also impute it with ‘mean’ or ‘median’, but that’s a choice that you have to make.
df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) ‘Mileage’ column‘Mileage’ column here means how many kilometres the car has driven. ‘km’ is written in the column after each reading. We will remove that.
#since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') ‘Engine Volume’ columnIn the ‘Engine Volumn’ column along with the Engine Volumn ‘type’ of the engine(Turbo or not Turbo) is also written. We will create a new column that shows the ‘type’ of ‘Engine’.
df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) ‘Doors’ Column df['Doors'].unique()Output:
‘Doors’ column represents the number of doors in the car. But as we can see it is not clean. Let’s clean
Handling ‘Outliers’This we will examine across numerical features.
cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); sns.boxplot(df[cols[1]]); sns.boxplot(df[cols[2]]); sns.boxplot(df[cols[3]]); sns.boxplot(df[cols[4]]);
As we can see there are outliers in ‘Levy’,’Engine volume’, ‘Mileage’, ‘Cylinders’ columns. We will remove these outliers using Inter Quantile Range(IQR) method.
def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower)Let’s examine the features after removing outliers.
plt.figure(figsize=(20,10)) df[outlier_cols].boxplot()We can observe that there are no outliers in the features now.
Creating Additional FeaturesWe see that ‘Mileage’ and ‘Engine Volume’ are continuous variables. While performing regression I have observed that binning such variables can help increase the performance of the model. So I am creating the ‘Bin’ features for these features/columns.
labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) Handling Categorical featuresI have used Ordinal Encoder to handle the categorical columns. OrdinalEncoder works similar to LabelEncoder but OrdinalEncoder can be applied to multiple features while LabelEncoder can be applied to One feature at a time. For more details please visit the below links
num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1)Checking correlation
final_all_df['price_log']=np.log(final_all_df['Price'])We can observe that features are not much correlated in the data. But there is one thing that we can notice is that after log transforming ‘Price’ column, correlation with few features got increased which is a good thing. We will be using log-transformed ‘Price’ to train the model. Please visit mentioned link below to better understand how feature transformations help improve model performance.
5. Data Splitting and ScalingWe have done an 80-20 split on the data. 80% of the data will be used for training and 20% data will be used for testing.
We will also scale the data since feature values in data do not have the same scale and having different scales can produce poor model performance.
cols_drop=['Price','price_log','Cylinders'] X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) 6. Model BuildingWe built LinearRegression, XGBoost, and RandomForest as machine learning models and two deep learning models one having a small network and another having a large network.
We built base models of LinearRegression, XGBoost, and RandomForest so there is not much to show about these models but we can see the model summary and how they converge with deep learning models that we built.
Deep Learning Model – Small Network model summary model_dl_small.summary() Deep Learning Model – Small Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() Deep Learning Model – Large Network model summary model_dl_large.summary() Deep Learning Model – Large Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid()6.1 Model Performance
We have evaluated the models using Mean_Squared_Error, Mean_Absolute_Error, Mean_Absolute_Percentage_Error, Mean_Squared_Log_Error as performance matrices, and below are the results we got.
We can observe that Deep Learning Model did not perform well in comparison with Machine Learning Models. RandomForest performed really well among Machine Learning Model.
Let’s visualize the results from Random Forest.
7. Result Visualization y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show()We can observe in the graph that the model is performing really well as seen in performance matrices as well.
