<prompt> Etsa algorithm e ka sebelisoang ho bona li-imeile tsa spam.nan  <response>: Instruction in English: Design an algorithm that can be used to detect spam emails. . Response in English: Here is a basic outline of an algorithm that could be used to detect spam emails:1. Create a list of spam indicators: Compile a list of common characteristics that are often found in spam emails, such as certain keywords or phrases, suspicious links or attachments, multiple exclamation marks or capital letters, etc.2. Analyze email content: When an incoming email arrives, analyze its content by looking for the presence of spam indicators. For example, check the subject line and body of the email for keywords and phrases that are commonly associated with spam messages. If links or attachments are present, inspect their URLs or file names for any red flags.3. Check sender information: Verify the senders name and email address, checking against a list of known spammers, or for inconsistencies such as domain misspellings.4. Apply machine learning: Utilize machine learning algorithms to analyze email content and learn from previously identified spam emails to improve future detection.5. Score the email: Assign a numerical score to the email based on the total number of spam indicators that were found. The higher the score, the more likely it is that the email is spam. 6. Take action: If the emails score exceeds a predetermined threshold, flag it as spam and move it to the spam folder. If the score is below the threshold but still relatively high, consider marking the email as potentially spam and allow the recipient to review it before taking any further action.7. Update the algorithm: Regularly update the list of spam indicators and the machine learning algorithms to ensure that the spam detection algorithm remains accurate and effective over time. . Response in Sesotho: Mona ke moralo oa motheo o ka sebelisoang ho lemoha li-imeile tsa spam: 1. Etsa lethathamo la matšoao a spam: Etsa lethathamo la litšobotsi tse tloaelehileng tseo hangata li fumanoang ho li-imeile tsa spam, joalo ka mantsoe a itseng kapa lipoleloana. , lihokelo kapa lihokelo tse belaetsang, litlatsetso tse ngata kapa litlhaku tse kholo, joalo-joalo 2. Hlahloba litaba tsa lengolo-tsoibila: Ha lengolo-tsoibila le kenang le fihla, sekaseka litaba tsa eona ka ho batla boteng ba matšoao a spam. Ka mohlala, hlahloba sehlooho le mele oa lengolo-tsoibila bakeng sa mantsoe a bohlokoa le lipoleloana tse atisang ho amahanngoa le melaetsa ea spam. Haeba lihokelo kapa lihokelo li le teng, hlahloba li-URL tsa tsona kapa mabitso a lifaele bakeng sa lifolakha tse khubelu. 3. Sheba lintlha tsa moromelli: Netefatsa lebitso la motho ea e rometseng le aterese ea hae ea lengolo-tsoibila, u sheba u ipapisitse le lethathamo la batho ba tsebahalang ba romelang melaetsa ea lekunutu, kapa bakeng sa ho se lumellane ho kang ho fosahetse ho domain. 4. Sebelisa ho ithuta ka mochini: Sebelisa mekhoa ea ho ithuta ka mochini ho sekaseka litaba tsa lengolo-tsoibila le ho ithuta ho tsoa ho li-imeile tsa spam tse neng li tsejoa nakong e fetileng ho ntlafatsa phumano ea nako e tlang. 5. Fana ka lengolo-tsoibila: Fana ka lintlha tsa linomoro ho lengolo-tsoibila ho latela kakaretso ea matšoao a spam a fumanoeng. Ha lintlha li phahame, ho na le monyetla o moholo oa hore lengolo-tsoibila ke spam. 6. Nka khato: Haeba lintlha tsa lengolo-tsoibila li feta tekanyo e reriloeng esale pele, e tšoaee e le spam me u e ise foldareng ea spam. Haeba lintlha li le ka tlase ho moeli empa li ntse li le holimo, nahana ka ho tšoaea lengolo-tsoibila e le mohlomong spam me u lumelle moamoheli ho e hlahloba pele a nka khato e ngoe hape. 7. Nchafatsa algorithm: Kamehla ntlafatsa lenane la li-spam indicators le mekhoa ea ho ithuta mochine ho netefatsa hore algorithm ea ho lemoha spam e lula e nepahetse ebile e sebetsa ha nako e ntse e ea. 