8. CodeCode was done on jupyter notebook. Below is the complete code for the project.
# Loading Libraries import pandas as pd import numpy as np from sklearn.preprocessing import OrdinalEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_log_error,mean_squared_error,mean_absolute_error,mean_absolute_percentage_error import datetime from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from xgboost import XGBRegressor from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns from keras.models import Sequential from keras.layers import Dense from prettytable import PrettyTable df=pd.read_csv('../input/Participant_Data_TheMathCompany_.DSHH/train.csv') df.head() # Data Inspection df.shape df.describe().transpose() df.info() sns.pairplot(df, diag_kind='kde') # Data Preprocessing df.drop('ID',axis=1,inplace=True) df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) milage_formats=set() def get_milage_format(x): x=x.split(' ')[1] milage_formats.add(x) df['Mileage'].apply(lambda x:get_milage_format(x)); milage_formats #since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') df['Engine volume'].unique() df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[1]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[2]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[3]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[4]]); def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower) #boxplot - to see outliers plt.figure(figsize=(20,10)) df[outlier_cols].boxplot() df['Doors'].unique() df['Doors']=df['Doors'].astype(str) #Creating Additional Features labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) #Handling Categorical features num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1) #Checking correlation final_all_df['price_log']=np.log(final_all_df['Price']) plt.figure(figsize=(20,10)) sns.heatmap(round(final_all_df.corr(),2),annot=True); cols_drop=['Price','price_log','Cylinders'] final_all_df.columns X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] # Data Splitting and Scaling X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) # Model Building def train_ml_model(x,y,model_type): if model_type=='lr': model=LinearRegression() elif model_type=='xgb': model=XGBRegressor() elif model_type=='rf': model=RandomForestRegressor() model.fit(X_train_scaled,np.log(y)) return model def model_evaluate(model,x,y): predictions=model.predict(x) predictions=np.exp(predictions) mse=mean_squared_error(y,predictions) mae=mean_absolute_error(y,predictions) mape=mean_absolute_percentage_error(y,predictions) msle=mean_squared_log_error(y,predictions) mse=round(mse,2) mae=round(mae,2) mape=round(mape,2) msle=round(msle,2) return [mse,mae,mape,msle] model_lr=train_ml_model(X_train_scaled,y_train,'lr') model_xgb=train_ml_model(X_train_scaled,y_train,'xgb') model_rf=train_ml_model(X_train_scaled,y_train,'rf') ## Deep Learning ### Small Network model_dl_small=Sequential() model_dl_small.add(Dense(16,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_small.add(Dense(8,activation='relu')) model_dl_small.add(Dense(4,activation='relu')) model_dl_small.add(Dense(1,activation='linear')) model_dl_small.summary() epochs=20 batch_size=10 model_dl_small.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() ### Large Network model_dl_large=Sequential() model_dl_large.add(Dense(64,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_large.add(Dense(32,activation='relu')) model_dl_large.add(Dense(16,activation='relu')) model_dl_large.add(Dense(1,activation='linear')) model_dl_large.summary() epochs=20 batch_size=10 model_dl_large.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() summary=PrettyTable(['Model','MSE','MAE','MAPE','MSLE']) summary.add_row(['LR']+model_evaluate(model_lr,X_test_scaled,y_test)) summary.add_row(['XGB']+model_evaluate(model_xgb,X_test_scaled,y_test)) summary.add_row(['RF']+model_evaluate(model_rf,X_test_scaled,y_test)) summary.add_row(['DL_SMALL']+model_evaluate(model_dl_small,X_test_scaled,y_test)) summary.add_row(['DL_LARGE']+model_evaluate(model_dl_large,X_test_scaled,y_test)) print(summary) y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show() 9.ConclusionIn this article, we tried predicting the car price using the various parameters that were provided in the data about the car. We build machine learning and deep learning models to predict car prices and saw that machine learning-based models performed well at this data than deep learning-based models.
10. About the AuthorHi, I am Kajal Kumari. I have completed my Master’s from IIT(ISM) Dhanbad in Computer Science & Engineering. As of now, I am working as Machine Learning Engineer in Hyderabad. You can also check out few other blogs that I have written here.
The media shown in this article on LSTM for Human Activity Recognition are not owned by Analytics Vidhya and are used at the Author’s discretion.
Related
Top Machine Learning Trends For 2023 And Beyond
What’s next in machine learning development?
Machine learning is one of the branches of artificial intelligence that creates algorithms to help machines understand and make decisions based on data. The process of automation of software testing is connected to the development of machine learning. Owing to that, there is a fast pace of development in the IT industry. Machine learning is being incorporated in several companies, including tech giants like Google, Apple, Facebook, Netflix, and eBay. Analysts predict that
machine learning will continue to grow in popularity until 2024, with the most growth in 2023 and 2023
.
For the next three years, these are the major trends and developments we can expect in the field of machine learning.
1. Machine learning and IoTThis is the trend that is most awaited by tech professionals. Its development will impact the usage of 5G, which will become the base for IoT. As 5G comes with high speeds, devices will react quickly and transfer and receive more information. IoT devices enable multiple devices to connect across a network via the internet. Year by year, the amount of devices that are being connected is increasing, and the amount of information transferred is being increased as well. The use of IoT devices will leverage many fields like environment, healthcare, education, and the IT field. This combination will also ensure there are fewer errors and data leaks on the internet.
2. Automated machine learningAutomated machine learning will help specialists to develop efficient models for higher productivity. Because of this, all the developments will be focused on giving out the most accurate task solving. AutoML is used to sustain high-quality custom models, to improve the efficiency of work without much knowledge of programming. Additionally, AutoML will be useful by subject matter experts. This technology will provide training without spending much time and sacrificing the quality of work.
3. Better cybersecurityMost of our appliances and apps have become smart, with a high level of tech progress.
They are constantly connected to the internet which raises the need to increase the level of security. By using machine learning, professionals can create innovative anti-virus models that can ward off cyber-crime, hackers, and minimize attacks by helping the model identify different kinds of threats, like the behavior of malware, code difference, and new viruses.
4. AI EthicsWith the development of AI and ML, ethics need to be established for these technologies. As technology is becoming modern, ethics to need to become modern, otherwise, machines will not be able to work and make wrong decisions, like
what is happening with self-driving cars
. Failure of artificial intelligence to perform as desired is the main reason for self-driving car failures. The programming in autonomous cars is driving biased conclusions by separating groups of people. These are two reasons for this:
• Developers are choosing data with bais, to begin with. For example, they can use the information where the majority of the factors can cause the machine to favor the other.
• Lack of data moderation can also make machine learning models learn from the wrong type of data. This can lead to prejudice in the neural network of the machine.
Upcoming Cybersecurity Trends For 2023
Mobile becomes a prime cyber attack vector, hackers will increasingly use machine learning from cloud and attacks may be regarded as fertile ground for compromise.
The wheels of the biggest cybersecurity threats of 2023 have already set the pace. Mobile, cloud and artificial intelligence, to name a few, are trends that will continue to be exploited by criminals. Add that the rapid growth of software development and a lack of cybersecurity skills and that should be enough to keep security professionals on their toes. Experts here say that the coming year in cyberspace is in store.
Ransomware was the scourge of 2023 and are also in 2023. Organized cyergangs will change focus from Implementing banking trojans in enormous multi-million dollar SWIFT-related heists and rather concentrate on smaller ransomware strikes. Why? “[They’re ] simpler to anonymizeeasier to launder, and [demand] much less sharing of illegal profits with street gangs that launder financial fraud profits,” stated Limor Kessem, together with IBM Security.
Mobile
Software
As software development grows, so will the requirement to nip security risks in the bud. The assault surface has risen from neighborhood code into pipeline code. To answer the question, a DevSecOps mindset needs to prevail, state security experts. Code review will have to begin from program inception to manufacturing in 2023, state specialists. “We are seeing associations begin to construct security into every stage of the development pipeline, and hope to see more of the change in 2023,” composed Veracode’s Suzanne Ciccone.
Cloud
As more corporate infrastructure goes into the cloud, so will the attention of criminals. The fantastic news and bad news after this tendency is”running an assault will become more challenging and the activities of danger actors will grow more complicated or more regular — relying on opportunity instead of planning,” based on a Kaspersky look at 2023 safety tendencies.
5G
Also read: Top 7 Work Operating Systems of 2023 Authentication
AI
Particular attacks like phishing will continue to leverage machine learning how to automate the optimization of campaigns. “Phishing baits and landing pages will probably be A/B analyzed by AI calculations to increase conversion rates, while new domain names will be created and enrolled with AI algorithms,” Lookout stated.
Fakes piqued
Also read: Best 10 Semrush Alternative for 2023 (Free & Paid) Microsoft
On January 2023 Microsoft will sunset support for Windows 7. For most consumers and businesses which don’t have extended-support setup so Microsoft will prevent patching and frequently upgrading the OS even when a security vulnerability is found. “History will repeat itself in 2023, together with one big attack leveraging the vulnerability to influence companies across the world, very similar to that which we saw with the end of existence of Windows XP,” composed Forescout.
Malware
Driven from the high price of complex malware-based strikes, growth in Cybersecurity Trends for 2023. “Direct attacks on infrastructure… has become a great deal more costly, requiring an increasing number of abilities and time to get the attacker,” Kaspersky composed. Because of this the year ahead will see, “increase in the amount of strikes using social engineering techniques… [T]he human factor remains a weak link in safety.” Because of this, “Attackers will probably be inclined to provide considerable quantities of cash to insiders. The cost to get insiders varies from area to area and is based on the target’s position in the organization,” according to Kaspersky.
Pinterest Search Trends For Fall 2023
Pinterest released an updated report on search trends with insight into what’s expected to be popular during the fall months of 2023.
Search trends are identified based on which topics are currently seeing a spike in search volume compared to this time last year.
Social media marketers can use this data to help guide their efforts throughout the rest of the season.
Pinterest calls this particular season “back to life,” saying autumn is like a second new year:
“Each year, the start of September and the beginning of autumn is seen by many people around the world as a ‘second new year’.
It’s a time when making small improvements, resetting goals and habits, and starting a fresh drive to create healthy routines feels more achievable and more personal than New Year’s resolutions.”
With that type of forward-thinking outlook, perhaps its no surprise that searches for “positivity” are among the top trends (up 64%).
“Pinners continue to reflect on personal growth, improvements and mental wellness, with significant spikes in ‘positivity’ in particular (+64%*). Life goals and travel plans have been replaced with personal projects – whether it’s home improvement or self-improvement.”
Fall 2023 is different from previous years, Pinterest notes, as more users are looking for inspiration within the home rather than outside the home.
Staying indoors and avoiding large gatherings is still what top experts recommend right now, which is reflected in Pinterest’s search trends reflect.
That’s even more of a reason to browse through this data – what would have worked on Pinterest at this time last year might not be what users are looking for this year.
With that said, let’s look at what’s top of mind for Pinterest users in fall 2023.
Pinterest Search Trends by DemographicPinterest breaks down its search trend data into two demographics:
Gen Z: Defined as users aged 18-24
Millennials: Defined as users aged 25-44
First, here are some highlights from Gen Z search trends.
Gen Z Pinterest Search TrendsSelf-love and safe havens are the two themes defining Gen Z’s search behavior this season, Pinterest says:
“With so much uncertainty in areas like school and work shifts, Gen Z Pinners are seeking ways to stay positive and healthy…”
Searches for the following topics are all seeing a surge of popularity:
Mental health check-in (up 5x)
Mindful eating (up 44%)
Photoshoot ideas (up 56x)
Zen bedroom ideas (up 5x)
Calming bedroom (up 3x)
Feng shui bedroom layout (up 2.5x)
Indie room (up 151x)
Hippie bedroom decor (up 19x)
Millennial Pinterest Search TrendsMillennials are taking to homelife in a much different way compared to Gen Z, according to Pinterest’s data.
Whereas Gen Z appears to be looking for ways to distract themselves with decor, millennials are looking for ways to keep themselves and their children occupied.
“For the past six months, home has replaced work, school and the gym, and outdoor spaces have become one of the safest places to practise social distancing.
Millennial parents continue to prioritise keeping their families healthy and happy, while addressing their children’s mental health and self-care practices…”
Searches for the following topics are all up amongst millennials:
Mental health activities for children (up 3.5x)
Occupational therapy for children (up 2x)
Conscious parenting (up 2x)
Schedule for children at home (up 20x)
Daily routine schedule for children (up 10x)
Children’s workout routine (up 88%)
Animal yoga poses for children (up 56%)
Indoor swings for children (up 3x)
Carnival games for children (up 3x)
Lava lamp experiments for children (21x)
Male Pinterest Search TrendsPinterest doesn’t ordinarily highlight search trends specifically among the male demographic, but an interesting shift has taken place.
The number of male Pinterest users is up nearly 50% since last year.
That’s notable because Pinterest has historically been made up of mostly female users, with little or no fluctuation in the percentage of male users.
So why are men suddenly flocking to Pinterest? Here’s what the report says:
“The number of male Pinners has jumped nearly 50% since last year, with men searching for homeschool inspiration, as well as improvement projects and projects that also bring younger family members in on improvements around the home.”
Searches for the following topics are up amongst male users:
Home improvement projects (up 78%)
DIY projector screen (up 41%)
Woodworking projects for children (up 2x)
Art therapy activities (up 65%)
Workout routine for men (up 3.5X)
Mental strength quotes (up 2.5x)
Source: Pinterest Newsroom
2003’S Top Trends In Im
Pundits will probably look back on 2003 — at best — as a year of very conservative growth for the instant messaging industry.
But those in the trenches this year deserve a good deal of congratulations. Despite long sales cycles and continued strain on IT budgets, the space experienced some of its largest product launches to date, a number of important alliances among stakeholders, and several key venture capital investments.
A number of key deployments also debuted, in industries ranging from financial services to entertainment, reflecting customers’ response to the gospel long preached by vendors of enterprise IM solutions — describing the benefits of real-time communications and presence awareness associated with the technology.
As 2003 draws to a close, then, chúng tôi looks back at some of the major trends that dominated the headlines during the year — and which will be sure to pave the way for developments in 2004.
Security Woes Aplenty in Public IM
First up, we take a look at an issue close to the hearts and minds of enterprise network security admins everywhere: the continuing threat to corporate infrastructure posed by consumer-grade IM. Instant messaging might be one of the hottest new channels of communications to hit the enterprise, but if it’s not properly regulated, it could easily bring with it a myriad of security headaches.
Of course, this isn’t a new issue. But it remains a constant source of worry for IT staff, and continues to be one of the chief factors behind the sale of enterprise-grade IM solutions.
New threats this year include the increasing use of instant messaging by viruses and worms, as a channel by which to spread. In October, enterprise security technology vendor Symantec released findings that indicated IM-based security threats are seeing a dramatic surge. The firm found that of the top 50 virus threats during the first six months of the year, IM and peer-to-peer technology played a role in 19 — a 400 percent increase from the previous year.
Also this year, Microsoft and Yahoo! each recently required users to upgrade to new versions of their IM clients in response to serious security threats. Despite the networks’ efforts, however, hackers continue finding holes in their clients. Just earlier this month, security researchers found a possible vulnerability in Yahoo! Messenger, for instance. And as public IM continues to grow in the workplace, these security holes seem likely to only increase in frequency.
Application Integration
On a brighter note, strides were made this year in linking enterprise instant messaging systems to other applications in the workplace — which raises the ROI of IM deployments and broadens their utility to businesses.
For one thing, Lotus began executing on its Workplace strategy and related product line. Workplace product components will be available individually or in a package, and portions of their features can be integrated with other apps through Web services. In Workplace 1.1, released in November, Lotus debuted new offerings integrating Web conferencing and IM capabilities from Lotus Team Workplace 3 (a product previously known as QuickPlace, and not part of the Workplace initiative) and Lotus Instant Messaging and Web Conferencing (formerly Sametime).
In Lotus Notes and Domino 6.5, the software giant more closely integrated Lotus Instant Messaging functionality, in an effort to give users presence capabilities that let them see when co-workers or other colleagues are online and ready to accept messages. From there, workers will have the capability to initiate an instant messaging session directly from their inboxes, from e-mail fields, or from an integrated contact list.
That’s a strategy similar to what Microsoft is pursuing with its Office System suite of applications. In connection with Office Live Communications Server, the enterprise IM server formerly known as “Greenwich,” Office applications like Word, SharePoint Team Services and Outlook can be enhanced with built-in presence-detection, contact lists, and instant messaging. Microsoft finally unleashed Live Communications Server in the year’s biggest product launch, in late October.
Both Microsoft and IBM see IM and presence being syndicated to third-party applications. As part of the new Notes and Domino release, Lotus Domino Designer will allow developers to add presence capabilities to applications that run inside of a Notes or Domino application layer. Microsoft is providing tools to allow for similar connectivity directly to customers, and through a bevy of partners.
Other giants are following suit. Like IBM and Microsoft, Sun’s recently revamped IM offering increases integration with other components of its business productivity suite, the Sun ONE Collaborative Business Platform.
Yahoo! also is getting into the action, with efforts to make it easier for third-party application developers to integrate Yahoo! Business Messenger into their systems. For instance, the company struck a deal with BEA Systems and PeopleSoft.
Pleasanton, Calif.-based PeopleSoft’s Enterprise Portal 8.8 also links to IBM Lotus Sametime, as well as Microsoft’s Live Communications Server. Later in the year, America Online’s AIM also signed on with PeopleSoft.
Efforts in unified messaging — integrating IM into telephony and voicemail systems — got a boost from such stalwarts in the field as Cisco and Siemens. With the launch of OpenScape in spring, Siemens took the wraps off its play in IM and UM, using an offering based on Microsoft’s Live Communications Server. Meanwhile, Cisco discussed plans to more closely embed IM in its unified messaging products.
Lights, Camera, IM: Multimedia Rising
In addition to tying IM into business applications, the major players in instant messaging are making efforts to build-in additional, richer forms of communications — making it easier for two parties to escalate their conversation from text chat to audio, video, and Web-sharing, as needed.
Much of the progress in linking IM and multimedia this year has come from the players in public IM.Following its having successfully petitioned the Federal Communications Commission to relax earlier restrictions, and after introducing some earlier, stopgap features, AOL is planning to unveil streaming video messaging.
Microsoft, too, has been busily enhancing multimedia features in MSN Messenger. This year, it allied itself with Webcam giant Logitech to incorporate the firm’s technology into its IM client.
In the realm of purely enterprise applications, IM management gateway provider FaceTime hooked up with online conferencing player Latitude (now being acquired by Cisco). The integration agreement between the firms enables IM users to easily migrate from chat sessions into Web conferencing.
Meanwhile, Yahoo! — which has long offered video through its public IM network — inked its deal with WebEx to provide for on-demand, ad hoc Web conferencing sessions launched via the Yahoo! Business Messenger IM client.
In more recent weeks, WiredRed added Web conferencing to its enterprise IM offering, while Userplane released its secure, Flash-based video instant messaging system for businesses and Web sites. eDial also entered the fray with its solution, which merges instant messaging, Web conferencing, and telephony.
Microsoft also has announced plans to more closely integrate Live Meeting and Live Communications Server — making it easier for business IM users to segue into Web conferences.
Inching Toward Interoperability
Like public security woes, interoperability among the major public IM networks is a perennial hot topic. But the possibility of authorized, cross-network communications saw its first real breakthrough only during the past several months, thanks to pioneering efforts by Reuters Group. In September, the financial information titan partnered with America Online in an agreement that would enable its users to exchange IMs with AIM users.
As the Reuters Messaging network is based on Microsoft’s Live Communications Server technology, it was no surprise that MSN soon after signed on as well for similar functionality. With additional agreements with major players in the enterprise market, IBM Lotus and Parlano, Reuters reshaped itself as a hub for cross-network communications. Those capabilities, it should be noted, will be available only to users of Reuters’ soon-to-be-released fee-based version of Reuters Messaging.
Additionally, as part of its settlement with Microsoft, AOL said the two companies would explore the possibility of interoperability. Little has yet to be seen from this announcement, however.
Meanwhile, while Reuters, AOL, IBM and Microsoft were expanding their relationships, a number of important strides were being made in other areas of IM. The supporters behind the open-source Extensible Messaging and Presence Protocol (XMPP) endorsed gateways linking XMPP to the Open Mobile Alliance’s Wireless Village specification — a major force in mobile IM — and Session Initiation Protocol (SIP) / SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE), which is widely supported by players in VoIP. The group also began discussions with major players behind SIP/SIMPLE for true protocol-level interoperability.
Mobile Messaging Maturing
Meanwhile, the infrastructure enabling mobile messaging in North America has been improving steadily and adding new features, paving the way for increased use of the medium as a marketing tool.
Mobile carriers also are striving to leverage IM as a means to generate additional revenue. Verizon Wireless recently introduced a multi-network IM client, while Cingular struck deals with AOL and Yahoo!. Other carriers have pursued similar integration strategies.
For the moment, the convergence of wired and wireless IM has been predominantly driven by carriers’ efforts to glean additional income from subscribers. But increasing evidence shows that businesses also want wireless IM and presence-enabled technologies — making the ascendancy of mobile IM one of the probable big stories of 2004.
What else is on the horizon for 2004? What will come of 2003’s developments in security, network interoperability, and application, multimedia and wireless integration? Be sure to e-mail us with your thoughts.
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